Section_09_Giskard_4.ipynb

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        "Continuous Integration and Deployment (CI/CD) processes have been essential in traditional software development, and they are now becoming equally important in the realm of Artificial Intelligence (AI) systems. By adapting CI/CD for AI applications, automated pipelines can be created to regularly update, retrain, and deploy AI models, ensuring consistent performance and accuracy over time. This approach fosters collaboration between engineering and data science teams, streamlining workflows involving data collection, model training, evaluation, and deployment.\n",
        "\n",
        "### Understanding CI/CD for AI\n",
        "CI/CD is a DevOps strategy that automates the process of building, testing, and deploying software updates, allowing code to move quickly from development to production. In AI systems, this involves a series of steps starting with data preparation, model training, and testing, followed by continuous integration and deployment of the model to production environments.\n",
        "\n",
        "The traditional CI/CD flow in software development starts with requirements gathering and design, moves through coding, building, and testing, and finally transitions into continuous deployment where updates are rolled out, monitored, and iterated upon. Similarly, in AI, the process involves preparing data, training models, testing their performance, and deploying them in real-world scenarios. This process is further enhanced by a feedback loop that continuously monitors AI models, ensuring they are up-to-date and effective in making accurate predictions.\n",
        "\n",
        "### CI/CD for AI Systems\n",
        "CI/CD for AI, often referred to as **MLOps**, is designed to streamline the creation, deployment, and management of AI models. It automates the AI lifecycle, removing the need for constant manual intervention by data scientists and engineers. Once an AI model is integrated into a CI/CD pipeline, it can be automatically retrained and redeployed as new data becomes available, reducing human error and improving model performance over time.\n",
        "\n",
        "This continuous feedback loop allows the AI system to evolve with changing data, ensuring that the model remains relevant and accurate. Regular monitoring and retraining are key components of this process, ensuring that AI models are consistently optimized based on real-world performance metrics.\n",
        "\n",
        "### Why CI/CD is Important for AI\n",
        "The value of CI/CD in AI lies in its scalability. For small-scale AI projects, manual updates and deployments may suffice. However, for organizations building large-scale AI systems, where hundreds or even thousands of model versions may need to be tested and deployed, a more robust and automated approach is essential. CI/CD provides the infrastructure needed to support these operations, enabling teams to manage the complexity of AI model development at scale while minimizing technical debt.\n",
        "\n",
        "By integrating CI/CD into the AI workflow, the entire pipeline—from data ingestion to model deployment—becomes more streamlined and scalable. CI/CD enables AI models to be automatically trained, tested, and deployed, allowing for faster experimentation and iteration. With proper infrastructure support, including cloud-based or on-premise compute resources, AI systems can continuously evolve with minimal manual oversight.\n",
        "\n",
        "### Building a CI/CD Pipeline for AI\n",
        "An AI pipeline using CI/CD starts with data collection and validation, ensuring the quality and integrity of the data used for model training. Next, various algorithms are applied to train the model, followed by rigorous testing to evaluate the model’s performance. Once the model passes these tests, it is deployed in production environments, where it begins making predictions or performing other tasks.\n",
        "\n",
        "A key aspect of CI/CD in AI is the continuous monitoring of model performance in production. Since AI models degrade over time as new data patterns emerge (a phenomenon known as **model drift** or **model decay**), the pipeline must automatically detect when retraining is necessary. This feedback loop is critical to ensuring that the AI model stays relevant and continues delivering accurate results.\n",
        "\n",
        "### Automating the AI Pipeline with CI/CD\n",
        "In AI, CI/CD pipelines must incorporate statistical testing, anomaly detection, and continuous monitoring to ensure data integrity and model accuracy. By continuously optimizing AI pipelines, organizations gain a competitive edge, ensuring that models in production are always performing at their best.\n",
        "\n",
        "Building a fully automated CI/CD pipeline for AI also reduces the need for frequent manual intervention from data scientists and engineers. This allows for the establishment of production-ready AI systems that can automatically adapt to new data and changing conditions. By automating model retraining and deployment, AI pipelines can scale effectively without sacrificing reliability or accuracy.\n",
        "\n",
        "### Conclusion\n",
        "CI/CD for AI systems is crucial for scaling AI models and ensuring their continued effectiveness in production. By automating the entire AI lifecycle—from data collection to model deployment and retraining—CI/CD enables AI systems to evolve continuously without manual oversight. With proper infrastructure and monitoring tools, AI pipelines can operate efficiently, ensuring that models remain accurate, compliant with regulations, and ready for real-world applications. As AI systems grow in complexity, the role of CI/CD will only become more important in maintaining the performance and reliability of these models.\n",
        "\n"
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        "# Install pipenv if not already installed\n",
        "!pip install pipenv\n",
        "\n",
        "# Initialize pipenv and install dependencies\n",
        "!pipenv install pandas numpy scikit-learn giskard\n"
      ],
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        "id": "qrOcJjKg4tqR",
        "outputId": "e287ff77-690f-4d62-910b-aa5f19cb160d"
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          "text": [
            "Collecting pipenv\n",
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            "\u001b[1mCreating a virtualenv for this project\u001b[0m\n",
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            "\u001b[1mUsing default python from\u001b[0m \u001b[1;33m/usr/bin/\u001b[0m\u001b[1;33mpython3\u001b[0m\u001b[32m3.10.12\u001b[0m\u001b[32m \u001b[0m\u001b[1;32mto create virtualenv\u001b[0m\u001b[1;32m...\u001b[0m\n",
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            "\u001b[36mBashActivator,CShellActivator,FishActivator,NushellActivator,PowerShellActivator,PythonActivator\u001b[0m\n",
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            "\u001b[?25lBuilding requirements...\n",
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            "To activate this project's virtualenv, run \u001b[33mpipenv shell\u001b[0m.\n",
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            "\u001b[1mInstalling dependencies from Pipfile.lock (6bc860)...\u001b[0m\n"
          ]
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.7/149.7 kB\u001b[0m \u001b[31m10.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for docopt (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "pip install \"giskard>=2.0.0b\" -U -q\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from giskard import Dataset\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Load the dataset\n",
        "DATASET_URL = 'https://raw.githubusercontent.com/Giskard-AI/examples/main/datasets/WA_Fn-UseC_-Telco-Customer-Churn.csv'\n",
        "churn_df = pd.read_csv(DATASET_URL)\n",
        "\n",
        "# Pre-process the dataset\n",
        "CATEGORICAL_COLUMNS = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']\n",
        "NUMERIC_COLUMNS = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
        "\n",
        "def preprocess(df: pd.DataFrame) -> pd.DataFrame:\n",
        "    df['TotalCharges'] = pd.to_numeric(df['TotalCharges'], errors='coerce')\n",
        "    df = df.dropna()\n",
        "    df = df.drop('customerID', axis=1)\n",
        "    df['PaymentMethod'] = df['PaymentMethod'].str.replace(' (automatic)', '', regex=False)\n",
        "    df[CATEGORICAL_COLUMNS + ['Churn']] = df[CATEGORICAL_COLUMNS + ['Churn']].astype('object')\n",
        "    return df\n",
        "\n",
        "churn_df = preprocess(churn_df)\n",
        "\n",
        "# Train-validation-test split\n",
        "X_train, X_valid, y_train, y_valid = train_test_split(churn_df.drop('Churn', axis=1), churn_df.Churn, test_size=0.3, random_state=42)\n",
        "X_valid, X_test, y_valid, y_test = train_test_split(X_valid, y_valid, test_size=0.5, random_state=42)\n",
        "\n",
        "# Wrap the dataset with Giskard\n",
        "raw_data = pd.concat([X_valid, y_valid], axis=1)\n",
        "wrapped_data = Dataset(\n",
        "    df = raw_data,  # A pandas.DataFrame that contains the raw data (before all the pre-processing steps) and the actual ground truth variable\n",
        "    target = 'Churn',  # Ground truth variable\n",
        "    name = \"Churn classification dataset\",  # Optional\n",
        "    cat_columns = CATEGORICAL_COLUMNS  # List of categorical columns. Optional, but is a MUST if available. Inferred automatically if not.\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCBvRqoyNwAa",
        "outputId": "8271e6dc-f6fd-4dec-ed7e-784652833827"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Your 'pandas.DataFrame' is successfully wrapped by Giskard's 'Dataset' wrapper class.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "\n",
        "# Pre-process steps for the pipeline\n",
        "preprocessor = ColumnTransformer(transformers=[\n",
        "    ('num', StandardScaler(), NUMERIC_COLUMNS),\n",
        "    ('cat', OneHotEncoder(handle_unknown='ignore',drop='first'), CATEGORICAL_COLUMNS)\n",
        "])\n",
        "\n",
        "# Define the pipeline\n",
        "pipeline = Pipeline(steps=[\n",
        "    ('preprocessor', preprocessor),\n",
        "    ('classifier', LogisticRegression())\n",
        "])\n",
        "\n",
        "# Train the model\n",
        "pipeline.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 189
        },
        "id": "JDlzYY-OOC1g",
        "outputId": "fc38619e-6027-4649-82ab-025c3710ac55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Pipeline(steps=[('preprocessor',\n",
              "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
              "                                                  ['tenure', 'MonthlyCharges',\n",
              "                                                   'TotalCharges']),\n",
              "                                                 ('cat',\n",
              "                                                  OneHotEncoder(drop='first',\n",
              "                                                                handle_unknown='ignore'),\n",
              "                                                  ['gender', 'SeniorCitizen',\n",
              "                                                   'Partner', 'Dependents',\n",
              "                                                   'PhoneService',\n",
              "                                                   'MultipleLines',\n",
              "                                                   'InternetService',\n",
              "                                                   'OnlineSecurity',\n",
              "                                                   'OnlineBackup',\n",
              "                                                   'DeviceProtection',\n",
              "                                                   'TechSupport', 'StreamingTV',\n",
              "                                                   'StreamingMovies',\n",
              "                                                   'Contract',\n",
              "                                                   'PaperlessBilling',\n",
              "                                                   'PaymentMethod'])])),\n",
              "                ('classifier', LogisticRegression())])"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;tenure&#x27;, &#x27;MonthlyCharges&#x27;,\n",
              "                                                   &#x27;TotalCharges&#x27;]),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
              "                                                                handle_unknown=&#x27;ignore&#x27;),\n",
              "                                                  [&#x27;gender&#x27;, &#x27;SeniorCitizen&#x27;,\n",
              "                                                   &#x27;Partner&#x27;, &#x27;Dependents&#x27;,\n",
              "                                                   &#x27;PhoneService&#x27;,\n",
              "                                                   &#x27;MultipleLines&#x27;,\n",
              "                                                   &#x27;InternetService&#x27;,\n",
              "                                                   &#x27;OnlineSecurity&#x27;,\n",
              "                                                   &#x27;OnlineBackup&#x27;,\n",
              "                                                   &#x27;DeviceProtection&#x27;,\n",
              "                                                   &#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;,\n",
              "                                                   &#x27;StreamingMovies&#x27;,\n",
              "                                                   &#x27;Contract&#x27;,\n",
              "                                                   &#x27;PaperlessBilling&#x27;,\n",
              "                                                   &#x27;PaymentMethod&#x27;])])),\n",
              "                (&#x27;classifier&#x27;, LogisticRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
              "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                                  [&#x27;tenure&#x27;, &#x27;MonthlyCharges&#x27;,\n",
              "                                                   &#x27;TotalCharges&#x27;]),\n",
              "                                                 (&#x27;cat&#x27;,\n",
              "                                                  OneHotEncoder(drop=&#x27;first&#x27;,\n",
              "                                                                handle_unknown=&#x27;ignore&#x27;),\n",
              "                                                  [&#x27;gender&#x27;, &#x27;SeniorCitizen&#x27;,\n",
              "                                                   &#x27;Partner&#x27;, &#x27;Dependents&#x27;,\n",
              "                                                   &#x27;PhoneService&#x27;,\n",
              "                                                   &#x27;MultipleLines&#x27;,\n",
              "                                                   &#x27;InternetService&#x27;,\n",
              "                                                   &#x27;OnlineSecurity&#x27;,\n",
              "                                                   &#x27;OnlineBackup&#x27;,\n",
              "                                                   &#x27;DeviceProtection&#x27;,\n",
              "                                                   &#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;,\n",
              "                                                   &#x27;StreamingMovies&#x27;,\n",
              "                                                   &#x27;Contract&#x27;,\n",
              "                                                   &#x27;PaperlessBilling&#x27;,\n",
              "                                                   &#x27;PaymentMethod&#x27;])])),\n",
              "                (&#x27;classifier&#x27;, LogisticRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
              "                                 [&#x27;tenure&#x27;, &#x27;MonthlyCharges&#x27;, &#x27;TotalCharges&#x27;]),\n",
              "                                (&#x27;cat&#x27;,\n",
              "                                 OneHotEncoder(drop=&#x27;first&#x27;,\n",
              "                                               handle_unknown=&#x27;ignore&#x27;),\n",
              "                                 [&#x27;gender&#x27;, &#x27;SeniorCitizen&#x27;, &#x27;Partner&#x27;,\n",
              "                                  &#x27;Dependents&#x27;, &#x27;PhoneService&#x27;, &#x27;MultipleLines&#x27;,\n",
              "                                  &#x27;InternetService&#x27;, &#x27;OnlineSecurity&#x27;,\n",
              "                                  &#x27;OnlineBackup&#x27;, &#x27;DeviceProtection&#x27;,\n",
              "                                  &#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;,\n",
              "                                  &#x27;StreamingMovies&#x27;, &#x27;Contract&#x27;,\n",
              "                                  &#x27;PaperlessBilling&#x27;, &#x27;PaymentMethod&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;tenure&#x27;, &#x27;MonthlyCharges&#x27;, &#x27;TotalCharges&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;gender&#x27;, &#x27;SeniorCitizen&#x27;, &#x27;Partner&#x27;, &#x27;Dependents&#x27;, &#x27;PhoneService&#x27;, &#x27;MultipleLines&#x27;, &#x27;InternetService&#x27;, &#x27;OnlineSecurity&#x27;, &#x27;OnlineBackup&#x27;, &#x27;DeviceProtection&#x27;, &#x27;TechSupport&#x27;, &#x27;StreamingTV&#x27;, &#x27;StreamingMovies&#x27;, &#x27;Contract&#x27;, &#x27;PaperlessBilling&#x27;, &#x27;PaymentMethod&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder(drop=&#x27;first&#x27;, handle_unknown=&#x27;ignore&#x27;)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from giskard import Model, scan\n",
        "\n",
        "# Define the prediction function\n",
        "def prediction_function(df: pd.DataFrame) -> np.ndarray:\n",
        "    # The pre-processor can be a pipeline of one-hot encoding, imputer, scaler, etc. OR\n",
        "    # Perform the pre-processing steps manually here\n",
        "    return pipeline.predict_proba(df)\n",
        "\n",
        "# Wrap the model with Giskard\n",
        "wrapped_model = Model(\n",
        "    model = prediction_function,                # A prediction function that encapsulates all the data pre-processing steps and that could be executed with the dataset used by the scan.\n",
        "    model_type = \"classification\",              # Either regression, classification or text_generation.\n",
        "    classification_labels = pipeline.classes_,  # Their order MUST be identical to the prediction_function's output order\n",
        "    name = \"Churn classification\",              # Name of the wrapped model [Optional]\n",
        "    feature_names = X_valid.columns.to_list(),  # Default: all columns of your dataset [Optional]\n",
        "    classification_threshold = 0.5,             # Default: 0.5 [Optional]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdP-Fk_AOH1A",
        "outputId": "51e3e6a2-bc4c-4e1d-b16e-d4c9d497949a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.models.automodel:Your 'prediction_function' is successfully wrapped by Giskard's 'PredictionFunctionModel' wrapper class.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from giskard import scan\n",
        "\n",
        "# Scan the model\n",
        "scan_results = scan(wrapped_model, wrapped_data)\n",
        "\n",
        "# Display the results in the notebook\n",
        "display(scan_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WMmeaFobOCSG",
        "outputId": "2de60e6c-503c-4f55-edc1-f19f70726f09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (10, 20) executed in 0:00:00.048850\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.070089\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (10, 20) executed in 0:00:00.057792\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1055, 20) executed in 0:00:00.143644\n",
            "INFO:giskard.scanner.logger:Running detectors: ['SpuriousCorrelationDetector', 'PerformanceBiasDetector', 'DataLeakageDetector', 'OverconfidenceDetector', 'UnderconfidenceDetector', 'StochasticityDetector', 'EthicalBiasDetector', 'TextPerturbationDetector']\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: Running\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1055, 20) executed in 0:00:00.045938\n",
            "INFO:giskard.datasets.base:Your 'pandas.DataFrame' is successfully wrapped by Giskard's 'Dataset' wrapper class.\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `tenure` < 19.500 AND `tenure` >= 15.500\tAssociation = 0.012\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Your model is successfully validated.\n",
            "🔎 Running scan…\n",
            "Running detector SpuriousCorrelationDetector…\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `tenure` < 5.500 AND `tenure` >= 2.500\tAssociation = 0.030\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `tenure` < 1.500\tAssociation = 0.040\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `tenure` >= 53.500\tAssociation = 0.159\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `TotalCharges` < 49.975\tAssociation = 0.002\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `TotalCharges` >= 4883.700\tAssociation = 0.085\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `TotalCharges` < 180.475 AND `TotalCharges` >= 80.900\tAssociation = 0.005\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `TotalCharges` >= 3281.275 AND `TotalCharges` < 4883.700\tAssociation = 0.015\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `TotalCharges` < 825.750 AND `TotalCharges` >= 603.800\tAssociation = 0.003\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MonthlyCharges` < 24.325\tAssociation = 0.085\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MonthlyCharges` >= 74.175 AND `MonthlyCharges` < 77.175\tAssociation = 0.034\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MonthlyCharges` >= 77.175 AND `MonthlyCharges` < 80.675\tAssociation = 0.000\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MonthlyCharges` >= 80.675 AND `MonthlyCharges` < 84.925\tAssociation = 0.006\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MonthlyCharges` >= 107.975\tAssociation = 0.015\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MonthlyCharges` < 68.375 AND `MonthlyCharges` >= 55.275\tAssociation = 0.026\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MonthlyCharges` < 50.075 AND `MonthlyCharges` >= 43.725\tAssociation = 0.018\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `gender` == \"Male\"\tAssociation = 0.000\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `gender` == \"Female\"\tAssociation = 0.000\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `SeniorCitizen` == 0\tAssociation = 0.048\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `SeniorCitizen` == 1\tAssociation = 0.048\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `Partner` == \"Yes\"\tAssociation = 0.041\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `Partner` == \"No\"\tAssociation = 0.041\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `Dependents` == \"Yes\"\tAssociation = 0.049\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `Dependents` == \"No\"\tAssociation = 0.049\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `PhoneService` == \"Yes\"\tAssociation = 0.002\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `PhoneService` == \"No\"\tAssociation = 0.002\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MultipleLines` == \"No\"\tAssociation = 0.001\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MultipleLines` == \"Yes\"\tAssociation = 0.004\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `MultipleLines` == \"No phone service\"\tAssociation = 0.002\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `InternetService` == \"No\"\tAssociation = 0.107\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `InternetService` == \"DSL\"\tAssociation = 0.076\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `InternetService` == \"Fiber optic\"\tAssociation = 0.223\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `OnlineSecurity` == \"No internet service\"\tAssociation = 0.107\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `OnlineSecurity` == \"No\"\tAssociation = 0.259\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `OnlineSecurity` == \"Yes\"\tAssociation = 0.104\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `OnlineBackup` == \"No internet service\"\tAssociation = 0.107\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `OnlineBackup` == \"Yes\"\tAssociation = 0.024\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `OnlineBackup` == \"No\"\tAssociation = 0.129\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `DeviceProtection` == \"No internet service\"\tAssociation = 0.107\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `DeviceProtection` == \"No\"\tAssociation = 0.108\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `DeviceProtection` == \"Yes\"\tAssociation = 0.016\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `TechSupport` == \"No internet service\"\tAssociation = 0.107\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `TechSupport` == \"Yes\"\tAssociation = 0.057\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `TechSupport` == \"No\"\tAssociation = 0.188\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `StreamingTV` == \"No internet service\"\tAssociation = 0.107\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `StreamingTV` == \"No\"\tAssociation = 0.034\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `StreamingTV` == \"Yes\"\tAssociation = 0.001\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `StreamingMovies` == \"No internet service\"\tAssociation = 0.107\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `StreamingMovies` == \"No\"\tAssociation = 0.019\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `StreamingMovies` == \"Yes\"\tAssociation = 0.005\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `Contract` == \"One year\"\tAssociation = 0.110\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `Contract` == \"Two year\"\tAssociation = 0.129\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `Contract` == \"Month-to-month\"\tAssociation = 0.287\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `PaperlessBilling` == \"No\"\tAssociation = 0.056\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `PaperlessBilling` == \"Yes\"\tAssociation = 0.056\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `PaymentMethod` == \"Mailed check\"\tAssociation = 0.022\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `PaymentMethod` == \"Credit card\"\tAssociation = 0.033\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `PaymentMethod` == \"Electronic check\"\tAssociation = 0.196\n",
