credit-scoring-boosting-algorithms.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.7.6","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 5GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-07-11T19:43:34.975424Z","iopub.execute_input":"2023-07-11T19:43:34.976019Z","iopub.status.idle":"2023-07-11T19:43:34.986970Z","shell.execute_reply.started":"2023-07-11T19:43:34.975962Z","shell.execute_reply":"2023-07-11T19:43:34.986000Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/bank-loan-modelling/Bank_Personal_Loan_Modelling.xlsx\n","output_type":"stream"}]},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nfrom xgboost import XGBClassifier\nfrom xgboost import plot_importance  \nfrom sklearn.ensemble import AdaBoostClassifier, GradientBoostingClassifier\n\n# Selection model\nfrom sklearn.model_selection import train_test_split, KFold, cross_val_score,GridSearchCV,StratifiedKFold \n\n# Metrics\nfrom sklearn.metrics import accuracy_score, roc_auc_score,f1_score, confusion_matrix, classification_report\n\n# Visualization\nimport matplotlib.pyplot as plt\n%matplotlib inline","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:43:34.988911Z","iopub.execute_input":"2023-07-11T19:43:34.989607Z","iopub.status.idle":"2023-07-11T19:43:36.803217Z","shell.execute_reply.started":"2023-07-11T19:43:34.989558Z","shell.execute_reply":"2023-07-11T19:43:36.802226Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import roc_curve, auc\n\n# %% Function for plotting ROC_AUC curve\n\ndef plot_roc_auc(y_test, preds):\n    '''\n    Takes actual and predicted(probabilities) as input and plots the Receiver\n    Operating Characteristic (ROC) curve\n    '''\n    fpr, tpr, threshold = roc_curve(y_test, preds)\n    roc_auc = auc(fpr, tpr)\n    plt.title('Receiver Operating Characteristic')\n    plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n    plt.legend(loc = 'lower right')\n    plt.plot([0, 1], [0, 1],'r--')\n    plt.xlim([0, 1])\n    plt.ylim([0, 1])\n    plt.ylabel('True Positive Rate')\n    plt.xlabel('False Positive Rate')\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:43:36.806100Z","iopub.execute_input":"2023-07-11T19:43:36.806675Z","iopub.status.idle":"2023-07-11T19:43:36.817089Z","shell.execute_reply.started":"2023-07-11T19:43:36.806632Z","shell.execute_reply":"2023-07-11T19:43:36.815994Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"def evalua(y_pred,y_test):\n    \n    # Evaluate of predictions \n    accuracy = accuracy_score(y_test, y_pred) \n    roc = roc_auc_score(y_test, y_pred)\n    f1=f1_score(y_test, y_pred)\n\n    # Data test results\n    print('Evaluation of predictions: \\n')\n    print(\"Accuracy: %.2f%%\" % (accuracy * 100.0))\n    print(\"Area ROC: %.2f%%\" % (roc * 100.0))\n    print(\"F1 Score: %.2f%%\" % (f1 * 100.0))","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:43:36.820828Z","iopub.execute_input":"2023-07-11T19:43:36.821158Z","iopub.status.idle":"2023-07-11T19:43:36.832173Z","shell.execute_reply.started":"2023-07-11T19:43:36.821128Z","shell.execute_reply":"2023-07-11T19:43:36.828854Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"original = pd.read_excel('../input/bank-loan-modelling/Bank_Personal_Loan_Modelling.xlsx',\"Data\")","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","execution":{"iopub.status.busy":"2023-07-11T19:43:36.833608Z","iopub.execute_input":"2023-07-11T19:43:36.833987Z","iopub.status.idle":"2023-07-11T19:43:38.267989Z","shell.execute_reply.started":"2023-07-11T19:43:36.833946Z","shell.execute_reply":"2023-07-11T19:43:38.267045Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"markdown","source":"## Features are detailed below:\n\n* Age: Customer's age\n* Experience: Number of years of professional experience\n* Income: Annual income of the customer\n* ZIPCode: Home Address ZIP code\n* Family: Family size of the customer\n* CCAvg: Average spending on credit cards per month\n* Education: 1: Undergrade; 2: Graduate; 3: Advanced/Professional\n* Mortgage: Value of house mortgage if any\n* Securities Account: Does the customer have a securities account with the bank?\n* CD Account: Does the customer have a certificate of deposit (CD) account with the bank?\n* Online: Does the customer use internet banking facilities?\n* CreditCard: Does the customer uses a credit card issued by UniversalBank?