            "INFO:giskard.scanner.logger:SpuriousCorrelationDetector: `PaymentMethod` == \"Bank transfer\"\tAssociation = 0.050\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Running\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Calculating loss\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1055, 20) executed in 0:00:00.028309\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SpuriousCorrelationDetector: 0 issue detected. (Took 0:00:01.525765)\n",
            "Running detector PerformanceBiasDetector…\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Loss calculated (took 0:00:02.026287)\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Finding data slices\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: 51 slices found (took 0:00:01.937303)\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Analyzing issues\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1055, 20) executed in 0:00:00.028412\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing 51 slices for performance issues.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `tenure` < 44.500\tPrecision = 0.664 (global 0.664) Δm = -0.000\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `tenure` >= 44.500 AND `tenure` < 70.500 because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `tenure` >= 70.500 because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `TotalCharges` >= 3485.375 because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TotalCharges` < 303.000\tPrecision = 0.756 (global 0.664) Δm = 0.139\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TotalCharges` < 3485.375 AND `TotalCharges` >= 303.000\tPrecision = 0.613 (global 0.664) Δm = -0.076\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `MonthlyCharges` < 20.775 because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MonthlyCharges` >= 20.775\tPrecision = 0.664 (global 0.664) Δm = 0.000\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `gender` == \"Male\"\tPrecision = 0.658 (global 0.664) Δm = -0.009\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `gender` == \"Female\"\tPrecision = 0.669 (global 0.664) Δm = 0.008\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `SeniorCitizen` == 0\tPrecision = 0.603 (global 0.664) Δm = -0.092\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `SeniorCitizen` == 1\tPrecision = 0.785 (global 0.664) Δm = 0.182\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Partner` == \"Yes\"\tPrecision = 0.652 (global 0.664) Δm = -0.018\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Partner` == \"No\"\tPrecision = 0.669 (global 0.664) Δm = 0.007\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Dependents` == \"Yes\"\tPrecision = 0.680 (global 0.664) Δm = 0.024\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Dependents` == \"No\"\tPrecision = 0.662 (global 0.664) Δm = -0.003\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PhoneService` == \"Yes\"\tPrecision = 0.653 (global 0.664) Δm = -0.017\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `PhoneService` == \"No\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MultipleLines` == \"No\"\tPrecision = 0.592 (global 0.664) Δm = -0.108\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MultipleLines` == \"Yes\"\tPrecision = 0.703 (global 0.664) Δm = 0.060\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `MultipleLines` == \"No phone service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `InternetService` == \"No\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `InternetService` == \"DSL\"\tPrecision = 0.667 (global 0.664) Δm = 0.004\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `InternetService` == \"Fiber optic\"\tPrecision = 0.663 (global 0.664) Δm = -0.001\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `OnlineSecurity` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineSecurity` == \"No\"\tPrecision = 0.649 (global 0.664) Δm = -0.023\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `OnlineSecurity` == \"Yes\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `OnlineBackup` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineBackup` == \"Yes\"\tPrecision = 0.620 (global 0.664) Δm = -0.066\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineBackup` == \"No\"\tPrecision = 0.676 (global 0.664) Δm = 0.018\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `DeviceProtection` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `DeviceProtection` == \"No\"\tPrecision = 0.687 (global 0.664) Δm = 0.035\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `DeviceProtection` == \"Yes\"\tPrecision = 0.585 (global 0.664) Δm = -0.119\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `TechSupport` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TechSupport` == \"Yes\"\tPrecision = 0.522 (global 0.664) Δm = -0.214\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TechSupport` == \"No\"\tPrecision = 0.679 (global 0.664) Δm = 0.023\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `StreamingTV` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingTV` == \"No\"\tPrecision = 0.688 (global 0.664) Δm = 0.037\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingTV` == \"Yes\"\tPrecision = 0.629 (global 0.664) Δm = -0.053\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `StreamingMovies` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingMovies` == \"No\"\tPrecision = 0.695 (global 0.664) Δm = 0.046\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingMovies` == \"Yes\"\tPrecision = 0.625 (global 0.664) Δm = -0.058\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `Contract` == \"One year\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `Contract` == \"Two year\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Contract` == \"Month-to-month\"\tPrecision = 0.664 (global 0.664) Δm = 0.000\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaperlessBilling` == \"No\"\tPrecision = 0.545 (global 0.664) Δm = -0.178\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaperlessBilling` == \"Yes\"\tPrecision = 0.691 (global 0.664) Δm = 0.041\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Mailed check\"\tPrecision = 0.593 (global 0.664) Δm = -0.107\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `PaymentMethod` == \"Credit card\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Electronic check\"\tPrecision = 0.705 (global 0.664) Δm = 0.062\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `PaymentMethod` == \"Bank transfer\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `tenure` < 44.500\tRecall = 0.604 (global 0.544) Δm = 0.111\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `tenure` >= 44.500 AND `tenure` < 70.500\tRecall = 0.062 (global 0.544) Δm = -0.885\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `tenure` >= 70.500 because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TotalCharges` >= 3485.375\tRecall = 0.171 (global 0.544) Δm = -0.686\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TotalCharges` < 303.000\tRecall = 0.619 (global 0.544) Δm = 0.139\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TotalCharges` < 3485.375 AND `TotalCharges` >= 303.000\tRecall = 0.596 (global 0.544) Δm = 0.096\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `MonthlyCharges` < 20.775 because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MonthlyCharges` >= 20.775\tRecall = 0.569 (global 0.544) Δm = 0.047\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `gender` == \"Male\"\tRecall = 0.528 (global 0.544) Δm = -0.028\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `gender` == \"Female\"\tRecall = 0.559 (global 0.544) Δm = 0.028\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `SeniorCitizen` == 0\tRecall = 0.456 (global 0.544) Δm = -0.161\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `SeniorCitizen` == 1\tRecall = 0.765 (global 0.544) Δm = 0.408\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Partner` == \"Yes\"\tRecall = 0.459 (global 0.544) Δm = -0.155\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Partner` == \"No\"\tRecall = 0.587 (global 0.544) Δm = 0.080\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Dependents` == \"Yes\"\tRecall = 0.327 (global 0.544) Δm = -0.399\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Dependents` == \"No\"\tRecall = 0.591 (global 0.544) Δm = 0.088\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PhoneService` == \"Yes\"\tRecall = 0.555 (global 0.544) Δm = 0.021\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PhoneService` == \"No\"\tRecall = 0.455 (global 0.544) Δm = -0.164\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MultipleLines` == \"No\"\tRecall = 0.479 (global 0.544) Δm = -0.118\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MultipleLines` == \"Yes\"\tRecall = 0.624 (global 0.544) Δm = 0.148\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MultipleLines` == \"No phone service\"\tRecall = 0.455 (global 0.544) Δm = -0.164\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `InternetService` == \"No\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `InternetService` == \"DSL\"\tRecall = 0.237 (global 0.544) Δm = -0.564\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `InternetService` == \"Fiber optic\"\tRecall = 0.711 (global 0.544) Δm = 0.309\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `OnlineSecurity` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineSecurity` == \"No\"\tRecall = 0.667 (global 0.544) Δm = 0.226\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineSecurity` == \"Yes\"\tRecall = 0.196 (global 0.544) Δm = -0.639\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `OnlineBackup` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineBackup` == \"Yes\"\tRecall = 0.443 (global 0.544) Δm = -0.185\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineBackup` == \"No\"\tRecall = 0.625 (global 0.544) Δm = 0.150\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `DeviceProtection` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `DeviceProtection` == \"No\"\tRecall = 0.628 (global 0.544) Δm = 0.156\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `DeviceProtection` == \"Yes\"\tRecall = 0.437 (global 0.544) Δm = -0.197\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `TechSupport` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TechSupport` == \"Yes\"\tRecall = 0.255 (global 0.544) Δm = -0.530\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TechSupport` == \"No\"\tRecall = 0.646 (global 0.544) Δm = 0.188\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `StreamingTV` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingTV` == \"No\"\tRecall = 0.617 (global 0.544) Δm = 0.135\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingTV` == \"Yes\"\tRecall = 0.526 (global 0.544) Δm = -0.033\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `StreamingMovies` == \"No internet service\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingMovies` == \"No\"\tRecall = 0.572 (global 0.544) Δm = 0.053\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingMovies` == \"Yes\"\tRecall = 0.586 (global 0.544) Δm = 0.077\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Contract` == \"One year\"\tRecall = 0.000 (global 0.544) Δm = -1.000\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Skipping slice `Contract` == \"Two year\" because metric was estimated on < 20 samples.\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Contract` == \"Month-to-month\"\tRecall = 0.607 (global 0.544) Δm = 0.117\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaperlessBilling` == \"No\"\tRecall = 0.348 (global 0.544) Δm = -0.360\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaperlessBilling` == \"Yes\"\tRecall = 0.606 (global 0.544) Δm = 0.114\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Mailed check\"\tRecall = 0.348 (global 0.544) Δm = -0.360\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Credit card\"\tRecall = 0.316 (global 0.544) Δm = -0.419\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Electronic check\"\tRecall = 0.731 (global 0.544) Δm = 0.344\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Bank transfer\"\tRecall = 0.167 (global 0.544) Δm = -0.693\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `tenure` < 44.500\tBalanced Accuracy = 0.709 (global 0.720) Δm = -0.016\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `tenure` >= 44.500 AND `tenure` < 70.500\tBalanced Accuracy = 0.529 (global 0.720) Δm = -0.265\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `tenure` >= 70.500\tBalanced Accuracy = 1.000 (global 0.720) Δm = 0.388\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TotalCharges` >= 3485.375\tBalanced Accuracy = 0.576 (global 0.720) Δm = -0.201\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TotalCharges` < 303.000\tBalanced Accuracy = 0.715 (global 0.720) Δm = -0.008\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TotalCharges` < 3485.375 AND `TotalCharges` >= 303.000\tBalanced Accuracy = 0.731 (global 0.720) Δm = 0.014\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MonthlyCharges` < 20.775\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MonthlyCharges` >= 20.775\tBalanced Accuracy = 0.722 (global 0.720) Δm = 0.002\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `gender` == \"Male\"\tBalanced Accuracy = 0.713 (global 0.720) Δm = -0.010\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `gender` == \"Female\"\tBalanced Accuracy = 0.727 (global 0.720) Δm = 0.009\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `SeniorCitizen` == 0\tBalanced Accuracy = 0.682 (global 0.720) Δm = -0.053\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `SeniorCitizen` == 1\tBalanced Accuracy = 0.795 (global 0.720) Δm = 0.104\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Partner` == \"Yes\"\tBalanced Accuracy = 0.701 (global 0.720) Δm = -0.027\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Partner` == \"No\"\tBalanced Accuracy = 0.716 (global 0.720) Δm = -0.006\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Dependents` == \"Yes\"\tBalanced Accuracy = 0.647 (global 0.720) Δm = -0.101\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Dependents` == \"No\"\tBalanced Accuracy = 0.727 (global 0.720) Δm = 0.010\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PhoneService` == \"Yes\"\tBalanced Accuracy = 0.723 (global 0.720) Δm = 0.004\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PhoneService` == \"No\"\tBalanced Accuracy = 0.702 (global 0.720) Δm = -0.026\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MultipleLines` == \"No\"\tBalanced Accuracy = 0.684 (global 0.720) Δm = -0.051\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MultipleLines` == \"Yes\"\tBalanced Accuracy = 0.759 (global 0.720) Δm = 0.054\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `MultipleLines` == \"No phone service\"\tBalanced Accuracy = 0.702 (global 0.720) Δm = -0.026\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `InternetService` == \"No\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `InternetService` == \"DSL\"\tBalanced Accuracy = 0.603 (global 0.720) Δm = -0.163\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `InternetService` == \"Fiber optic\"\tBalanced Accuracy = 0.731 (global 0.720) Δm = 0.015\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineSecurity` == \"No internet service\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineSecurity` == \"No\"\tBalanced Accuracy = 0.709 (global 0.720) Δm = -0.016\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineSecurity` == \"Yes\"\tBalanced Accuracy = 0.598 (global 0.720) Δm = -0.170\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineBackup` == \"No internet service\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineBackup` == \"Yes\"\tBalanced Accuracy = 0.690 (global 0.720) Δm = -0.042\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `OnlineBackup` == \"No\"\tBalanced Accuracy = 0.703 (global 0.720) Δm = -0.024\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `DeviceProtection` == \"No internet service\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `DeviceProtection` == \"No\"\tBalanced Accuracy = 0.716 (global 0.720) Δm = -0.006\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `DeviceProtection` == \"Yes\"\tBalanced Accuracy = 0.680 (global 0.720) Δm = -0.057\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TechSupport` == \"No internet service\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TechSupport` == \"Yes\"\tBalanced Accuracy = 0.607 (global 0.720) Δm = -0.158\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `TechSupport` == \"No\"\tBalanced Accuracy = 0.715 (global 0.720) Δm = -0.008\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingTV` == \"No internet service\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingTV` == \"No\"\tBalanced Accuracy = 0.732 (global 0.720) Δm = 0.016\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingTV` == \"Yes\"\tBalanced Accuracy = 0.702 (global 0.720) Δm = -0.026\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingMovies` == \"No internet service\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingMovies` == \"No\"\tBalanced Accuracy = 0.717 (global 0.720) Δm = -0.004\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `StreamingMovies` == \"Yes\"\tBalanced Accuracy = 0.724 (global 0.720) Δm = 0.006\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Contract` == \"One year\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Contract` == \"Two year\"\tBalanced Accuracy = 0.500 (global 0.720) Δm = -0.306\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `Contract` == \"Month-to-month\"\tBalanced Accuracy = 0.686 (global 0.720) Δm = -0.047\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaperlessBilling` == \"No\"\tBalanced Accuracy = 0.646 (global 0.720) Δm = -0.104\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaperlessBilling` == \"Yes\"\tBalanced Accuracy = 0.731 (global 0.720) Δm = 0.015\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Mailed check\"\tBalanced Accuracy = 0.646 (global 0.720) Δm = -0.104\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Credit card\"\tBalanced Accuracy = 0.641 (global 0.720) Δm = -0.110\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Electronic check\"\tBalanced Accuracy = 0.723 (global 0.720) Δm = 0.003\tis_issue = False\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: Testing slice `PaymentMethod` == \"Bank transfer\"\tBalanced Accuracy = 0.558 (global 0.720) Δm = -0.226\tis_issue = True\n",
            "INFO:giskard.scanner.logger:PerformanceBiasDetector: 30 issues found (took 0:00:07.688982)\n",
            "INFO:giskard.scanner.logger:DataLeakageDetector: Running\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1055, 20) executed in 0:00:00.037858\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.027467\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.030741\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.029676\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "PerformanceBiasDetector: 15 issues detected. (Took 0:00:11.694657)\n",
            "Running detector DataLeakageDetector…\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.033954\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.027003\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.026371\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.030731\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.030862\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.033527\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.061908\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.032004\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.033302\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.043767\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028851\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.025971\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.025968\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.037906\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.026087\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.034599\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.033157\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028131\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.036141\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028548\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.038307\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.027153\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.026884\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.045579\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.059360\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.083060\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.052810\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.050068\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.043149\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.032872\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.069832\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.051564\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.041831\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.026554\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.026851\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.052967\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.053965\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.033616\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.032501\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.105924\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.029637\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.116469\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.091579\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.062813\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.053532\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.053113\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.032604\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.035769\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.039079\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.032249\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028756\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028763\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.056234\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.055514\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.058296\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.092477\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.058516\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.024569\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.026236\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.040886\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.038274\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.025890\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.025060\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.037334\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.051179\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.034610\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.033044\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.047269\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.030037\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.025820\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.045480\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.035272\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.089924\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.054201\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.034597\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028749\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.027718\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.034275\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028716\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028448\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.030345\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.031443\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.029169\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.025920\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.031296\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.036115\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.032158\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.045911\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028827\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.042526\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.050452\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.029223\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.030787\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028390\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028879\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.025041\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1, 20) executed in 0:00:00.028023\n",
            "INFO:giskard.scanner.logger:OverconfidenceDetector: Running\n",
            "INFO:giskard.scanner.logger:OverconfidenceDetector: Calculating loss\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1055, 20) executed in 0:00:00.035717\n",
            "INFO:giskard.scanner.logger:OverconfidenceDetector: Loss calculated (took 0:00:00.046113)\n",
            "INFO:giskard.scanner.logger:OverconfidenceDetector: Finding data slices\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataLeakageDetector: 0 issue detected. (Took 0:00:06.207372)\n",
            "Running detector OverconfidenceDetector…\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.scanner.logger:OverconfidenceDetector: 27 slices found (took 0:00:01.835561)\n",
            "INFO:giskard.scanner.logger:OverconfidenceDetector: Analyzing issues\n",
            "INFO:giskard.datasets.base:Your 'pandas.DataFrame' is successfully wrapped by Giskard's 'Dataset' wrapper class.\n",
            "INFO:giskard.scanner.logger:OverconfidenceDetector: Using overconfidence threshold = 0.5\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (30, 20) executed in 0:00:00.058377\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (23, 20) executed in 0:00:00.053315\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (26, 20) executed in 0:00:00.088822\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (20, 20) executed in 0:00:00.056733\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (21, 20) executed in 0:00:00.054811\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (23, 20) executed in 0:00:00.028805\n",
            "INFO:giskard.scanner.logger:OverconfidenceDetector: 6 issues found (took 0:00:01.238337)\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Running\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Calculating loss\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1055, 20) executed in 0:00:00.085329\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Loss calculated (took 0:00:00.100983)\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Finding data slices\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "OverconfidenceDetector: 6 issues detected. (Took 0:00:03.146652)\n",
            "Running detector UnderconfidenceDetector…\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: 56 slices found (took 0:00:04.439201)\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Analyzing issues\n",
            "INFO:giskard.datasets.base:Your 'pandas.DataFrame' is successfully wrapped by Giskard's 'Dataset' wrapper class.\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Testing slice `Dependents` == \"No\"\tUnderconfidence rate (slice) = 0.028 (global 0.023) Δm = 0.226\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (21, 20) executed in 0:00:00.017425\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Testing slice `PhoneService` == \"Yes\"\tUnderconfidence rate (slice) = 0.021 (global 0.023) Δm = -0.068\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Testing slice `OnlineSecurity` == \"No\"\tUnderconfidence rate (slice) = 0.037 (global 0.023) Δm = 0.637\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (20, 20) executed in 0:00:00.020779\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Testing slice `TechSupport` == \"No\"\tUnderconfidence rate (slice) = 0.037 (global 0.023) Δm = 0.637\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (20, 20) executed in 0:00:00.022637\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: Testing slice `Contract` == \"Month-to-month\"\tUnderconfidence rate (slice) = 0.040 (global 0.023) Δm = 0.776\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (24, 20) executed in 0:00:00.020886\n",
            "INFO:giskard.scanner.logger:UnderconfidenceDetector: 4 issues found (took 0:00:00.685748)\n",
            "INFO:giskard.scanner.logger:StochasticityDetector: Running\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (100, 20) executed in 0:00:00.017365\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (100, 20) executed in 0:00:00.021415\n",
            "INFO:giskard.scanner.logger:EthicalBiasDetector: Running with transformations=['Switch Gender', 'Switch Religion', 'Switch countries from high- to low-income and vice versa'] threshold=None output_sensitivity=None num_samples=None\n",
            "INFO:giskard.scanner.logger:TextPerturbationDetector: Running with transformations=['Transform to uppercase', 'Transform to lowercase', 'Transform to title case', 'Add typos', 'Punctuation Removal', 'Transform numbers to words', 'Accent Removal'] threshold=None output_sensitivity=None num_samples=None\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "UnderconfidenceDetector: 4 issues detected. (Took 0:00:05.259773)\n",
            "Running detector StochasticityDetector…\n",
            "StochasticityDetector: 0 issue detected. (Took 0:00:00.051666)\n",
            "Running detector EthicalBiasDetector…\n",
            "EthicalBiasDetector: 0 issue detected. (Took 0:00:00.003023)\n",
            "Running detector TextPerturbationDetector…\n",
            "TextPerturbationDetector: 0 issue detected. (Took 0:00:00.003296)\n",
            "Scan completed: 25 issues found. (Took 0:00:27.918871)\n"
          ]
        },
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              "\n",
              "&lt;/head&gt;\n",
              "\n",
              "&lt;body&gt;\n",
              "    \n",
              "&lt;div class=&quot;dark&quot;&gt;\n",
              "    &lt;div id=&quot;gsk-scan&quot; class=&quot;dark:text-white dark:bg-zinc-800 rounded border border-gray-500&quot;&gt;\n",
              "        &lt;!-- TAB HEADER --&gt;\n",
              "&lt;div class=&quot;flex items-end pt-1 dark:bg-zinc-900 rounded-t&quot;&gt;\n",
              "    \n",
              "    &lt;div class=&quot;flex items-center px-4 dark:fill-white border-b border-gray-500 h-11&quot;&gt;\n",
              "        &lt;div&gt;\n",
              "            &lt;svg xmlns=&quot;http://www.w3.org/2000/svg&quot; width=&quot;30&quot; height=&quot;15&quot; fill=&quot;none&quot;&gt;\n",
              "                &lt;path fill=&quot;#fff&quot; fill-rule=&quot;evenodd&quot;\n",
              "                    d=&quot;M22.504 1.549a4.196 4.196 0 0 1 2.573-.887v.002a3.783 3.783 0 0 1 2.706 1.086 3.783 3.783 0 0 1 1.126 2.69 3.771 3.771 0 0 1-1.126 2.69 3.77 3.77 0 0 1-2.706 1.085l-4.794.011-2.533 3.467L8.203 15l2.881-3.335a9.829 9.829 0 0 1-4.663-1.68H3.185L0 7.163h3.934C4.263 3.165 8.187 0 12.96 0c2.24 0 4.489.696 6.175 1.909a7.423 7.423 0 0 1 1.882 1.919 4.194 4.194 0 0 1 1.487-2.28ZM7.05 3.249l3.91 3.915h1.505L7.89 2.584a7.773 7.773 0 0 0-.84.665Zm4.079-2.008 5.923 5.923h1.503l-6.086-6.087c-.45.023-.898.078-1.34.164ZM4.574 8.226h-1.77l.784.693h1.584a8.454 8.454 0 0 1-.598-.693Zm9.479 0H5.984c1.469 1.477 3.656 2.377 5.977 2.422l2.092-2.422Zm-2.458 4.472 5.492-1.902 1.878-2.569h-3.508l-3.862 4.47Zm10.361-5.552h3.265a2.714 2.714 0 0 0 1.747-4.648 2.711 2.711 0 0 0-1.888-.773 3.127 3.127 0 0 0-3.123 3.124v2.297Zm3.659-3.73a.677.677 0 1 1-.134 1.348.677.677 0 0 1 .134-1.348Z&quot;\n",
              "                    clip-rule=&quot;evenodd&quot; /&gt;\n",
              "            &lt;/svg&gt;\n",
              "        &lt;/div&gt;\n",
              "        &lt;span class=&quot;uppercase text-sm ml-2 py-2 leading-4 w-min&quot;&gt;25&amp;nbsp;issues detected&lt;/span&gt;\n",
              "    &lt;/div&gt;\n",
              "\n",
              "    \n",
              "    \n",
              "    &lt;div data-tab-target=&quot;Performance&quot;\n",
              "        class=&quot;tab-header cursor-pointer group active&quot;&gt;\n",
              "        &lt;div\n",
              "            class=&quot;overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap&quot;&gt;\n",
              "            Performance\n",
              "\n",
              "            &lt;span\n",
              "                class=&quot;ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-red-400&quot;&gt;\n",
              "                15\n",
              "            &lt;/span&gt;\n",
              "        &lt;/div&gt;\n",
              "    &lt;/div&gt;\n",
              "    \n",
              "    &lt;div data-tab-target=&quot;Overconfidence&quot;\n",
              "        class=&quot;tab-header cursor-pointer group&quot;&gt;\n",
              "        &lt;div\n",
              "            class=&quot;overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap&quot;&gt;\n",
              "            Overconfidence\n",
              "\n",
              "            &lt;span\n",
              "                class=&quot;ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-red-400&quot;&gt;\n",
              "                6\n",
              "            &lt;/span&gt;\n",
              "        &lt;/div&gt;\n",
              "    &lt;/div&gt;\n",
              "    \n",
              "    &lt;div data-tab-target=&quot;Underconfidence&quot;\n",
              "        class=&quot;tab-header cursor-pointer group&quot;&gt;\n",
              "        &lt;div\n",
              "            class=&quot;overflow-hidden text-ellipsis group-[.active]:bg-zinc-800 group-[.active]:border-b-zinc-800 group-[.active]:border-gray-500 border-l px-3 py-2 border-r border-t border-b border-gray-600 border-b-gray-500 h-11 whitespace-nowrap&quot;&gt;\n",
              "            Underconfidence\n",
              "\n",
              "            &lt;span\n",
              "                class=&quot;ml-1 rounded-full text-xs min-w-4 min-h-4 px-1 py-0.5 inline-block text-center bg-red-400&quot;&gt;\n",
              "                4\n",
              "            &lt;/span&gt;\n",
              "        &lt;/div&gt;\n",
              "    &lt;/div&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;div class=&quot;flex-grow border-b border-gray-500 h-11&quot;&gt;&lt;/div&gt;\n",
              "&lt;/div&gt;\n",
              "&lt;!-- TAB HEADER END --&gt;\n",
              "        \n",
              "&lt;div id=&quot;Performance&quot; role=&quot;tabpanel&quot; class=&quot;m-4 mb-4&quot;&gt;\n",
              "    &lt;div class=&quot;p-3 bg-amber-100/40 rounded-sm w-full flex align-middle&quot;&gt;\n",
              "        &lt;div class=&quot;text-amber-100 mt-1.5&quot;&gt;\n",
              "            &lt;svg xmlns=&quot;http://www.w3.org/2000/svg&quot; fill=&quot;none&quot; viewBox=&quot;0 0 24 24&quot; stroke-width=&quot;1.5&quot;\n",
              "                stroke=&quot;currentColor&quot; class=&quot;w-6 h-6&quot;&gt;\n",
              "                &lt;path stroke-linecap=&quot;round&quot; stroke-linejoin=&quot;round&quot;\n",
              "                    d=&quot;M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z&quot; /&gt;\n",
              "            &lt;/svg&gt;\n",
              "        &lt;/div&gt;\n",
              "        &lt;div class=&quot;ml-2 text-amber-100 text-sm&quot;&gt;\n",
              "            &lt;div class=&quot;prose&quot;&gt;\n",
              "                &lt;p&gt;We found some data slices in your dataset on which your model performance is lower than average. Performance bias may\n",
              "happen for different reasons:&lt;/p&gt;\n",
              "&lt;ul&gt;\n",
              "&lt;li&gt;Not enough examples in the low-performing data slice in the training set&lt;/li&gt;\n",
              "&lt;li&gt;Wrong labels in the training set in the low-performing data slice&lt;/li&gt;\n",
              "&lt;li&gt;Drift between your training set and test set&lt;/li&gt;\n",
              "&lt;/ul&gt;\n",
              "&lt;p&gt;To learn more about causes and solutions, check our &lt;a href=&quot;https://docs.giskard.ai/en/stable/knowledge/key_vulnerabilities/performance_bias/index.html&quot;&gt;guide on performance bias.&lt;/a&gt;&lt;/p&gt;\n",
              "            &lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "    &lt;/div&gt;\n",
              "\n",
              "    &lt;div class=&quot;flex items-center space-x-1&quot;&gt;\n",
              "        &lt;h2 class=&quot;uppercase my-4 mr-2 font-medium&quot;&gt;Issues&lt;/h2&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-xs border rounded px-1 uppercase text-red-400 border-red-400&quot;&gt;15\n",
              "            major&lt;/span&gt;\n",
              "        \n",
              "        \n",
              "        \n",
              "    &lt;/div&gt;\n",
              "\n",
              "    \n",
              "        &lt;div class=&quot;overflow-x-auto overflow-y-clip h-max&quot;&gt;\n",
              "    &lt;table class=&quot;table-auto w-full text-white&quot;&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `Contract` == &amp;#34;One year&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.000e+00&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -100.00% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            215 samples affected&lt;br&gt;\n",
              "            (20.4% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `Contract` == &amp;#34;One year&amp;#34;, the Recall is 100.0% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;Contract&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6035&lt;/th&gt;\n",
              "      &lt;td&gt;One year&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.76)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4795&lt;/th&gt;\n",
              "      &lt;td&gt;One year&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4675&lt;/th&gt;\n",
              "      &lt;td&gt;One year&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.73)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `tenure` &amp;gt;= 44.500 AND `tenure` &amp;lt; 70.500\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.062&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -88.50% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            303 samples affected&lt;br&gt;\n",
              "            (28.7% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `tenure` &amp;gt;= 44.500 AND `tenure` &amp;lt; 70.500, the Recall is 88.5% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;tenure&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;65&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6032&lt;/th&gt;\n",
              "      &lt;td&gt;68&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.53)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6035&lt;/th&gt;\n",
              "      &lt;td&gt;66&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.76)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `PaymentMethod` == &amp;#34;Bank transfer&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.167&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -69.34% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            252 samples affected&lt;br&gt;\n",
              "            (23.9% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `PaymentMethod` == &amp;#34;Bank transfer&amp;#34;, the Recall is 69.34% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;PaymentMethod&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;Bank transfer&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6347&lt;/th&gt;\n",
              "      &lt;td&gt;Bank transfer&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.55)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;898&lt;/th&gt;\n",
              "      &lt;td&gt;Bank transfer&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.52)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `TotalCharges` &amp;gt;= 3485.375\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.171&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -68.59% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            304 samples affected&lt;br&gt;\n",
              "            (28.8% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `TotalCharges` &amp;gt;= 3485.375, the Recall is 68.59% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;TotalCharges&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;4820.15&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6032&lt;/th&gt;\n",
              "      &lt;td&gt;7299.65&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.53)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6035&lt;/th&gt;\n",
              "      &lt;td&gt;7491.75&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.76)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `OnlineSecurity` == &amp;#34;Yes&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.196&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -63.93% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            308 samples affected&lt;br&gt;\n",
              "            (29.2% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `OnlineSecurity` == &amp;#34;Yes&amp;#34;, the Recall is 63.93% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;OnlineSecurity&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;898&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.52)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;354&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.59)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `InternetService` == &amp;#34;DSL&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.237&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -56.43% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            370 samples affected&lt;br&gt;\n",
              "            (35.1% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `InternetService` == &amp;#34;DSL&amp;#34;, the Recall is 56.43% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;InternetService&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;DSL&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;354&lt;/th&gt;\n",
              "      &lt;td&gt;DSL&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.59)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1655&lt;/th&gt;\n",
              "      &lt;td&gt;DSL&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.68)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `TechSupport` == &amp;#34;Yes&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.255&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -53.03% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            308 samples affected&lt;br&gt;\n",
              "            (29.2% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `TechSupport` == &amp;#34;Yes&amp;#34;, the Recall is 53.03% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;TechSupport&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;898&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.52)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4058&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.70)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `PaymentMethod` == &amp;#34;Credit card&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.316&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -41.90% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            217 samples affected&lt;br&gt;\n",
              "            (20.6% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `PaymentMethod` == &amp;#34;Credit card&amp;#34;, the Recall is 41.9% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;PaymentMethod&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3971&lt;/th&gt;\n",
              "      &lt;td&gt;Credit card&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.98)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1822&lt;/th&gt;\n",
              "      &lt;td&gt;Credit card&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.59)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1008&lt;/th&gt;\n",
              "      &lt;td&gt;Credit card&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.58)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `Dependents` == &amp;#34;Yes&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.327&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -39.85% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            302 samples affected&lt;br&gt;\n",
              "            (28.6% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `Dependents` == &amp;#34;Yes&amp;#34;, the Recall is 39.85% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;Dependents&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1822&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.59)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4058&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.70)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `PaperlessBilling` == &amp;#34;No&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.348&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -36.01% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            424 samples affected&lt;br&gt;\n",
              "            (40.2% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `PaperlessBilling` == &amp;#34;No&amp;#34;, the Recall is 36.01% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;PaperlessBilling&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3971&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.98)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6467&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.61)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;684&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.57)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `PaymentMethod` == &amp;#34;Mailed check&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Recall = 0.348&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.544)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -36.01% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            240 samples affected&lt;br&gt;\n",