\n* Personal Loan: Did this customer accept the personal loan offered in the last campaign?","metadata":{}},{"cell_type":"code","source":"original.head()","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:43:38.269521Z","iopub.execute_input":"2023-07-11T19:43:38.269865Z","iopub.status.idle":"2023-07-11T19:43:38.292809Z","shell.execute_reply.started":"2023-07-11T19:43:38.269828Z","shell.execute_reply":"2023-07-11T19:43:38.291764Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"   ID  Age  Experience  Income  ZIP Code  Family  CCAvg  Education  Mortgage  \\\n0   1   25           1      49     91107       4    1.6          1         0   \n1   2   45          19      34     90089       3    1.5          1         0   \n2   3   39          15      11     94720       1    1.0          1         0   \n3   4   35           9     100     94112       1    2.7          2         0   \n4   5   35           8      45     91330       4    1.0          2         0   \n\n   Personal Loan  Securities Account  CD Account  Online  CreditCard  \n0              0                   1           0       0           0  \n1              0                   1           0       0           0  \n2              0                   0           0       0           0  \n3              0                   0           0       0           0  \n4              0                   0           0       0           1  ","text/html":"<div>\n<style scoped>\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</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>ID</th>\n      <th>Age</th>\n      <th>Experience</th>\n      <th>Income</th>\n      <th>ZIP Code</th>\n      <th>Family</th>\n      <th>CCAvg</th>\n      <th>Education</th>\n      <th>Mortgage</th>\n      <th>Personal Loan</th>\n      <th>Securities Account</th>\n      <th>CD Account</th>\n      <th>Online</th>\n      <th>CreditCard</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>25</td>\n      <td>1</td>\n      <td>49</td>\n      <td>91107</td>\n      <td>4</td>\n      <td>1.6</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>45</td>\n      <td>19</td>\n      <td>34</td>\n      <td>90089</td>\n      <td>3</td>\n      <td>1.5</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>39</td>\n      <td>15</td>\n      <td>11</td>\n      <td>94720</td>\n      <td>1</td>\n      <td>1.0</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>35</td>\n      <td>9</td>\n      <td>100</td>\n      <td>94112</td>\n      <td>1</td>\n      <td>2.7</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>35</td>\n      <td>8</td>\n      <td>45</td>\n      <td>91330</td>\n      <td>4</td>\n      <td>1.0</td>\n      <td>2</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"original.info()","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:43:38.294397Z","iopub.execute_input":"2023-07-11T19:43:38.294790Z","iopub.status.idle":"2023-07-11T19:43:38.311080Z","shell.execute_reply.started":"2023-07-11T19:43:38.294753Z","shell.execute_reply":"2023-07-11T19:43:38.310187Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 5000 entries, 0 to 4999\nData columns (total 14 columns):\n #   Column              Non-Null Count  Dtype  \n---  ------              --------------  -----  \n 0   ID                  5000 non-null   int64  \n 1   Age                 5000 non-null   int64  \n 2   Experience          5000 non-null   int64  \n 3   Income              5000 non-null   int64  \n 4   ZIP Code            5000 non-null   int64  \n 5   Family              5000 non-null   int64  \n 6   CCAvg               5000 non-null   float64\n 7   Education           5000 non-null   int64  \n 8   Mortgage            5000 non-null   int64  \n 9   Personal Loan       5000 non-null   int64  \n 10  Securities Account  5000 non-null   int64  \n 11  CD Account          5000 non-null   int64  \n 12  Online              5000 non-null   int64  \n 13  CreditCard          5000 non-null   int64  \ndtypes: float64(1), int64(13)\nmemory usage: 547.0 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df = original\ndf.drop(['ID'],axis = 1, inplace = True)\nx = df.drop(['Personal Loan'], axis = 1)\ny = df['Personal Loan']","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:43:38.312799Z","iopub.execute_input":"2023-07-11T19:43:38.313202Z","iopub.status.idle":"2023-07-11T19:43:38.322440Z","shell.execute_reply.started":"2023-07-11T19:43:38.313161Z","shell.execute_reply":"2023-07-11T19:43:38.321537Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:43:38.324402Z","iopub.execute_input":"2023-07-11T19:43:38.324829Z","iopub.status.idle":"2023-07-11T19:43:38.336438Z","shell.execute_reply.started":"2023-07-11T19:43:38.324763Z","shell.execute_reply":"2023-07-11T19:43:38.335702Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"# Grid Search \nmodel = XGBClassifier() \n\nlearning_rate = [0.01, 0.1, 0.2] \nmax_depth=[6]\nbooster=['gbtree']\nn_estimators=[100]\nnthread=[6]\nmin_child_weight=[1,5,10,20,30]\n\nparam_grid = dict(min_child_weight=min_child_weight,nthread=nthread,learning_rate=learning_rate,max_depth=max_depth,booster=booster,n_estimators=n_estimators)\nkfold = StratifiedKFold(n_splits=5, shuffle=True, random_state=7) \ngrid_search = GridSearchCV(model, param_grid, scoring=\"accuracy\", n_jobs = 10, cv=kfold) \ngrid_result = grid_search.fit(x_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:43:38.338206Z","iopub.execute_input":"2023-07-11T19:43:38.338676Z","iopub.status.idle":"2023-07-11T19:44:41.710326Z","shell.execute_reply.started":"2023-07-11T19:43:38.338615Z","shell.execute_reply":"2023-07-11T19:44:41.706703Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"#Adaboost\nadaboost = AdaBoostClassifier(n_estimators=100, learning_rate=0.1)\n# Train the AdaBoost classifier\nadaboost.fit(x_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:41.713600Z","iopub.execute_input":"2023-07-11T19:44:41.717275Z","iopub.status.idle":"2023-07-11T19:44:42.237580Z","shell.execute_reply.started":"2023-07-11T19:44:41.717212Z","shell.execute_reply":"2023-07-11T19:44:42.236566Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"AdaBoostClassifier(learning_rate=0.1, n_estimators=100)"},"metadata":{}}]},{"cell_type":"code","source":"# Create the Gradient Boost classifier\ngradientboost = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)\n# Train the Gradient Boost classifier\ngradientboost.fit(x_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:42.241553Z","iopub.execute_input":"2023-07-11T19:44:42.241837Z","iopub.status.idle":"2023-07-11T19:44:42.680350Z","shell.execute_reply.started":"2023-07-11T19:44:42.241808Z","shell.execute_reply":"2023-07-11T19:44:42.679402Z"},"trusted":true},"execution_count":12,"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"GradientBoostingClassifier()"},"metadata":{}}]},{"cell_type":"code","source":"# Summarize results   \nprint(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\nmeans = grid_result.cv_results_['mean_test_score'] \nstds = grid_result.cv_results_['std_test_score'] \nparams = grid_result.cv_results_['params']","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:42.681955Z","iopub.execute_input":"2023-07-11T19:44:42.682333Z","iopub.status.idle":"2023-07-11T19:44:42.688666Z","shell.execute_reply.started":"2023-07-11T19:44:42.682295Z","shell.execute_reply":"2023-07-11T19:44:42.687579Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Best: 0.984776 using {'booster': 'gbtree', 'learning_rate': 0.2, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100, 'nthread': 6}\n","output_type":"stream"}]},{"cell_type":"code","source":"# Evaluate of predictions \nmodel_best = grid_search.best_estimator_\ny_pred=model_best.predict(x_test)\nevalua(y_pred,y_test)\n\n# Plot feature importance \nplot_importance(model_best) \nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:42.690281Z","iopub.execute_input":"2023-07-11T19:44:42.690749Z","iopub.status.idle":"2023-07-11T19:44:43.092331Z","shell.execute_reply.started":"2023-07-11T19:44:42.690706Z","shell.execute_reply":"2023-07-11T19:44:43.091428Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"Evaluation of predictions: \n\nAccuracy: 98.55%\nArea ROC: 95.15%\nF1 Score: 91.67%\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"#Adaboost\n#adaboost = AdaBoostClassifier(n_estimators=100, learning_rate=0.1)\n# Train the AdaBoost classifier\n#adaboost.fit(x_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.093723Z","iopub.execute_input":"2023-07-11T19:44:43.094079Z","iopub.status.idle":"2023-07-11T19:44:43.098518Z","shell.execute_reply.started":"2023-07-11T19:44:43.094041Z","shell.execute_reply":"2023-07-11T19:44:43.097521Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"y_pred1=adaboost.predict(x_test)\nevalua(y_pred1,y_test)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.099881Z","iopub.execute_input":"2023-07-11T19:44:43.100464Z","iopub.status.idle":"2023-07-11T19:44:43.155502Z","shell.execute_reply.started":"2023-07-11T19:44:43.100412Z","shell.execute_reply":"2023-07-11T19:44:43.154600Z"},"trusted":true},"execution_count":16,"outputs":[{"name":"stdout","text":"Evaluation of predictions: \n\nAccuracy: 96.42%\nArea ROC: 83.39%\nF1 Score: 76.86%\n","output_type":"stream"}]},{"cell_type":"code","source":"# Create the Gradient Boost classifier\n#gradientboost = GradientBoostingClassifier(n_estimators=100, learning_rate=0.1)\n# Train the Gradient Boost