              "            (22.7% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `PaymentMethod` == &amp;#34;Mailed check&amp;#34;, the Recall is 36.01% lower than the global Recall.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;PaymentMethod&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;408&lt;/th&gt;\n",
              "      &lt;td&gt;Mailed check&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.75)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;684&lt;/th&gt;\n",
              "      &lt;td&gt;Mailed check&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.57)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1655&lt;/th&gt;\n",
              "      &lt;td&gt;Mailed check&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.68)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `MonthlyCharges` &amp;lt; 20.775\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Balanced Accuracy = 0.500&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.720)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -30.59% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            154 samples affected&lt;br&gt;\n",
              "            (14.6% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `MonthlyCharges` &amp;lt; 20.775, the Balanced Accuracy is 30.59% lower than the global Balanced Accuracy.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;MonthlyCharges&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;408&lt;/th&gt;\n",
              "      &lt;td&gt;19.90&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.75)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;5326&lt;/th&gt;\n",
              "      &lt;td&gt;20.00&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.84)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1837&lt;/th&gt;\n",
              "      &lt;td&gt;20.05&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.77)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `InternetService` == &amp;#34;No&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Balanced Accuracy = 0.500&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.720)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -30.59% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            210 samples affected&lt;br&gt;\n",
              "            (19.9% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `InternetService` == &amp;#34;No&amp;#34;, the Balanced Accuracy is 30.59% lower than the global Balanced Accuracy.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;InternetService&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3971&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.98)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;408&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.75)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;5326&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.84)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `OnlineSecurity` == &amp;#34;No internet service&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Balanced Accuracy = 0.500&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.720)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -30.59% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            210 samples affected&lt;br&gt;\n",
              "            (19.9% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `OnlineSecurity` == &amp;#34;No internet service&amp;#34;, the Balanced Accuracy is 30.59% lower than the global Balanced Accuracy.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;OnlineSecurity&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3971&lt;/th&gt;\n",
              "      &lt;td&gt;No internet service&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.98)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;408&lt;/th&gt;\n",
              "      &lt;td&gt;No internet service&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.75)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;5326&lt;/th&gt;\n",
              "      &lt;td&gt;No internet service&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.84)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `OnlineBackup` == &amp;#34;No internet service&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Balanced Accuracy = 0.500&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.720)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            -30.59% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            210 samples affected&lt;br&gt;\n",
              "            (19.9% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `OnlineBackup` == &amp;#34;No internet service&amp;#34;, the Balanced Accuracy is 30.59% lower than the global Balanced Accuracy.\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;OnlineBackup&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3971&lt;/th&gt;\n",
              "      &lt;td&gt;No internet service&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.98)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;408&lt;/th&gt;\n",
              "      &lt;td&gt;No internet service&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.75)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;5326&lt;/th&gt;\n",
              "      &lt;td&gt;No internet service&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.84)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "    &lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "    \n",
              "&lt;/div&gt;\n",
              "\n",
              "&lt;div id=&quot;Overconfidence&quot; role=&quot;tabpanel&quot; class=&quot;m-4 mb-4 hidden&quot;&gt;\n",
              "    &lt;div class=&quot;p-3 bg-amber-100/40 rounded-sm w-full flex align-middle&quot;&gt;\n",
              "        &lt;div class=&quot;text-amber-100 mt-1.5&quot;&gt;\n",
              "            &lt;svg xmlns=&quot;http://www.w3.org/2000/svg&quot; fill=&quot;none&quot; viewBox=&quot;0 0 24 24&quot; stroke-width=&quot;1.5&quot;\n",
              "                stroke=&quot;currentColor&quot; class=&quot;w-6 h-6&quot;&gt;\n",
              "                &lt;path stroke-linecap=&quot;round&quot; stroke-linejoin=&quot;round&quot;\n",
              "                    d=&quot;M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z&quot; /&gt;\n",
              "            &lt;/svg&gt;\n",
              "        &lt;/div&gt;\n",
              "        &lt;div class=&quot;ml-2 text-amber-100 text-sm&quot;&gt;\n",
              "            &lt;div class=&quot;prose&quot;&gt;\n",
              "                &lt;p&gt;We found some data slices in your dataset containing significant number of overconfident predictions. Overconfident predictions are rows that are incorrect but are predicted with high probabilities or confidence scores. This happens when:&lt;/p&gt;\n",
              "&lt;ul&gt;\n",
              "&lt;li&gt;There are not enough examples in the overconfident data slice in the training set&lt;/li&gt;\n",
              "&lt;li&gt;Wrongly labeled examples in the training set in the overconfident data slice&lt;/li&gt;\n",
              "&lt;li&gt;For imbalanced datasets, the model may assign high probabilities to predictions of the majority class&lt;/li&gt;\n",
              "&lt;/ul&gt;\n",
              "&lt;p&gt;To learn more about causes and solutions, check our &lt;a href=&quot;https://docs.giskard.ai/en/stable/knowledge/key_vulnerabilities/overconfidence/index.html&quot;&gt;guide on overconfidence issues.&lt;/a&gt;&lt;/p&gt;\n",
              "            &lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "    &lt;/div&gt;\n",
              "\n",
              "    &lt;div class=&quot;flex items-center space-x-1&quot;&gt;\n",
              "        &lt;h2 class=&quot;uppercase my-4 mr-2 font-medium&quot;&gt;Issues&lt;/h2&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-xs border rounded px-1 uppercase text-red-400 border-red-400&quot;&gt;2\n",
              "            major&lt;/span&gt;\n",
              "        \n",
              "        \n",
              "        &lt;span class=&quot;text-xs border rounded px-1 uppercase text-amber-200 border-amber-200&quot;&gt;4\n",
              "            medium&lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/div&gt;\n",
              "\n",
              "    \n",
              "        &lt;div class=&quot;overflow-x-auto overflow-y-clip h-max&quot;&gt;\n",
              "    &lt;table class=&quot;table-auto w-full text-white&quot;&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `OnlineBackup` == &amp;#34;Yes&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Overconfidence rate = 0.362&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.262)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            +38.24% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            371 samples affected&lt;br&gt;\n",
              "            (35.2% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `OnlineBackup` == &amp;#34;Yes&amp;#34;, we found a significantly higher number of overconfident wrong predictions (21 samples, corresponding to 36.21% of the wrong predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;OnlineBackup&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;2412&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.96)&lt;br&gt;Yes (p = 0.04)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;5270&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.94)&lt;br&gt;Yes (p = 0.06)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;5475&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.94)&lt;br&gt;Yes (p = 0.06)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `PaperlessBilling` == &amp;#34;No&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Overconfidence rate = 0.354&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.262)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            +35.10% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            424 samples affected&lt;br&gt;\n",
              "            (40.2% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `PaperlessBilling` == &amp;#34;No&amp;#34;, we found a significantly higher number of overconfident wrong predictions (23 samples, corresponding to 35.38% of the wrong predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;PaperlessBilling&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4795&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;br&gt;Yes (p = 0.01)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3971&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.98)&lt;br&gt;Yes (p = 0.02)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1780&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.96)&lt;br&gt;Yes (p = 0.04)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `MultipleLines` == &amp;#34;Yes&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Overconfidence rate = 0.306&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.262)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-amber-200&quot;&gt;\n",
              "            +16.79% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            465 samples affected&lt;br&gt;\n",
              "            (44.1% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `MultipleLines` == &amp;#34;Yes&amp;#34;, we found a significantly higher number of overconfident wrong predictions (26 samples, corresponding to 30.59% of the wrong predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;MultipleLines&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3971&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.98)&lt;br&gt;Yes (p = 0.02)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;2536&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.95)&lt;br&gt;Yes (p = 0.05)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1840&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.95)&lt;br&gt;Yes (p = 0.05)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `Partner` == &amp;#34;Yes&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Overconfidence rate = 0.299&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.262)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-amber-200&quot;&gt;\n",
              "            +14.05% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            512 samples affected&lt;br&gt;\n",
              "            (48.5% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `Partner` == &amp;#34;Yes&amp;#34;, we found a significantly higher number of overconfident wrong predictions (23 samples, corresponding to 29.87% of the wrong predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;Partner&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4795&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;br&gt;Yes (p = 0.01)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;br&gt;Yes (p = 0.01)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;2412&lt;/th&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.96)&lt;br&gt;Yes (p = 0.04)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `InternetService` == &amp;#34;DSL&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Overconfidence rate = 0.299&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.262)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-amber-200&quot;&gt;\n",
              "            +13.98% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            370 samples affected&lt;br&gt;\n",
              "            (35.1% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `InternetService` == &amp;#34;DSL&amp;#34;, we found a significantly higher number of overconfident wrong predictions (20 samples, corresponding to 29.85% of the wrong predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;InternetService&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4642&lt;/th&gt;\n",
              "      &lt;td&gt;DSL&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;br&gt;Yes (p = 0.01)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;2412&lt;/th&gt;\n",
              "      &lt;td&gt;DSL&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.96)&lt;br&gt;Yes (p = 0.04)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;5270&lt;/th&gt;\n",
              "      &lt;td&gt;DSL&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.94)&lt;br&gt;Yes (p = 0.06)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `gender` == &amp;#34;Female&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Overconfidence rate = 0.288&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.262)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-amber-200&quot;&gt;\n",
              "            +10.14% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            528 samples affected&lt;br&gt;\n",
              "            (50.0% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in the dataset where `gender` == &amp;#34;Female&amp;#34;, we found a significantly higher number of overconfident wrong predictions (30 samples, corresponding to 28.85% of the wrong predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;gender&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4795&lt;/th&gt;\n",
              "      &lt;td&gt;Female&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.99)&lt;br&gt;Yes (p = 0.01)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3971&lt;/th&gt;\n",
              "      &lt;td&gt;Female&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.98)&lt;br&gt;Yes (p = 0.02)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;1780&lt;/th&gt;\n",
              "      &lt;td&gt;Female&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.96)&lt;br&gt;Yes (p = 0.04)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "    &lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "    \n",
              "&lt;/div&gt;\n",
              "\n",
              "&lt;div id=&quot;Underconfidence&quot; role=&quot;tabpanel&quot; class=&quot;m-4 mb-4 hidden&quot;&gt;\n",
              "    &lt;div class=&quot;p-3 bg-amber-100/40 rounded-sm w-full flex align-middle&quot;&gt;\n",
              "        &lt;div class=&quot;text-amber-100 mt-1.5&quot;&gt;\n",
              "            &lt;svg xmlns=&quot;http://www.w3.org/2000/svg&quot; fill=&quot;none&quot; viewBox=&quot;0 0 24 24&quot; stroke-width=&quot;1.5&quot;\n",
              "                stroke=&quot;currentColor&quot; class=&quot;w-6 h-6&quot;&gt;\n",
              "                &lt;path stroke-linecap=&quot;round&quot; stroke-linejoin=&quot;round&quot;\n",
              "                    d=&quot;M12 9v3.75m-9.303 3.376c-.866 1.5.217 3.374 1.948 3.374h14.71c1.73 0 2.813-1.874 1.948-3.374L13.949 3.378c-.866-1.5-3.032-1.5-3.898 0L2.697 16.126zM12 15.75h.007v.008H12v-.008z&quot; /&gt;\n",
              "            &lt;/svg&gt;\n",
              "        &lt;/div&gt;\n",
              "        &lt;div class=&quot;ml-2 text-amber-100 text-sm&quot;&gt;\n",
              "            &lt;div class=&quot;prose&quot;&gt;\n",
              "                &lt;p&gt;We found some data slices in your dataset containing significant number of underconfident predictions. Underconfident predictions refer to situations where the predicted label has a probability that is very close to the probability of the next highest probability label. This happens when:&lt;/p&gt;\n",
              "&lt;ul&gt;\n",
              "&lt;li&gt;There are not enough examples in the training set for the underconfident data slice&lt;/li&gt;\n",
              "&lt;li&gt;The model is too simple and struggles to capture the complexity of the underlying data&lt;/li&gt;\n",
              "&lt;li&gt;The underconfident data slice contains inherent noise or overlapping feature distributions&lt;/li&gt;\n",
              "&lt;/ul&gt;\n",
              "&lt;p&gt;To learn more about causes and solutions, check our &lt;a href=&quot;https://docs.giskard.ai/en/stable/knowledge/key_vulnerabilities/underconfidence/index.html&quot;&gt;guide on underconfidence issues.&lt;/a&gt;&lt;/p&gt;\n",
              "            &lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "    &lt;/div&gt;\n",
              "\n",
              "    &lt;div class=&quot;flex items-center space-x-1&quot;&gt;\n",
              "        &lt;h2 class=&quot;uppercase my-4 mr-2 font-medium&quot;&gt;Issues&lt;/h2&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-xs border rounded px-1 uppercase text-red-400 border-red-400&quot;&gt;4\n",
              "            major&lt;/span&gt;\n",
              "        \n",
              "        \n",
              "        \n",
              "    &lt;/div&gt;\n",
              "\n",
              "    \n",
              "        &lt;div class=&quot;overflow-x-auto overflow-y-clip h-max&quot;&gt;\n",
              "    &lt;table class=&quot;table-auto w-full text-white&quot;&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `Contract` == &amp;#34;Month-to-month&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Underconfidence rate = 0.040&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.023)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            +77.61% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            594 samples affected&lt;br&gt;\n",
              "            (56.3% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in your dataset where `Contract` == &amp;#34;Month-to-month&amp;#34;, we found a significantly higher number of underconfident predictions (24 samples, corresponding to 4.04% of the predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;Contract&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4641&lt;/th&gt;\n",
              "      &lt;td&gt;Month-to-month&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.50)&lt;br&gt;No (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3773&lt;/th&gt;\n",
              "      &lt;td&gt;Month-to-month&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.50)&lt;br&gt;Yes (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6055&lt;/th&gt;\n",
              "      &lt;td&gt;Month-to-month&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.50)&lt;br&gt;No (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `OnlineSecurity` == &amp;#34;No&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Underconfidence rate = 0.037&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.023)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            +63.72% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            537 samples affected&lt;br&gt;\n",
              "            (50.9% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in your dataset where `OnlineSecurity` == &amp;#34;No&amp;#34;, we found a significantly higher number of underconfident predictions (20 samples, corresponding to 3.72% of the predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;OnlineSecurity&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4641&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.50)&lt;br&gt;No (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3773&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.50)&lt;br&gt;Yes (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6055&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.50)&lt;br&gt;No (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `TechSupport` == &amp;#34;No&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Underconfidence rate = 0.037&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.023)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            +63.72% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            537 samples affected&lt;br&gt;\n",
              "            (50.9% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in your dataset where `TechSupport` == &amp;#34;No&amp;#34;, we found a significantly higher number of underconfident predictions (20 samples, corresponding to 3.72% of the predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;TechSupport&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4641&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.50)&lt;br&gt;No (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3773&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.50)&lt;br&gt;Yes (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6055&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.50)&lt;br&gt;No (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "        &lt;tbody class=&quot;first:border-t border-b border-zinc-500&quot;&gt;\n",
              "            &lt;tr class=&quot;gsk-issue text-sm group peer text-left cursor-pointer hover:bg-zinc-700&quot;&gt;\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        \n",
              "        &lt;code class=&quot;mono text-blue-300&quot;&gt;\n",
              "        `Dependents` == &amp;#34;No&amp;#34;\n",
              "        &lt;/code&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "\n",
              "    \n",
              "\n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span&gt;Underconfidence rate = 0.028&lt;/span&gt;\n",
              "        \n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            (Global = 0.023)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;        \n",
              "        &lt;span class=&quot;text-red-400&quot;&gt;\n",
              "            +22.59% than global\n",
              "        &lt;/span&gt;\n",
              "    &lt;/td&gt;\n",
              "    \n",
              "    \n",
              "    &lt;td class=&quot;p-3&quot;&gt;\n",
              "        &lt;span class=&quot;text-gray-400&quot;&gt;\n",
              "            753 samples affected&lt;br&gt;\n",
              "            (71.4% of dataset)\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "    &lt;td class=&quot;p-3 text-xs text-right space-x-1&quot;&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;gsk-issue-detail-btn inline-block group-[.open]:hidden border border-zinc-100/50 text-zinc-100/90 hover:bg-zinc-500 hover:border-zinc-500 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Show details&lt;/a&gt;\n",
              "        &lt;a href=&quot;#&quot;\n",
              "            class=&quot;hidden group-[.open]:inline-block gsk-issue-detail-btn border border-zinc-500 text-zinc-100/90 bg-zinc-500 hover:bg-zinc-400 hover:text-white px-2 py-0.5 rounded-sm&quot;&gt;Hide details&lt;/a&gt;\n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "&lt;tr class=&quot;gsk-issue-detail text-left hidden  peer-[.open]:table-row border-b border-zinc-500 bg-zinc-700&quot;&gt;\n",
              "    &lt;td colspan=&quot;1000&quot; class=&quot;p-3&quot;&gt;\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm&quot;&gt;Description&lt;/h4&gt;\n",
              "        For records in your dataset where `Dependents` == &amp;#34;No&amp;#34;, we found a significantly higher number of underconfident predictions (21 samples, corresponding to 2.79% of the predictions in the data slice).\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Examples&lt;/h4&gt;\n",
              "        &lt;div class=&quot;text-white text-sm overflow-scroll&quot;&gt;\n",
              "            &lt;div&gt;\n",
              "&lt;style scoped&gt;\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "&lt;/style&gt;\n",
              "&lt;table border=&quot;1&quot; class=&quot;dataframe&quot;&gt;\n",
              "  &lt;thead&gt;\n",
              "    &lt;tr style=&quot;text-align: right;&quot;&gt;\n",
              "      &lt;th&gt;&lt;/th&gt;\n",
              "      &lt;th&gt;Dependents&lt;/th&gt;\n",
              "      &lt;th&gt;Churn&lt;/th&gt;\n",
              "      &lt;th&gt;Predicted `Churn`&lt;/th&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/thead&gt;\n",
              "  &lt;tbody&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;4641&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.50)&lt;br&gt;No (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;3773&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes&lt;/td&gt;\n",
              "      &lt;td&gt;No (p = 0.50)&lt;br&gt;Yes (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "    &lt;tr&gt;\n",
              "      &lt;th&gt;6055&lt;/th&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;No&lt;/td&gt;\n",
              "      &lt;td&gt;Yes (p = 0.50)&lt;br&gt;No (p = 0.50)&lt;/td&gt;\n",
              "    &lt;/tr&gt;\n",
              "  &lt;/tbody&gt;\n",
              "&lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "        &lt;/div&gt;\n",
              "        \n",
              "\n",
              "        \n",
              "\n",
              "        \n",
              "        &lt;h4 class=&quot;font-bold text-sm mt-4&quot;&gt;Taxonomy&lt;/h4&gt;\n",
              "        \n",
              "        &lt;span class=&quot;inline-block bg-blue-300/25 text-zinc-100 px-2 py-0.5 rounded-sm text-sm mr-1 my-2&quot;&gt;\n",
              "            avid-effect:performance:P0204\n",
              "        &lt;/span&gt;\n",
              "        \n",
              "        \n",
              "    &lt;/td&gt;\n",
              "&lt;/tr&gt;\n",
              "        &lt;/tbody&gt;\n",
              "        \n",
              "    &lt;/table&gt;\n",
              "&lt;/div&gt;\n",
              "    \n",
              "&lt;/div&gt;\n",
              "\n",
              "\n",
              "\n",
              "        \n",
              "        &lt;div class=&quot;p-4&quot;&gt;\n",
              "  &lt;h2 class=&quot;my-4 mr-2 font-medium&quot;&gt;What&#x27;s next?&lt;/h2&gt;\n",
              "  &lt;div class=&quot;text-sm my-2&quot;&gt;\n",
              "    &lt;h3 class=&quot;text-sm my-1&quot;&gt;\n",
              "      1. Generate a test suite from your scan results\n",
              "    &lt;/h3&gt;\n",
              "    &lt;pre&gt;&lt;code class=&quot;language-python rounded&quot;&gt;test_suite = results.generate_test_suite(&quot;My first test suite&quot;)&lt;/code&gt;&lt;/pre&gt;\n",
              "    &lt;br /&gt;\n",
              "    &lt;h3 class=&quot;text-sm my-1&quot;&gt;2. Run your test suite&lt;/h3&gt;\n",
              "    &lt;pre&gt;&lt;code class=&quot;language-python rounded&quot;&gt;test_suite.run()&lt;/code&gt;&lt;/pre&gt;\n",
              "  &lt;/div&gt;\n",
              "&lt;/div&gt;\n",
              "        \n",
              "    &lt;/div&gt;\n",
              "&lt;/div&gt;\n",
              "\n",
              "    \n",
              "&lt;script type=&quot;text/javascript&quot;&gt;\n",
              "&quot;use strict&quot;;function _typeof(u){return(_typeof=&quot;function&quot;==typeof Symbol&amp;&amp;&quot;symbol&quot;==typeof Symbol.iterator?function(u){return typeof u}:function(u){return u&amp;&amp;&quot;function&quot;==typeof Symbol&amp;&amp;u.constructor===Symbol&amp;&amp;u!==Symbol.prototype?&quot;symbol&quot;:typeof u})(u)}function _wrapNativeSuper(u){var e=&quot;function&quot;==typeof Map?new Map:void 0;return(_wrapNativeSuper=function(u){if(null===u||!_isNativeFunction(u))return u;if(&quot;function&quot;!=typeof u)throw new TypeError(&quot;Super expression must either be null or a function&quot;);if(void 0!==e){if(e.has(u))return e.get(u);e.set(u,t)}function t(){return _construct(u,arguments,_getPrototypeOf(this).constructor)}return t.prototype=Object.create(u.prototype,{constructor:{value:t,enumerable:!1,writable:!0,configurable:!0}}),_setPrototypeOf(t,u)})(u)}function 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              "        t = Number(w[e]);\n",
              "      O(b, \"Checking \" + e + \" is in range \" + i + \"-\" + n), t < i && (t = i, O(b, \"Set \" + e + \" to min value\")), n < t && (t = n, O(b, \"Set \" + e + \" to max value\")), w[e] = \"\" + t;\n",
              "    }\n",
              "    function o() {\n",
              "      var t = e.origin,\n",
              "        o = k[b] && k[b].checkOrigin;\n",
              "      if (o && \"\" + t != \"null\" && !function () {\n",
              "        if (o.constructor !== Array) return e = k[b] && k[b].remoteHost, O(b, \"Checking connection is from: \" + e), t === e;\n",
              "        var e,\n",
              "          n = 0,\n",
              "          i = !1;\n",
              "        for (O(b, \"Checking connection is from allowed list of origins: \" + o); n < o.length; n++) if (o[n] === t) {\n",
              "          i = !0;\n",
              "          break;\n",
              "        }\n",
              "        return i;\n",
              "      }()) throw new Error(\"Unexpected message received from: \" + t + \" for \" + w.iframe.id + \". Message was: \" + e.data + \". This error can be disabled by setting the checkOrigin: false option or by providing of array of trusted domains.\");\n",
              "      return 1;\n",
              "    }\n",
              "    function a(e) {\n",
              "      return p.slice(p.indexOf(\":\") + v + e);\n",
              "    }\n",
              "    function s(i, t) {\n",
              "      var e, n, o;\n",
              "      e = function e() {\n",
              "        var e, n;\n",
              "        A(\"Send Page Info\", \"pageInfo:\" + (e = document.body.getBoundingClientRect(), n = w.iframe.getBoundingClientRect(), JSON.stringify({\n",
              "          iframeHeight: n.height,\n",
              "          iframeWidth: n.width,\n",
              "          clientHeight: Math.max(document.documentElement.clientHeight, window.innerHeight || 0),\n",
              "          clientWidth: Math.max(document.documentElement.clientWidth, window.innerWidth || 0),\n",
              "          offsetTop: parseInt(n.top - e.top, 10),\n",
              "          offsetLeft: parseInt(n.left - e.left, 10),\n",
              "          scrollTop: window.pageYOffset,\n",
              "          scrollLeft: window.pageXOffset,\n",
              "          documentHeight: document.documentElement.clientHeight,\n",
              "          documentWidth: document.documentElement.clientWidth,\n",
              "          windowHeight: window.innerHeight,\n",
              "          windowWidth: window.innerWidth\n",
              "        })), i, t);\n",
              "      }, n = 32, z[o = t] || (z[o] = setTimeout(function () {\n",
              "        z[o] = null, e();\n",
              "      }, n));\n",
              "    }\n",
              "    function r(e) {\n",
              "      e = e.getBoundingClientRect();\n",
              "      return W(b), {\n",
              "        x: Math.floor(Number(e.left) + Number(M.x)),\n",
              "        y: Math.floor(Number(e.top) + Number(M.y))\n",
              "      };\n",
              "    }\n",
              "    function d(e) {\n",
              "      var n = e ? r(w.iframe) : {\n",
              "          x: 0,\n",
              "          y: 0\n",
              "        },\n",
              "        i = {\n",
              "          x: Number(w.width) + n.x,\n",
              "          y: Number(w.height) + n.y\n",
              "        };\n",
              "      O(b, \"Reposition requested from iFrame (offset x:\" + n.x + \" y:\" + n.y + \")\"), window.top === window.self ? (M = i, c(), O(b, \"--\")) : window.parentIFrame ? window.parentIFrame[\"scrollTo\" + (e ? \"Offset\" : \"\")](i.x, i.y) : R(b, \"Unable to scroll to requested position, window.parentIFrame not found\");\n",
              "    }\n",
              "    function c() {\n",
              "      !1 === l(\"onScroll\", M) ? S() : C(b);\n",
              "    }\n",
              "    function u(e) {\n",
              "      var e = e.split(\"#\")[1] || \"\",\n",
              "        n = decodeURIComponent(e),\n",
              "        n = document.getElementById(n) || document.getElementsByName(n)[0];\n",
              "      n ? (n = r(n), O(b, \"Moving to in page link (#\" + e + \") at x: \" + n.x + \" y: \" + n.y), M = {\n",
              "        x: n.x,\n",
              "        y: n.y\n",
              "      }, c(), O(b, \"--\")) : window.top === window.self ? O(b, \"In page link #\" + e + \" not found\") : window.parentIFrame ? window.parentIFrame.moveToAnchor(e) : O(b, \"In page link #\" + e + \" not found and window.parentIFrame not found\");\n",
              "    }\n",
              "    function f(e) {\n",
              "      var n,\n",
              "        i = {};\n",
              "      i = 0 === Number(w.width) && 0 === Number(w.height) ? {\n",
              "        x: (n = a(9).split(\":\"))[1],\n",
              "        y: n[0]\n",
              "      } : {\n",
              "        x: w.width,\n",
              "        y: w.height\n",
              "      }, l(e, {\n",
              "        iframe: w.iframe,\n",
              "        screenX: Number(i.x),\n",
              "        screenY: Number(i.y),\n",
              "        type: w.type\n",
              "      });\n",
              "    }\n",
              "    function l(e, n) {\n",
              "      return T(b, e, n);\n",
              "    }\n",
              "    function m() {\n",
              "      switch (k[b] && k[b].firstRun && k[b] && (k[b].firstRun = !1), w.type) {\n",
              "        case \"close\":\n",
              "          N(w.iframe);\n",
              "          break;\n",
              "        case \"message\":\n",
              "          n = a(6), O(b, \"onMessage passed: {iframe: \" + w.iframe.id + \", message: \" + n + \"}\"), l(\"onMessage\", {\n",
              "            iframe: w.iframe,\n",
              "            message: JSON.parse(n)\n",
              "          }), O(b, \"--\");\n",
              "          break;\n",
              "        case \"mouseenter\":\n",
              "          f(\"onMouseEnter\");\n",
              "          break;\n",
              "        case \"mouseleave\":\n",
              "          f(\"onMouseLeave\");\n",
              "          break;\n",
              "        case \"autoResize\":\n",
              "          k[b].autoResize = JSON.parse(a(9));\n",
              "          break;\n",
              "        case \"scrollTo\":\n",
              "          d(!1);\n",
              "          break;\n",
              "        case \"scrollToOffset\":\n",
              "          d(!0);\n",
              "          break;\n",
              "        case \"pageInfo\":\n",
              "          s(k[b] && k[b].iframe, b), r = b, e(\"Add \", F), k[r] && (k[r].stopPageInfo = o);\n",
              "          break;\n",
              "        case \"pageInfoStop\":\n",
              "          k[b] && k[b].stopPageInfo && (k[b].stopPageInfo(), delete k[b].stopPageInfo);\n",
              "          break;\n",
              "        case \"inPageLink\":\n",
              "          u(a(9));\n",
              "          break;\n",
              "        case \"reset\":\n",
              "          j(w);\n",
              "          break;\n",
              "        case \"init\":\n",
              "          i(), l(\"onInit\", w.iframe);\n",
              "          break;\n",
              "        default:\n",
              "          0 === Number(w.width) && 0 === Number(w.height) ? R(\"Unsupported message received (\" + w.type + \"), this is likely due to the iframe containing a later version of iframe-resizer than the parent page\") : i();\n",
              "      }\n",
              "      function e(n, i) {\n",
              "        function t() {\n",
              "          k[r] ? s(k[r].iframe, r) : o();\n",
              "        }\n",
              "        [\"scroll\", \"resize\"].forEach(function (e) {\n",
              "          O(r, n + e + \" listener for sendPageInfo\"), i(window, e, t);\n",
              "        });\n",
              "      }\n",
              "      function o() {\n",
              "        e(\"Remove \", B);\n",
              "      }\n",
              "      var r, n;\n",
              "    }\n",
              "    var g,\n",
              "      h,\n",
              "      p = e.data,\n",
              "      w = {},\n",
              "      b = null;\n",
              "    if (\"[iFrameResizerChild]Ready\" === p) for (var y in k) A(\"iFrame requested init\", L(y), k[y].iframe, y);else x === (\"\" + p).slice(0, I) && p.slice(I).split(\":\")[0] in k ? (w = n(), b = w.id, k[b] && (k[b].loaded = !0), (h = w.type in {\n",
              "      \"true\": 1,\n",
              "      \"false\": 1,\n",
              "      undefined: 1\n",
              "    }) && O(b, \"Ignoring init message from meta parent page\"), !h && (h = !0, k[g = b] || (h = !1, R(w.type + \" No settings for \" + g + \". Message was: \" + p)), h) && (O(b, \"Received: \" + p), g = !0, null === w.iframe && (R(b, \"IFrame (\" + w.id + \") not found\"), g = !1), g && o() && m())) : E(b, \"Ignored: \" + p);\n",
              "  }\n",
              "  function T(e, n, i) {\n",
              "    var t = null,\n",
              "      o = null;\n",
              "    if (k[e]) {\n",
              "      if (\"function\" != typeof (t = k[e][n])) throw new TypeError(n + \" on iFrame[\" + e + \"] is not a function\");\n",
              "      o = t(i);\n",
              "    }\n",
              "    return o;\n",
              "  }\n",
              "  function g(e) {\n",
              "    e = e.id;\n",
              "    delete k[e];\n",
              "  }\n",
              "  function N(e) {\n",
              "    var n = e.id;\n",
              "    if (!1 === T(n, \"onClose\", n)) O(n, \"Close iframe cancelled by onClose event\");else {\n",
              "      O(n, \"Removing iFrame: \" + n);\n",
              "      try {\n",
              "        e.parentNode && e.parentNode.removeChild(e);\n",
              "      } catch (e) {\n",
              "        R(e);\n",
              "      }\n",
              "      T(n, \"onClosed\", n), O(n, \"--\"), g(e);\n",
              "    }\n",
              "  }\n",
              "  function W(e) {\n",
              "    null === M && O(e, \"Get page position: \" + (M = {\n",
              "      x: window.pageXOffset === d ? document.documentElement.scrollLeft : window.pageXOffset,\n",
              "      y: window.pageYOffset === d ? document.documentElement.scrollTop : window.pageYOffset\n",
              "    }).x + \",\" + M.y);\n",
              "  }\n",
              "  function C(e) {\n",
              "    null !== M && (window.scrollTo(M.x, M.y), O(e, \"Set page position: \" + M.x + \",\" + M.y), S());\n",
              "  }\n",
              "  function S() {\n",
              "    M = null;\n",
              "  }\n",
              "  function j(e) {\n",
              "    O(e.id, \"Size reset requested by \" + (\"init\" === e.type ? \"host page\" : \"iFrame\")), W(e.id), P(function () {\n",
              "      H(e), A(\"reset\", \"reset\", e.iframe, e.id);\n",
              "    }, e, \"reset\");\n",
              "  }\n",
              "  function H(o) {\n",
              "    function i(e) {\n",
              "      var n;\n",
              "      function i() {\n",
              "        Object.keys(k).forEach(function (e) {\n",
              "          function n(e) {\n",
              "            return \"0px\" === (k[i] && k[i].iframe.style[e]);\n",
              "          }\n",
              "          var i;\n",
              "          k[i = e] && null !== k[i].iframe.offsetParent && (n(\"height\") || n(\"width\")) && A(\"Visibility change\", \"resize\", k[i].iframe, i);\n",
              "        });\n",
              "      }\n",
              "      function t(e) {\n",
              "        O(\"window\", \"Mutation observed: \" + e[0].target + \" \" + e[0].type), h(i, 16);\n",
              "      }\n",
              "      !a && \"0\" === o[e] && (a = !0, O(r, \"Hidden iFrame detected, creating visibility listener\"), e = m()) && (n = document.querySelector(\"body\"), new e(t).observe(n, {\n",
              "        attributes: !0,\n",
              "        attributeOldValue: !1,\n",
              "        characterData: !0,\n",
              "        characterDataOldValue: !1,\n",
              "        childList: !0,\n",
              "        subtree: !0\n",
              "      }));\n",
              "    }\n",
              "    function e(e) {\n",
              "      var n;\n",
              "      n = e, o.id ? (o.iframe.style[n] = o[n] + \"px\", O(o.id, \"IFrame (\" + r + \") \" + n + \" set to \" + o[n] + \"px\")) : O(\"undefined\", \"messageData id not set\"), i(e);\n",
              "    }\n",
              "    var r = o.iframe.id;\n",
              "    k[r] && (k[r].sizeHeight && e(\"height\"), k[r].sizeWidth) && e(\"width\");\n",
              "  }\n",
              "  function P(e, n, i) {\n",
              "    i !== n.type && r && !window.jasmine ? (O(n.id, \"Requesting animation frame\"), r(e)) : e();\n",
              "  }\n",
              "  function A(n, i, t, o, e) {\n",
              "    function r() {\n",
              "      var e;\n",
              "      t && \"contentWindow\" in t && null !== t.contentWindow ? (e = k[o] && k[o].targetOrigin, O(o, \"[\" + n + \"] Sending msg to iframe[\" + o + \"] (\" + i + \") targetOrigin: \" + e), t.contentWindow.postMessage(x + i, e)) : R(o, \"[\" + n + \"] IFrame(\" + o + \") not found\");\n",
              "    }\n",
              "    function a() {\n",
              "      e && k[o] && k[o].warningTimeout && (k[o].msgTimeout = setTimeout(function () {\n",
              "        !k[o] || k[o].loaded || s || (s = !0, R(o, \"IFrame has not responded within \" + k[o].warningTimeout / 1e3 + \" seconds. Check iFrameResizer.contentWindow.js has been loaded in iFrame. This message can be ignored if everything is working, or you can set the warningTimeout option to a higher value or zero to suppress this warning.\"));\n",
              "      }, k[o].warningTimeout));\n",
              "    }\n",
              "    var s = !1;\n",
              "    o = o || t.id, k[o] && (r(), a());\n",
              "  }\n",
              "  function L(e) {\n",
              "    return e + \":\" + k[e].bodyMarginV1 + \":\" + k[e].sizeWidth + \":\" + k[e].log + \":\" + k[e].interval + \":\" + k[e].enablePublicMethods + \":\" + k[e].autoResize + \":\" + k[e].bodyMargin + \":\" + k[e].heightCalculationMethod + \":\" + k[e].bodyBackground + \":\" + k[e].bodyPadding + \":\" + k[e].tolerance + \":\" + k[e].inPageLinks + \":\" + k[e].resizeFrom + \":\" + k[e].widthCalculationMethod + \":\" + k[e].mouseEvents;\n",
              "  }\n",
              "  function s(t, i) {\n",
              "    function e(i) {\n",
              "      var e = m();\n",
              "      e && (e = e, t.parentNode) && new e(function (e) {\n",
              "        e.forEach(function (e) {\n",
              "          Array.prototype.slice.call(e.removedNodes).forEach(function (e) {\n",
              "            e === t && N(t);\n",
              "          });\n",
              "        });\n",
              "      }).observe(t.parentNode, {\n",
              "        childList: !0\n",
              "      }), F(t, \"load\", function () {\n",
              "        var e, n;\n",
              "        A(\"iFrame.onload\", i, t, d, !0), e = k[r] && k[r].firstRun, n = k[r] && k[r].heightCalculationMethod in f, !e && n && j({\n",
              "          iframe: t,\n",
              "          height: 0,\n",
              "          width: 0,\n",
              "          type: \"init\"\n",
              "        });\n",
              "      }), A(\"init\", i, t, d, !0);\n",
              "    }\n",
              "    function o(e) {\n",
              "      var n = e.split(\"Callback\");\n",
              "      2 === n.length && (this[n = \"on\" + n[0].charAt(0).toUpperCase() + n[0].slice(1)] = this[e], delete this[e], R(r, \"Deprecated: '\" + e + \"' has been renamed '\" + n + \"'. The old method will be removed in the next major version.\"));\n",
              "    }\n",
              "    function n(e) {\n",
              "      if (e = e || {}, k[r] = Object.create(null), k[r].iframe = t, k[r].firstRun = !0, k[r].remoteHost = t.src && t.src.split(\"/\").slice(0, 3).join(\"/\"), \"object\" != _typeof(e)) throw new TypeError(\"Options is not an object\");\n",
              "      Object.keys(e).forEach(o, e);\n",
              "      var n,\n",
              "        i = e;\n",
              "      for (n in l) Object.prototype.hasOwnProperty.call(l, n) && (k[r][n] = (Object.prototype.hasOwnProperty.call(i, n) ? i : l)[n]);\n",
              "      k[r] && (k[r].targetOrigin = !0 !== k[r].checkOrigin || \"\" === (e = k[r].remoteHost) || null !== e.match(/^(about:blank|javascript:|file:\\/\\/)/) ? \"*\" : e);\n",
              "    }\n",
              "    var r = function (e) {\n",
              "      if (\"string\" != typeof e) throw new TypeError(\"Invaild id for iFrame. Expected String\");\n",
              "      var n;\n",
              "      return \"\" === e && (t.id = (n = i && i.id || l.id + c++, null !== document.getElementById(n) && (n += c++), e = n), u = (i || {}).log, O(e, \"Added missing iframe ID: \" + e + \" (\" + t.src + \")\")), e;\n",
              "    }(t.id);\n",
              "    if (r in k && \"iFrameResizer\" in t) R(r, \"Ignored iFrame, already setup.\");else {\n",
              "      switch (n(i), O(r, \"IFrame scrolling \" + (k[r] && k[r].scrolling ? \"enabled\" : \"disabled\") + \" for \" + r), t.style.overflow = !1 === (k[r] && k[r].scrolling) ? \"hidden\" : \"auto\", k[r] && k[r].scrolling) {\n",
              "        case \"omit\":\n",
              "          break;\n",
              "        case !0:\n",
              "          t.scrolling = \"yes\";\n",
              "          break;\n",
              "        case !1:\n",
              "          t.scrolling = \"no\";\n",
              "          break;\n",
              "        default:\n",
              "          t.scrolling = k[r] ? k[r].scrolling : \"no\";\n",
              "      }\n",
              "      s(\"Height\"), s(\"Width\"), a(\"maxHeight\"), a(\"minHeight\"), a(\"maxWidth\"), a(\"minWidth\"), \"number\" != typeof (k[r] && k[r].bodyMargin) && \"0\" !== (k[r] && k[r].bodyMargin) || (k[r].bodyMarginV1 = k[r].bodyMargin, k[r].bodyMargin = k[r].bodyMargin + \"px\"), e(L(r)), k[r] && (k[r].iframe.iFrameResizer = {\n",
              "        close: N.bind(null, k[r].iframe),\n",
              "        removeListeners: g.bind(null, k[r].iframe),\n",
              "        resize: A.bind(null, \"Window resize\", \"resize\", k[r].iframe),\n",
              "        moveToAnchor: function moveToAnchor(e) {\n",
              "          A(\"Move to anchor\", \"moveToAnchor:\" + e, k[r].iframe, r);\n",
              "        },\n",
              "        sendMessage: function sendMessage(e) {\n",
              "          A(\"Send Message\", \"message:\" + (e = JSON.stringify(e)), k[r].iframe, r);\n",
              "        }\n",
              "      });\n",
              "    }\n",
              "    function a(e) {\n",
              "      var n = k[r][e];\n",
              "      1 / 0 !== n && 0 !== n && (t.style[e] = \"number\" == typeof n ? n + \"px\" : n, O(r, \"Set \" + e + \" = \" + t.style[e]));\n",
              "    }\n",
              "    function s(e) {\n",
              "      if (k[r][\"min\" + e] > k[r][\"max\" + e]) throw new Error(\"Value for min\" + e + \" can not be greater than max\" + e);\n",
              "    }\n",
              "  }\n",
              "  function h(e, n) {\n",
              "    null === i && (i = setTimeout(function () {\n",
              "      i = null, e();\n",
              "    }, n));\n",
              "  }\n",
              "  function e() {\n",
              "    \"hidden\" !== document.visibilityState && (O(\"document\", \"Trigger event: Visibility change\"), h(function () {\n",
              "      b(\"Tab Visible\", \"resize\");\n",
              "    }, 16));\n",
              "  }\n",
              "  function b(i, t) {\n",
              "    Object.keys(k).forEach(function (e) {\n",
              "      var n;\n",
              "      k[n = e] && \"parent\" === k[n].resizeFrom && k[n].autoResize && !k[n].firstRun && A(i, t, k[e].iframe, e);\n",
              "    });\n",
              "  }\n",
              "  function y() {\n",
              "    F(window, \"message\", w), F(window, \"resize\", function () {\n",
              "      var e;\n",
              "      O(\"window\", \"Trigger event: \" + (e = \"resize\")), h(function () {\n",
              "        b(\"Window \" + e, \"resize\");\n",
              "      }, 16);\n",
              "    }), F(document, \"visibilitychange\", e), F(document, \"-webkit-visibilitychange\", e);\n",
              "  }\n",
              "  function n() {\n",
              "    function t(e, n) {\n",
              "      if (n) {\n",
              "        if (!n.tagName) throw new TypeError(\"Object is not a valid DOM element\");\n",
              "        if (\"IFRAME\" !== n.tagName.toUpperCase()) throw new TypeError(\"Expected <IFRAME> tag, found <\" + n.tagName + \">\");\n",
              "        s(n, e), o.push(n);\n",
              "      }\n",
              "    }\n",
              "    for (var o, e = [\"moz\", \"webkit\", \"o\", \"ms\"], n = 0; n < e.length && !r; n += 1) r = window[e[n] + \"RequestAnimationFrame\"];\n",
              "    return r ? r = r.bind(window) : O(\"setup\", \"RequestAnimationFrame not supported\"), y(), function (e, n) {\n",
              "      var i;\n",
              "      switch (o = [], (i = e) && i.enablePublicMethods && R(\"enablePublicMethods option has been removed, public methods are now always available in the iFrame\"), _typeof(n)) {\n",
              "        case \"undefined\":\n",
              "        case \"string\":\n",
              "          Array.prototype.forEach.call(document.querySelectorAll(n || \"iframe\"), t.bind(d, e));\n",
              "          break;\n",
              "        case \"object\":\n",
              "          t(e, n);\n",
              "          break;\n",
              "        default:\n",
              "          throw new TypeError(\"Unexpected data type (\" + _typeof(n) + \")\");\n",
              "      }\n",
              "      return o;\n",
              "    };\n",
              "  }\n",
              "  function q(e) {\n",
              "    e.fn ? e.fn.iFrameResize || (e.fn.iFrameResize = function (i) {\n",
              "      return this.filter(\"iframe\").each(function (e, n) {\n",
              "        s(n, i);\n",
              "      }).end();\n",
              "    }) : E(\"\", \"Unable to bind to jQuery, it is not fully loaded.\");\n",
              "  }\n",
              "  \"undefined\" != typeof window && (c = 0, a = u = !1, v = \"message\".length, I = (x = \"[iFrameSizer]\").length, M = null, r = window.requestAnimationFrame, f = Object.freeze({\n",
              "    max: 1,\n",
              "    scroll: 1,\n",
              "    bodyScroll: 1,\n",
              "    documentElementScroll: 1\n",
              "  }), k = {}, i = null, l = Object.freeze({\n",
              "    autoResize: !0,\n",
              "    bodyBackground: null,\n",
              "    bodyMargin: null,\n",
              "    bodyMarginV1: 8,\n",
              "    bodyPadding: null,\n",
              "    checkOrigin: !0,\n",
              "    inPageLinks: !1,\n",
              "    enablePublicMethods: !0,\n",
              "    heightCalculationMethod: \"bodyOffset\",\n",
              "    id: \"iFrameResizer\",\n",
              "    interval: 32,\n",
              "    log: !1,\n",
              "    maxHeight: 1 / 0,\n",
              "    maxWidth: 1 / 0,\n",
              "    minHeight: 0,\n",
              "    minWidth: 0,\n",
              "    mouseEvents: !0,\n",
              "    resizeFrom: \"parent\",\n",
              "    scrolling: !1,\n",
              "    sizeHeight: !0,\n",
              "    sizeWidth: !1,\n",
              "    warningTimeout: 5e3,\n",
              "    tolerance: 0,\n",
              "    widthCalculationMethod: \"scroll\",\n",
              "    onClose: function onClose() {\n",
              "      return !0;\n",
              "    },\n",
              "    onClosed: function onClosed() {},\n",
              "    onInit: function onInit() {},\n",
              "    onMessage: function onMessage() {\n",
              "      R(\"onMessage function not defined\");\n",
              "    },\n",
              "    onMouseEnter: function onMouseEnter() {},\n",
              "    onMouseLeave: function onMouseLeave() {},\n",
              "    onResized: function onResized() {},\n",
              "    onScroll: function onScroll() {\n",
              "      return !0;\n",
              "    }\n",
              "  }), z = {}, window.jQuery !== d && q(window.jQuery), \"function\" == typeof define && define.amd ? define([], n) : \"object\" == (typeof module === \"undefined\" ? \"undefined\" : _typeof(module)) && \"object\" == _typeof(module.exports) && (module.exports = n()), window.iFrameResize = window.iFrameResize || n());\n",
              "}();\n",
              "(function(){iFrameResize({ checkOrigin: false }, '#scan-132829531747136');})();\n",
              "</script>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Save the results to a html file\n",
        "scan_results.to_html(\"scan_results.html\")\n",
        "\n",
        "# Save the results to a dataframe\n",
        "results_df = scan_results.to_dataframe()"
      ],
      "metadata": {
        "id": "uPYt14MhOOsI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a test suite from the scan results\n",
        "test_suite = scan_results.generate_test_suite(name=\"My first test suite\")\n",
        "\n",
        "# You can run the test suite locally to verify that it reproduces the issues\n",
        "test_suite.run()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "2ko9Ar5DOV4h",
        "outputId": "2602aa51-0ff6-47f1-c0b1-39eee031adf8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (215, 20) executed in 0:00:00.020270\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (303, 20) executed in 0:00:00.020424\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (252, 20) executed in 0:00:00.032141\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (304, 20) executed in 0:00:00.023490\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (308, 20) executed in 0:00:00.021597\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Recall on data slice “`Contract` == \"One year\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005210>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.0\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`tenure` >= 44.500 AND `tenure` < 70.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8540760>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.06\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`PaymentMethod` == \"Bank transfer\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec904aad0>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.17\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`TotalCharges` >= 3485.375”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8fbeb30>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.17\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`OnlineSecurity` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff3970>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.2\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (370, 20) executed in 0:00:00.023094\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (308, 20) executed in 0:00:00.023470\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (217, 20) executed in 0:00:00.018502\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (302, 20) executed in 0:00:00.022352\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (424, 20) executed in 0:00:00.028456\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Recall on data slice “`InternetService` == \"DSL\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2fb0>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.24\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`TechSupport` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004940>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.26\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`PaymentMethod` == \"Credit card\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004520>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.32\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`Dependents` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2230>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.33\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (240, 20) executed in 0:00:00.029863\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Recall on data slice “`PaperlessBilling` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90054b0>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.35\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`PaymentMethod` == \"Mailed check\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005990>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.35\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (371, 20) executed in 0:00:00.213856\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (424, 20) executed in 0:00:00.036688\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (465, 20) executed in 0:00:00.037033\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (512, 20) executed in 0:00:00.036668\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Overconfidence on data slice “`OnlineBackup` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132860>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.36\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`PaperlessBilling` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec852b430>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.35\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`MultipleLines` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9130040>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.31\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`Partner` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec84ba740>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.3\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (370, 20) executed in 0:00:00.035118\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (528, 20) executed in 0:00:00.038014\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (594, 20) executed in 0:00:00.039688\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (537, 20) executed in 0:00:00.035150\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Overconfidence on data slice “`InternetService` == \"DSL\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132530>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.3\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`gender` == \"Female\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ec14e0>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.29\n",
            "               \n",
            "               \n",
            "Executed 'Underconfidence on data slice “`Contract` == \"Month-to-month\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908f460>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}: \n",
            "               Test failed\n",
            "               Metric: 0.04\n",
            "               \n",
            "               \n",
            "Executed 'Underconfidence on data slice “`OnlineSecurity` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90626b0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}: \n",
            "               Test failed\n",
            "               Metric: 0.04\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (537, 20) executed in 0:00:00.033835\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (753, 20) executed in 0:00:00.039720\n",
            "INFO:giskard.core.suite:Executed test suite 'My first test suite'\n",
            "INFO:giskard.core.suite:result: failed\n",
            "INFO:giskard.core.suite:Recall on data slice “`Contract` == \"One year\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005210>, 'threshold': 0.5163763066202091}): {failed, metric=0.0}\n",
            "INFO:giskard.core.suite:Recall on data slice “`tenure` >= 44.500 AND `tenure` < 70.500” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8540760>, 'threshold': 0.5163763066202091}): {failed, metric=0.0625}\n",
            "INFO:giskard.core.suite:Recall on data slice “`PaymentMethod` == \"Bank transfer\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec904aad0>, 'threshold': 0.5163763066202091}): {failed, metric=0.16666666666666666}\n",
            "INFO:giskard.core.suite:Recall on data slice “`TotalCharges` >= 3485.375” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8fbeb30>, 'threshold': 0.5163763066202091}): {failed, metric=0.17073170731707318}\n",
            "INFO:giskard.core.suite:Recall on data slice “`OnlineSecurity` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff3970>, 'threshold': 0.5163763066202091}): {failed, metric=0.19607843137254902}\n",
            "INFO:giskard.core.suite:Recall on data slice “`InternetService` == \"DSL\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2fb0>, 'threshold': 0.5163763066202091}): {failed, metric=0.23684210526315788}\n",
            "INFO:giskard.core.suite:Recall on data slice “`TechSupport` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004940>, 'threshold': 0.5163763066202091}): {failed, metric=0.2553191489361702}\n",
            "INFO:giskard.core.suite:Recall on data slice “`PaymentMethod` == \"Credit card\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004520>, 'threshold': 0.5163763066202091}): {failed, metric=0.3157894736842105}\n",
            "INFO:giskard.core.suite:Recall on data slice “`Dependents` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2230>, 'threshold': 0.5163763066202091}): {failed, metric=0.3269230769230769}\n",
            "INFO:giskard.core.suite:Recall on data slice “`PaperlessBilling` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90054b0>, 'threshold': 0.5163763066202091}): {failed, metric=0.34782608695652173}\n",
            "INFO:giskard.core.suite:Recall on data slice “`PaymentMethod` == \"Mailed check\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005990>, 'threshold': 0.5163763066202091}): {failed, metric=0.34782608695652173}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`OnlineBackup` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132860>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.3620689655172414}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`PaperlessBilling` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec852b430>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.35384615384615387}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`MultipleLines` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9130040>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.3058823529411765}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`Partner` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec84ba740>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.2987012987012987}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`InternetService` == \"DSL\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132530>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.29850746268656714}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`gender` == \"Female\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ec14e0>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.28846153846153844}\n",
            "INFO:giskard.core.suite:Underconfidence on data slice “`Contract` == \"Month-to-month\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908f460>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}): {failed, metric=0.04040404040404041}\n",
            "INFO:giskard.core.suite:Underconfidence on data slice “`OnlineSecurity` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90626b0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}): {failed, metric=0.037243947858473}\n",
            "INFO:giskard.core.suite:Underconfidence on data slice “`TechSupport` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908cc70>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}): {failed, metric=0.037243947858473}\n",
            "INFO:giskard.core.suite:Underconfidence on data slice “`Dependents` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9060bb0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}): {failed, metric=0.027888446215139442}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Underconfidence on data slice “`TechSupport` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908cc70>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}: \n",
            "               Test failed\n",
            "               Metric: 0.04\n",
            "               \n",
            "               \n",
            "Executed 'Underconfidence on data slice “`Dependents` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9060bb0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}: \n",
            "               Test failed\n",
            "               Metric: 0.03\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<TestSuiteResult (failed)>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    .alert {\n",
              "        padding: 16px;\n",
              "        border-radius: 4px;\n",
              "        display: flex;\n",
              "        align-items: center;\n",
              "        flex-wrap: wrap;\n",
              "        gap: 4px;\n",
              "        margin-bottom: 16px;\n",
              "    }\n",
              "\n",
              "    .test-result {\n",
              "        border-radius: 4px;\n",
              "        padding: 0 12px;\n",
              "        display: flex;\n",
              "        gap: 4px;\n",
              "        align-items: center;\n",
              "    }\n",
              "\n",
              "    .alert-error, .test-card-failed .test-result {\n",
              "        color: rgb(255, 82, 82);\n",
              "        background: rgb(243, 226, 226);\n",
              "        fill: currentcolor;\n",
              "    }\n",
              "\n",
              "    .test-card-error .test-result {\n",
              "        color: #856404;\n",
              "        background: #fff3cd;\n",
              "        fill: currentcolor;\n",
              "    }\n",
              "\n",
              "    .alert-success, .test-card-passed .test-result {\n",
              "        color: rgb(76, 175, 80);\n",
              "        background: rgb(226, 243, 226);\n",
              "        fill: currentcolor;\n",
              "    }\n",
              "\n",
              "    .alert svg {\n",
              "        height: 24px;\n",
              "        width: 24px;\n",
              "        border-radius: 50%;\n",
              "        background: rgba(0, 0, 0, 10%);\n",
              "    }\n",
              "\n",
              "    .test-result svg {\n",
              "        height: 16px;\n",
              "        width: 16px;\n",
              "    }\n",
              "\n",
              "    .card-container {\n",
              "        display: flex;\n",
              "        flex-direction: column;\n",
              "        gap: 16px;\n",
              "    }\n",
              "\n",
              "    .test-card {\n",
              "        border-radius: 4px;\n",
              "        border: 1px solid #e0e0e0;\n",
              "        color: rgb(98, 98, 98);\n",
              "        background: #fff;\n",
              "    }\n",
              "\n",
              "    .param {\n",
              "        display: flex;\n",
              "        flex-direction: column;\n",
              "    }\n",
              "\n",
              "    .test-card .test-name, .test-card .param-value {\n",
              "        color: #000\n",
              "    }\n",
              "\n",
              "    .test-card-row {\n",
              "        padding: 10px;\n",
              "        display: flex;\n",
              "        gap: 20px;\n",
              "        align-items: center;\n",
              "        flex-wrap: wrap;\n",
              "    }\n",
              "\n",
              "    .test-card-row:last-child {\n",
              "        border-top: 1px solid #dee2e6;\n",
              "    }\n",
              "\n",
              "    .spacer {\n",
              "        flex-grow: 1;\n",
              "    }\n",
              "\n",
              "    .test-card-failed .metric {\n",
              "        color: #b71c1c;\n",
              "    }\n",
              "</style>\n",
              "\n",
              "\n",
              "\n",
              "\n",
              "<div class='dark'>\n",
              "    <div id='gsk-suite'>\n",
              "        <div class=\"alert alert-error\">\n",
              "  \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" viewBox=\"0 0 24 24\">\n",
              "    <title>close</title>\n",
              "    <path\n",
              "      d=\"M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z\"\n",
              "    />\n",
              "  </svg>\n",
              "  <strong>Test suite failed.</strong>\n",
              "  \n",
              "</div>\n",
              "        <div class='card-container'>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`Contract` == &#34;One year&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.0</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`Contract` == &#34;One year&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`tenure` &gt;= 44.500 AND `tenure` &lt; 70.500”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.0625</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`tenure` &gt;= 44.500 AND `tenure` &lt; 70.500</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`PaymentMethod` == &#34;Bank transfer&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.16667</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`PaymentMethod` == &#34;Bank transfer&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`TotalCharges` &gt;= 3485.375”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.17073</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`TotalCharges` &gt;= 3485.375</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`OnlineSecurity` == &#34;Yes&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.19608</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`OnlineSecurity` == &#34;Yes&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`InternetService` == &#34;DSL&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.23684</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`InternetService` == &#34;DSL&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`TechSupport` == &#34;Yes&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.25532</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`TechSupport` == &#34;Yes&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`PaymentMethod` == &#34;Credit card&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.31579</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`PaymentMethod` == &#34;Credit card&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`Dependents` == &#34;Yes&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.32692</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`Dependents` == &#34;Yes&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`PaperlessBilling` == &#34;No&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.34783</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`PaperlessBilling` == &#34;No&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Recall on data slice “`PaymentMethod` == &#34;Mailed check&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.34783</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`PaymentMethod` == &#34;Mailed check&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.5163763066202091</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Overconfidence on data slice “`OnlineBackup` == &#34;Yes&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.36207</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`OnlineBackup` == &#34;Yes&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.2880952380952381</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.5</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Overconfidence on data slice “`PaperlessBilling` == &#34;No&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.35385</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`PaperlessBilling` == &#34;No&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.2880952380952381</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.5</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Overconfidence on data slice “`MultipleLines` == &#34;Yes&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.30588</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`MultipleLines` == &#34;Yes&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.2880952380952381</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.5</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Overconfidence on data slice “`Partner` == &#34;Yes&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.2987</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`Partner` == &#34;Yes&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.2880952380952381</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.5</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Overconfidence on data slice “`InternetService` == &#34;DSL&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.29851</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`InternetService` == &#34;DSL&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.2880952380952381</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.5</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Overconfidence on data slice “`gender` == &#34;Female&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.28846</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`gender` == &#34;Female&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.2880952380952381</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.5</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Underconfidence on data slice “`Contract` == &#34;Month-to-month&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.0404</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`Contract` == &#34;Month-to-month&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.025023696682464455</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.95</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Underconfidence on data slice “`OnlineSecurity` == &#34;No&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.03724</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`OnlineSecurity` == &#34;No&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.025023696682464455</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.95</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Underconfidence on data slice “`TechSupport` == &#34;No&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.03724</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`TechSupport` == &#34;No&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.025023696682464455</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.95</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "  <div\n",
              "    class='test-card test-card-failed'>\n",
              "    <div class='test-card-row'>\n",
              "      <span class='test-name'>Test Underconfidence on data slice “`Dependents` == &#34;No&#34;”</span>\n",
              "      <div class='spacer'></div>\n",
              "      \n",
              "      <span class='metric'>Measured <strong>Metric = 0.02789</strong></span>\n",
              "      \n",
              "      <span class='test-result'>\n",
              "      \n",
              "        <svg xmlns='http://www.w3.org/2000/svg' viewBox='0 0 24 24'><title>close</title>\n",
              "          <path\n",
              "            d='M19,6.41L17.59,5L12,10.59L6.41,5L5,6.41L10.59,12L5,17.59L6.41,19L12,13.41L17.59,19L19,17.59L13.41,12L19,6.41Z' />\n",
              "        </svg>\n",
              "        Failed\n",
              "      \n",
              "    </span>\n",
              "    </div>\n",
              "    <div class='test-card-row'>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>model</span>\n",
              "        <span class='param-value'>Churn classification</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>dataset</span>\n",
              "        <span class='param-value'>Churn classification dataset</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>slicing_function</span>\n",
              "        <span class='param-value'>`Dependents` == &#34;No&#34;</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>threshold</span>\n",
              "        <span class='param-value'>0.025023696682464455</span>\n",
              "      </div>\n",
              "      \n",
              "      <div class='param'>\n",
              "        <span>p_threshold</span>\n",
              "        <span class='param-value'>0.95</span>\n",
              "      </div>\n",
              "      \n",
              "    </div>\n",
              "  </div>\n",
              "  \n",
              "</div>\n",
              "    </div>\n",
              "</div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from giskard import testing\n",
        "\n",
        "# Add a test to the test suite\n",
        "test_suite = test_suite.add_test(testing.test_accuracy(wrapped_model, wrapped_data, threshold=0.80))\n",
        "\n",
        "# Run the test suite\n",
        "test_suite_results = test_suite.run()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0JJ6BVqVOZaO",
        "outputId": "f6fb4b21-8378-4427-8341-f2ad7edfb5aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (215, 20) executed in 0:00:00.054532\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (303, 20) executed in 0:00:00.056554\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (252, 20) executed in 0:00:00.026125\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (304, 20) executed in 0:00:00.029877\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Recall on data slice “`Contract` == \"One year\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005210>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.0\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`tenure` >= 44.500 AND `tenure` < 70.500”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8540760>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.06\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`PaymentMethod` == \"Bank transfer\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec904aad0>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.17\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (308, 20) executed in 0:00:00.021278\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (370, 20) executed in 0:00:00.022738\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (308, 20) executed in 0:00:00.023585\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (217, 20) executed in 0:00:00.021530\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Recall on data slice “`TotalCharges` >= 3485.375”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8fbeb30>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.17\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`OnlineSecurity` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff3970>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.2\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`InternetService` == \"DSL\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2fb0>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.24\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`TechSupport` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004940>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.26\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`PaymentMethod` == \"Credit card\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004520>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.32\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (302, 20) executed in 0:00:00.033732\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (424, 20) executed in 0:00:00.026068\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (240, 20) executed in 0:00:00.020933\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (371, 20) executed in 0:00:00.024293\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (424, 20) executed in 0:00:00.024472\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Recall on data slice “`Dependents` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2230>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.33\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`PaperlessBilling` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90054b0>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.35\n",
            "               \n",
            "               \n",
            "Executed 'Recall on data slice “`PaymentMethod` == \"Mailed check\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005990>, 'threshold': 0.5163763066202091}: \n",
            "               Test failed\n",
            "               Metric: 0.35\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`OnlineBackup` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132860>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.36\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`PaperlessBilling` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec852b430>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.35\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (465, 20) executed in 0:00:00.027330\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (512, 20) executed in 0:00:00.025007\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (370, 20) executed in 0:00:00.026288\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (528, 20) executed in 0:00:00.024383\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (594, 20) executed in 0:00:00.028006\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Overconfidence on data slice “`MultipleLines` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9130040>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.31\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`Partner` == \"Yes\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec84ba740>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.3\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`InternetService` == \"DSL\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132530>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.3\n",
            "               \n",
            "               \n",
            "Executed 'Overconfidence on data slice “`gender` == \"Female\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ec14e0>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}: \n",
            "               Test failed\n",
            "               Metric: 0.29\n",
            "               \n",
            "               \n",
            "Executed 'Underconfidence on data slice “`Contract` == \"Month-to-month\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908f460>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}: \n",
            "               Test failed\n",
            "               Metric: 0.04\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (537, 20) executed in 0:00:00.027447\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (537, 20) executed in 0:00:00.042397\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (753, 20) executed in 0:00:00.042490\n",
            "INFO:giskard.datasets.base:Casting dataframe columns from {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'} to {'gender': 'object', 'SeniorCitizen': 'object', 'Partner': 'object', 'Dependents': 'object', 'tenure': 'int64', 'PhoneService': 'object', 'MultipleLines': 'object', 'InternetService': 'object', 'OnlineSecurity': 'object', 'OnlineBackup': 'object', 'DeviceProtection': 'object', 'TechSupport': 'object', 'StreamingTV': 'object', 'StreamingMovies': 'object', 'Contract': 'object', 'PaperlessBilling': 'object', 'PaymentMethod': 'object', 'MonthlyCharges': 'float64', 'TotalCharges': 'float64'}\n",
            "INFO:giskard.utils.logging_utils:Predicted dataset with shape (1055, 20) executed in 0:00:00.026414\n",
            "INFO:giskard.core.suite:Executed test suite 'My first test suite'\n",
            "INFO:giskard.core.suite:result: failed\n",
            "INFO:giskard.core.suite:Recall on data slice “`Contract` == \"One year\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005210>, 'threshold': 0.5163763066202091}): {failed, metric=0.0}\n",
            "INFO:giskard.core.suite:Recall on data slice “`tenure` >= 44.500 AND `tenure` < 70.500” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8540760>, 'threshold': 0.5163763066202091}): {failed, metric=0.0625}\n",
            "INFO:giskard.core.suite:Recall on data slice “`PaymentMethod` == \"Bank transfer\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec904aad0>, 'threshold': 0.5163763066202091}): {failed, metric=0.16666666666666666}\n",
            "INFO:giskard.core.suite:Recall on data slice “`TotalCharges` >= 3485.375” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8fbeb30>, 'threshold': 0.5163763066202091}): {failed, metric=0.17073170731707318}\n",
            "INFO:giskard.core.suite:Recall on data slice “`OnlineSecurity` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff3970>, 'threshold': 0.5163763066202091}): {failed, metric=0.19607843137254902}\n",
            "INFO:giskard.core.suite:Recall on data slice “`InternetService` == \"DSL\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2fb0>, 'threshold': 0.5163763066202091}): {failed, metric=0.23684210526315788}\n",
            "INFO:giskard.core.suite:Recall on data slice “`TechSupport` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004940>, 'threshold': 0.5163763066202091}): {failed, metric=0.2553191489361702}\n",
            "INFO:giskard.core.suite:Recall on data slice “`PaymentMethod` == \"Credit card\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004520>, 'threshold': 0.5163763066202091}): {failed, metric=0.3157894736842105}\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Executed 'Underconfidence on data slice “`OnlineSecurity` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90626b0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}: \n",
            "               Test failed\n",
            "               Metric: 0.04\n",
            "               \n",
            "               \n",
            "Executed 'Underconfidence on data slice “`TechSupport` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908cc70>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}: \n",
            "               Test failed\n",
            "               Metric: 0.04\n",
            "               \n",
            "               \n",
            "Executed 'Underconfidence on data slice “`Dependents` == \"No\"”' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9060bb0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}: \n",
            "               Test failed\n",
            "               Metric: 0.03\n",
            "               \n",
            "               \n",
            "Executed 'Accuracy' with arguments {'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'threshold': 0.8}: \n",
            "               Test succeeded\n",
            "               Metric: 0.8\n",
            "               \n",
            "               \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:giskard.core.suite:Recall on data slice “`Dependents` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2230>, 'threshold': 0.5163763066202091}): {failed, metric=0.3269230769230769}\n",
            "INFO:giskard.core.suite:Recall on data slice “`PaperlessBilling` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90054b0>, 'threshold': 0.5163763066202091}): {failed, metric=0.34782608695652173}\n",
            "INFO:giskard.core.suite:Recall on data slice “`PaymentMethod` == \"Mailed check\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005990>, 'threshold': 0.5163763066202091}): {failed, metric=0.34782608695652173}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`OnlineBackup` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132860>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.3620689655172414}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`PaperlessBilling` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec852b430>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.35384615384615387}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`MultipleLines` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9130040>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.3058823529411765}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`Partner` == \"Yes\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec84ba740>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.2987012987012987}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`InternetService` == \"DSL\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132530>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.29850746268656714}\n",
            "INFO:giskard.core.suite:Overconfidence on data slice “`gender` == \"Female\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ec14e0>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}): {failed, metric=0.28846153846153844}\n",
            "INFO:giskard.core.suite:Underconfidence on data slice “`Contract` == \"Month-to-month\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908f460>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}): {failed, metric=0.04040404040404041}\n",
            "INFO:giskard.core.suite:Underconfidence on data slice “`OnlineSecurity` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90626b0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}): {failed, metric=0.037243947858473}\n",
            "INFO:giskard.core.suite:Underconfidence on data slice “`TechSupport` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908cc70>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}): {failed, metric=0.037243947858473}\n",
            "INFO:giskard.core.suite:Underconfidence on data slice “`Dependents` == \"No\"” ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9060bb0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}): {failed, metric=0.027888446215139442}\n",
            "INFO:giskard.core.suite:Accuracy ({'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'threshold': 0.8}): {passed, metric=0.8009478672985783}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Extract the values of the test suite results using the `results` attribute\n",
        "# The format of the results is a list of dictionaries\n",
        "test_suite_results.results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tpnBH0WbOdCf",
        "outputId": "003969df-9168-4a45-8af0-4611ba64dc1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[SuiteResult(test_name='Recall on data slice “`Contract` == \"One year\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.0\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005210>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005210>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005210>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`Contract` == \"One year\"”', display_name='Recall on data slice “`Contract` == \"One year\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`tenure` >= 44.500 AND `tenure` < 70.500”', result=\n",
              "                Test failed\n",
              "                Metric: 0.06\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8540760>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8540760>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8540760>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`tenure` >= 44.500 AND `tenure` < 70.500”', display_name='Recall on data slice “`tenure` >= 44.500 AND `tenure` < 70.500”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`PaymentMethod` == \"Bank transfer\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.17\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec904aad0>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec904aad0>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec904aad0>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`PaymentMethod` == \"Bank transfer\"”', display_name='Recall on data slice “`PaymentMethod` == \"Bank transfer\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`TotalCharges` >= 3485.375”', result=\n",
              "                Test failed\n",
              "                Metric: 0.17\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8fbeb30>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8fbeb30>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8fbeb30>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`TotalCharges` >= 3485.375”', display_name='Recall on data slice “`TotalCharges` >= 3485.375”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`OnlineSecurity` == \"Yes\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.2\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff3970>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff3970>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff3970>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`OnlineSecurity` == \"Yes\"”', display_name='Recall on data slice “`OnlineSecurity` == \"Yes\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`InternetService` == \"DSL\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.24\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2fb0>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2fb0>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2fb0>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`InternetService` == \"DSL\"”', display_name='Recall on data slice “`InternetService` == \"DSL\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`TechSupport` == \"Yes\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.26\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004940>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004940>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004940>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`TechSupport` == \"Yes\"”', display_name='Recall on data slice “`TechSupport` == \"Yes\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`PaymentMethod` == \"Credit card\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.32\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004520>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004520>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9004520>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`PaymentMethod` == \"Credit card\"”', display_name='Recall on data slice “`PaymentMethod` == \"Credit card\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`Dependents` == \"Yes\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.33\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2230>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2230>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ff2230>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`Dependents` == \"Yes\"”', display_name='Recall on data slice “`Dependents` == \"Yes\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`PaperlessBilling` == \"No\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.35\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90054b0>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90054b0>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90054b0>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`PaperlessBilling` == \"No\"”', display_name='Recall on data slice “`PaperlessBilling` == \"No\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Recall on data slice “`PaymentMethod` == \"Mailed check\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.35\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005990>, 'threshold': 0.5163763066202091}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005990>, 'threshold': 0.5163763066202091}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9005990>, 'threshold': 0.5163763066202091}, test_id='Recall on data slice “`PaymentMethod` == \"Mailed check\"”', display_name='Recall on data slice “`PaymentMethod` == \"Mailed check\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Overconfidence on data slice “`OnlineBackup` == \"Yes\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.36\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132860>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132860>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132860>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_id='Overconfidence on data slice “`OnlineBackup` == \"Yes\"”', display_name='Overconfidence on data slice “`OnlineBackup` == \"Yes\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Overconfidence on data slice “`PaperlessBilling` == \"No\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.35\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec852b430>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec852b430>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec852b430>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_id='Overconfidence on data slice “`PaperlessBilling` == \"No\"”', display_name='Overconfidence on data slice “`PaperlessBilling` == \"No\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Overconfidence on data slice “`MultipleLines` == \"Yes\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.31\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9130040>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9130040>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9130040>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_id='Overconfidence on data slice “`MultipleLines` == \"Yes\"”', display_name='Overconfidence on data slice “`MultipleLines` == \"Yes\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Overconfidence on data slice “`Partner` == \"Yes\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.3\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec84ba740>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec84ba740>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec84ba740>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_id='Overconfidence on data slice “`Partner` == \"Yes\"”', display_name='Overconfidence on data slice “`Partner` == \"Yes\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Overconfidence on data slice “`InternetService` == \"DSL\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.3\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132530>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132530>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9132530>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_id='Overconfidence on data slice “`InternetService` == \"DSL\"”', display_name='Overconfidence on data slice “`InternetService` == \"DSL\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Overconfidence on data slice “`gender` == \"Female\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.29\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ec14e0>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ec14e0>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec8ec14e0>, 'threshold': 0.2880952380952381, 'p_threshold': 0.5}, test_id='Overconfidence on data slice “`gender` == \"Female\"”', display_name='Overconfidence on data slice “`gender` == \"Female\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Underconfidence on data slice “`Contract` == \"Month-to-month\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.04\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908f460>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908f460>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908f460>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, test_id='Underconfidence on data slice “`Contract` == \"Month-to-month\"”', display_name='Underconfidence on data slice “`Contract` == \"Month-to-month\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Underconfidence on data slice “`OnlineSecurity` == \"No\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.04\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90626b0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90626b0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec90626b0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, test_id='Underconfidence on data slice “`OnlineSecurity` == \"No\"”', display_name='Underconfidence on data slice “`OnlineSecurity` == \"No\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Underconfidence on data slice “`TechSupport` == \"No\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.04\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908cc70>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908cc70>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec908cc70>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, test_id='Underconfidence on data slice “`TechSupport` == \"No\"”', display_name='Underconfidence on data slice “`TechSupport` == \"No\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Underconfidence on data slice “`Dependents` == \"No\"”', result=\n",
              "                Test failed\n",
              "                Metric: 0.03\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9060bb0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9060bb0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, provided_inputs={'slicing_function': <giskard.slicing.slice.QueryBasedSliceFunction object at 0x78cec9060bb0>, 'threshold': 0.025023696682464455, 'p_threshold': 0.95}, test_id='Underconfidence on data slice “`Dependents` == \"No\"”', display_name='Underconfidence on data slice “`Dependents` == \"No\"”', suite_test_id=None)),\n",
              " SuiteResult(test_name='Accuracy', result=\n",
              "                Test succeeded\n",
              "                Metric: 0.8\n",
              "                \n",
              "                , params={'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>, 'threshold': 0.8}, test_partial=TestPartial(giskard_test=To execute the test call \"execute()\" method\n",
              " Named inputs: {'threshold': 0.8, 'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>}, provided_inputs={'threshold': 0.8, 'model': <giskard.models.function.PredictionFunctionModel object at 0x78cec9a60a90>, 'dataset': <giskard.datasets.base.Dataset object at 0x78cec9b4ea70>}, test_id='Accuracy', display_name=None, suite_test_id=None))]"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Save the model\n",
        "import pickle\n",
        "\n",
        "with open(\"/content/model.pkl\", \"wb\") as f:\n",
        "    pickle.dump(pipeline, f)\n",
        "\n",
        "import os\n",
        "\n",
        "# Create directory if it doesn't exist\n",
        "os.makedirs('/content/data', exist_ok=True)\n",
        "\n",
        "# Save the dataset\n",
        "raw_data.to_csv('/content/data/validation_data.csv', index=False)\n",
        "\n"
      ],
      "metadata": {
        "id": "lAUfcBhLOiy5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile giskard_validation.py\n",
        "import os\n",
        "import re\n",
        "import pickle\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings\n",
        "import logging\n",
        "\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder\n",
        "from giskard import Dataset, Model, scan, testing\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Set up logging\n",
        "logging.basicConfig(\n",
        "    level=logging.INFO,\n",
        "    format=\"%(asctime)s [%(levelname)s] %(message)s\"\n",
        ")\n",
        "\n",
        "CATEGORICAL_COLUMNS = ['gender', 'SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod']\n",
        "NUMERIC_COLUMNS = ['tenure', 'MonthlyCharges', 'TotalCharges']\n",
        "\n",
        "# Load the validation dataset\n",
        "logging.info(\"Loading the validation dataset\")\n",
        "validation_df = pd.read_csv('data/validation_data.csv')\n",
        "validation_df[CATEGORICAL_COLUMNS] = validation_df[CATEGORICAL_COLUMNS].astype('object')\n",
        "FEATURES = [col for col in validation_df.columns if col != 'Churn']\n",
        "\n",
        "# Load the model\n",
        "logging.info(\"Loading the model\")\n",
        "with open(\"model/model.pkl\", \"rb\") as f:\n",
        "    pipeline = pickle.load(f)\n",
        "\n",
        "# Wrap the dataset with Giskard\n",
        "logging.info(\"Wrapping the dataset with Giskard\")\n",
        "wrapped_data = Dataset(\n",
        "    df = validation_df,  # A pandas.DataFrame that contains the raw data (before all the pre-processing steps) and the actual ground truth variable\n",
        "    target = 'Churn',  # Ground truth variable\n",
        "    name = \"Churn classification dataset\",  # Optional\n",
        "    cat_columns = CATEGORICAL_COLUMNS  # List of categorical columns. Optional, but is a MUST if available. Inferred automatically if not.\n",
        ")\n",
        "\n",
        "# Define the prediction function\n",
        "logging.info(\"Defining the prediction function\")\n",
        "def prediction_function(df: pd.DataFrame) -> np.ndarray:\n",
        "    return pipeline.predict_proba(df)\n",
        "\n",
        "# Wrap the model with Giskard\n",
        "logging.info(\"Wrapping the model with Giskard\")\n",
        "wrapped_model = Model(\n",
        "    model = prediction_function,                # A prediction function that encapsulates all the data pre-processing steps and that could be executed with the dataset used by the scan.\n",
        "    model_type = \"classification\",              # Either regression, classification or text_generation.\n",
        "    classification_labels = pipeline.classes_,  # Their order MUST be identical to the prediction_function's output order\n",
        "    name = \"Churn classification\",              # Name of the wrapped model [Optional]\n",
        "    feature_names = FEATURES,                   # Default: all columns of your dataset [Optional]\n",
        "    classification_threshold = 0.5              # Default: 0.5 [Optional]\n",
        ")\n",
        "\n",
        "# Scan the model\n",
        "logging.info(\"Scanning the model\")\n",
        "scan_results = scan(wrapped_model, wrapped_data)\n",
        "\n",
        "# Create a test suite from the scan results and add custom tests\n",
        "logging.info(\"Creating a test suite from the scan results and adding custom tests\")\n",
        "test_suite = scan_results.generate_test_suite(\"My first test suite\")\n",
        "test_suite = test_suite.add_test(testing.test_accuracy(wrapped_model, wrapped_data, threshold=0.75))\n",
        "test_suite_results = test_suite.run()\n",
        "\n",
        "if scan_results.has_issues():\n",
        "    print(\"Your model has vulnerabilities\")\n",
        "else:\n",
        "    print(\"Your model is safe\")\n",
        "\n",
        "# Extract the values of the test suite results using the `results` attribute\n",
        "logging.info(\"Extracting the values of the test suite results using the `results` attribute\")\n",
        "output = dict()\n",
        "for idx, test_result in enumerate(test_suite_results.results):\n",
        "    test_name = re.sub('\"|`|\"|\"', \"\", test_result[0])\n",
        "    output[test_name] = {\n",
        "        \"Status\": test_result[1].passed,\n",
        "        \"Threshold\": test_result[2][\"threshold\"],\n",
        "        \"Score\": test_result[1].metric,\n",
        "    }\n",
        "\n",
        "# To log the results to a pull request comment,\n",
        "# save the results as a GitHub environment variable\n",
        "logging.info(\"Saving the results as a GitHub environment variable\")\n",
        "import json\n",
        "with open(os.getenv(\"GITHUB_ENV\"), 'a') as fh:\n",
        "    fh.write(f'TEST_RESULT={json.dumps(output)}')"
      ],
      "metadata": {
        "id": "qQ6yCQdYOn0x",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1609d2b0-f55a-41da-bc58-6e3f77f32f82"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Writing giskard_validation.py\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p .github/workflows\n",
        "!touch .github/workflows/ci-cd.yml\n"
      ],
      "metadata": {
        "id": "YtUUW5oRkJVM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!ls -la"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yzcsAqQvkifQ",
        "outputId": "bba72ecf-678a-4c5b-fce4-ec000894c148"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 288\n",
            "drwxr-xr-x 1 root root   4096 Sep 20 03:57 .\n",
            "drwxr-xr-x 1 root root   4096 Sep 20 03:53 ..\n",
            "drwxr-xr-x 4 root root   4096 Sep 18 13:24 .config\n",
            "drwxr-xr-x 2 root root   4096 Sep 20 03:57 data\n",
            "-rw-r--r-- 1 root root   3899 Sep 20 03:57 giskard_validation.py\n",
            "drwxr-xr-x 3 root root   4096 Sep 20 03:57 .github\n",
            "-rw-r--r-- 1 root root   4105 Sep 20 03:57 model.pkl\n",
            "-rw-r--r-- 1 root root    197 Sep 20 03:55 Pipfile\n",
            "-rw-r--r-- 1 root root  82101 Sep 20 03:55 Pipfile.lock\n",
            "drwxr-xr-x 1 root root   4096 Sep 18 13:25 sample_data\n",
            "-rw-r--r-- 1 root root 164634 Sep 20 03:57 scan_results.html\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Define the content of the ci-cd.yml file\n",
        "ci_cd_content = '''\n",
        "name: Giskard-CI-CD\n",
        "\n",
        "on:\n",
        "  push:\n",
        "    paths:\n",
        "      - 'data/**'\n",
        "      - 'model/**'\n",
        "      - 'giskard_validation.py'\n",
        "      - 'README.md'\n",
        "    branches:\n",
        "      - main\n",
        "      - feature\n",
        "\n",
        "permissions:\n",
        "  contents: read\n",
        "\n",
        "jobs:\n",
        "  run-giskard-test-suite:\n",
        "    name: Giskard-Test-Suite\n",
        "    runs-on: ubuntu-latest\n",
        "    permissions:\n",
        "      pull-requests: write\n",
        "\n",
        "    steps:\n",
        "      - name: Checkout Code\n",
        "        uses: actions/checkout@v3\n",
        "\n",
        "      - name: Set up Python\n",
        "        uses: actions/setup-python@v4\n",
        "        with:\n",
        "          python-version: '3.9'\n",
        "\n",
        "      - name: Install dependencies\n",
        "        run: |\n",
        "          pip install --upgrade pip\n",
        "          pip install pipenv\n",
        "\n",
        "      - name: Install dependencies\n",
        "        working-directory: .\n",
        "        run: pipenv install --system --deploy\n",
        "\n",
        "      - name: Execute Giskard Test Suite\n",
        "        run: python giskard_validation.py\n",
        "\n",
        "      - name: Sanitize TEST_RESULT\n",
        "        run: |\n",
        "          # Safely replace Unicode characters in TEST_RESULT\n",
        "          TEST_RESULT_ESCAPED=$(echo \"${TEST_RESULT}\" | sed 's/\\\\u201c/\"/g' | sed 's/\\\\u201d/\"/g')\n",
        "          echo \"Sanitized TEST_RESULT: $TEST_RESULT_ESCAPED\"\n",
        "          # Export the sanitized value for further steps\n",
        "          echo \"TEST_RESULT_ESCAPED=$TEST_RESULT_ESCAPED\" >> $GITHUB_ENV\n",
        "\n",
        "      - name: PR comment\n",
        "        uses: actions/github-script@v6\n",
        "        with:\n",
        "          script: |\n",
        "            try {\n",
        "              const issue_number = context.payload.pull_request ? context.payload.pull_request.number : null;\n",
        "              if (issue_number) {\n",
        "                if (!process.env.TEST_RESULT_ESCAPED) {\n",
        "                  console.log(\"No TEST_RESULT_ESCAPED found.\");\n",
        "                  return;\n",
        "                }\n",
        "                let testResults;\n",
        "                try {\n",
        "                  testResults = JSON.stringify(JSON.parse(process.env.TEST_RESULT_ESCAPED), null, 2);\n",
        "                } catch (error) {\n",
        "                  console.log(\"Error parsing TEST_RESULT_ESCAPED:\", error.message);\n",
        "                  return;\n",
        "                }\n",
        "                github.rest.issues.createComment({\n",
        "                  issue_number: issue_number,\n",
        "                  owner: context.repo.owner,\n",
        "                  repo: context.repo.repo,\n",
        "                  body: `Test Suites Results:\\n\\n\\`\\`\\`json\\n${testResults}\\n\\`\\`\\``\n",
        "                });\n",
        "              } else {\n",
        "                console.log(\"No issue number found. This may not be a pull request event.\");\n",
        "              }\n",
        "            } catch (error) {\n",
        "              console.log(\"Error in PR comment action:\", error.message);\n",
        "            }\n",
        "'''\n",
        "\n",
        "# Write the content to the ci-cd.yml file\n",
        "with open('.github/workflows/ci-cd.yml', 'w') as file:\n",
        "    file.write(ci_cd_content)\n"
      ],
      "metadata": {
        "id": "ShC2o6o1kjM0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import shutil\n",
        "\n",
        "# Replace 'old_file_path' and 'new_folder_path' with the actual paths\n",
        "old_file_path = \"/content/model.pkl\"\n",
        "new_folder_path = \"/content/model\"\n",
        "\n",
        "shutil.move(old_file_path, new_folder_path)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "9ut8FRcDlnBq",
        "outputId": "1c17c735-3655-4088-e71d-663dbdea77e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content/model/model.pkl'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Initialize the git repository\n",
        "!git init\n",
        "\n",
        "# Step 2: Add the remote repository\n",
        "!git remote add origin https://github.com/toniramchandani1/CICDTestingAIApps.git\n",
        "\n",
        "# Step 3: Configure Git\n",
        "!git config --global user.email \"ramchandani.toni@example.com\"\n",
        "!git config --global user.name \"Toni Ramchandani\"\n",
        "\n",
        "# Step 4: Add the files to the staging area\n",
        "!git add .\n",
        "\n",
        "# Step 5: Commit the files\n",
        "!git commit -m \"Add CI/CD pipeline\"\n",
        "\n",
        "# Step 6: Create a new branch and switch to it\n",
        "!git checkout -b feature\n",
        "\n",
        "# Step 7: Push the files to GitHub\n",
        "import getpass\n",
        "token = getpass.getpass('Enter your GitHub Personal Access Token: ')\n",
        "!git push https://{token}@github.com/toniramchandani1/CICDTestingAIApps.git feature\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ke8lMIDNmIWE",
        "outputId": "de3f3be5-e5f3-44b2-ad3e-97cddc7e9bff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reinitialized existing Git repository in /content/.git/\n",
            "error: remote origin already exists.\n",
            "On branch feature\n",
            "nothing to commit, working tree clean\n",
            "fatal: A branch named 'feature' already exists.\n",
            "Enter your GitHub Personal Access Token: ··········\n",
            "Enumerating objects: 39, done.\n",
            "Counting objects: 100% (39/39), done.\n",
            "Delta compression using up to 2 threads\n",
            "Compressing objects: 100% (28/28), done.\n",
            "Writing objects: 100% (39/39), 8.50 MiB | 1.75 MiB/s, done.\n",
            "Total 39 (delta 5), reused 0 (delta 0), pack-reused 0\n",
            "remote: Resolving deltas: 100% (5/5), done.\u001b[K\n",
            "To https://github.com/toniramchandani1/CICDTestingAIApps.git\n",
            " * [new branch]      feature -> feature\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "g-DcNM0DmaYT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git add Pipfile Pipfile.lock\n",
        "!git commit -m \"Add Pipfile and Pipfile.lock for dependency management\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hFVE75E445-i",
        "outputId": "aa77a0a9-39df-482a-8180-e40e27bc46c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "On branch feature\n",
            "nothing to commit, working tree clean\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git fetch origin\n",
        "!git checkout feature"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jGGo9lCT500T",
        "outputId": "69d6b713-65df-45e1-ce0b-dbcd812bb7d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Already on 'feature'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git merge origin/feature\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OhsydXTs57Vf",
        "outputId": "d64794af-24f6-4844-edf3-5eb9506e80cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "merge: origin/feature2 - not something we can merge\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git add .\n",
        "!git commit -m \"Piplock file\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCxOAvLG6DB9",
        "outputId": "ce968bc1-3c36-4ee1-dfec-4bed17b22bfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "On branch feature\n",
            "nothing to commit, working tree clean\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "token = getpass.getpass('Enter your GitHub Personal Access Token: ')\n",
        "!git push https://{token}@github.com/toniramchandani1/CICDAITesting.git feature\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f0Jr4vzA5E3H",
        "outputId": "79e3cd99-c434-4986-b7f0-47c74400d170"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter your GitHub Personal Access Token: ··········\n",
            "To https://github.com/toniramchandani1/CICDAITesting.git\n",
            " \u001b[31m! [rejected]       \u001b[m feature -> feature (fetch first)\n",
            "\u001b[31merror: failed to push some refs to 'https://github.com/toniramchandani1/CICDAITesting.git'\n",
            "\u001b[m\u001b[33mhint: Updates were rejected because the remote contains work that you do\u001b[m\n",
            "\u001b[33mhint: not have locally. This is usually caused by another repository pushing\u001b[m\n",
            "\u001b[33mhint: to the same ref. You may want to first integrate the remote changes\u001b[m\n",
            "\u001b[33mhint: (e.g., 'git pull ...') before pushing again.\u001b[m\n",
            "\u001b[33mhint: See the 'Note about fast-forwards' in 'git push --help' for details.\u001b[m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "\n",
        "# Get your GitHub Personal Access Token securely (don't copy this line)\n",
        "token = getpass.getpass(\"Enter your GitHub Personal Access Token: \")\n",
        "\n",
        "# Fetch remote changes before pushing\n",
        "!git fetch origin\n",
        "\n",
        "# Integrate remote changes (choose either merge or rebase)\n",
        "# Option 1: Merge (may cause conflicts)\n",
        "#!git checkout feature\n",
        "#!git merge origin/feature\n",
        "\n",
        "# Option 2: Rebase (cleaner but rewrites history)\n",
        "!git checkout feature\n",
        "!git rebase origin/feature\n",
        "\n",
        "# Push your changes after integrating remote updates\n",
        "!git push https://{token}@github.com/toniramchandani1/CICDTestingAIApps.git feature\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rn-WHT8eqgfL",
        "outputId": "f955d882-d779-4a56-8def-983bb10252f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter your GitHub Personal Access Token: ··········\n",
            "Already on 'feature'\n",
            "Current branch feature is up to date.\n",
            "Everything up-to-date\n"
          ]
        }
      ]
    }
  ]
}