classifier\n#gradientboost.fit(x_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.156863Z","iopub.execute_input":"2023-07-11T19:44:43.157396Z","iopub.status.idle":"2023-07-11T19:44:43.161424Z","shell.execute_reply.started":"2023-07-11T19:44:43.157354Z","shell.execute_reply":"2023-07-11T19:44:43.160464Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"y_pred2=gradientboost.predict(x_test)\nevalua(y_pred2,y_test)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.162911Z","iopub.execute_input":"2023-07-11T19:44:43.163515Z","iopub.status.idle":"2023-07-11T19:44:43.181102Z","shell.execute_reply.started":"2023-07-11T19:44:43.163462Z","shell.execute_reply":"2023-07-11T19:44:43.179618Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Evaluation of predictions: \n\nAccuracy: 98.42%\nArea ROC: 94.77%\nF1 Score: 90.97%\n","output_type":"stream"}]},{"cell_type":"code","source":"# Classification report xgboost\nprint('Classification Report:\\n')\nprint(classification_report(y_test, y_pred),'\\n')","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.184055Z","iopub.execute_input":"2023-07-11T19:44:43.184327Z","iopub.status.idle":"2023-07-11T19:44:43.198115Z","shell.execute_reply.started":"2023-07-11T19:44:43.184299Z","shell.execute_reply":"2023-07-11T19:44:43.197098Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"Classification Report:\n\n              precision    recall  f1-score   support\n\n           0       0.99      0.99      0.99      1505\n           1       0.92      0.91      0.92       145\n\n    accuracy                           0.99      1650\n   macro avg       0.96      0.95      0.95      1650\nweighted avg       0.99      0.99      0.99      1650\n \n\n","output_type":"stream"}]},{"cell_type":"code","source":"# Classification report for adaboost\nprint('Classification Report:\\n')\nprint(classification_report(y_test, y_pred1),'\\n')","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.199634Z","iopub.execute_input":"2023-07-11T19:44:43.200215Z","iopub.status.idle":"2023-07-11T19:44:43.213671Z","shell.execute_reply.started":"2023-07-11T19:44:43.200176Z","shell.execute_reply":"2023-07-11T19:44:43.211917Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Classification Report:\n\n              precision    recall  f1-score   support\n\n           0       0.97      0.99      0.98      1505\n           1       0.89      0.68      0.77       145\n\n    accuracy                           0.96      1650\n   macro avg       0.93      0.83      0.87      1650\nweighted avg       0.96      0.96      0.96      1650\n \n\n","output_type":"stream"}]},{"cell_type":"code","source":"# Classification report for gradientboost\nprint('Classification Report:\\n')\nprint(classification_report(y_test, y_pred2),'\\n')","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.214823Z","iopub.execute_input":"2023-07-11T19:44:43.215081Z","iopub.status.idle":"2023-07-11T19:44:43.233288Z","shell.execute_reply.started":"2023-07-11T19:44:43.215055Z","shell.execute_reply":"2023-07-11T19:44:43.232628Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"Classification Report:\n\n              precision    recall  f1-score   support\n\n           0       0.99      0.99      0.99      1505\n           1       0.92      0.90      0.91       145\n\n    accuracy                           0.98      1650\n   macro avg       0.95      0.95      0.95      1650\nweighted avg       0.98      0.98      0.98      1650\n \n\n","output_type":"stream"}]},{"cell_type":"code","source":"# Confusion matrix\ncm =confusion_matrix(y_test,y_pred)\n\nplt.clf()\nplt.imshow(cm, interpolation='nearest', cmap=plt.cm.Wistia)\nclassNames = ['Negative','Positive']\nplt.title('Confusion Matrix - Test Data')\nplt.ylabel('True label')\nplt.xlabel('Predicted label')\ntick_marks = np.arange(len(classNames))\nplt.xticks(tick_marks, classNames, rotation=45)\nplt.yticks(tick_marks, classNames)\ns = [['TN','FP'], ['FN', 'TP']]\n \nfor i in range(2):\n    for j in range(2):\n        plt.text(j,i, str(s[i][j])+\" = \"+str(cm[i][j]))\nplt.show()\n\nplot_roc_auc(y_test, y_pred)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.235250Z","iopub.execute_input":"2023-07-11T19:44:43.235794Z","iopub.status.idle":"2023-07-11T19:44:43.473068Z","shell.execute_reply.started":"2023-07-11T19:44:43.235755Z","shell.execute_reply":"2023-07-11T19:44:43.472212Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAASkAAAEzCAYAAABt1PV/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3deZyd493H8c83iewkJClRQtQaSzzEFhKxBUEtDfo0be1bLW0t5WmVoFVdaK0tpUKpKkUtIVGaogiJJQitKooIEkF2WX7PH9d1kpPJzGSSWc49me/79ZrXnHs59/07c+Z8z3Vf96aIwMysqFpVugAzs9o4pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIdUCSOog6T5Jn0q6ox7LGSZpdEPWVgmSHpR0RKXrsLpxSBWIpK9JGidphqT384dplwZY9FBgTaBbRBy6oguJiFsjYnAD1LMESYMkhaS7qozvm8ePqeNyhku6ZVnzRcS+EXHTCpZb07oH5PdthqSZue4ZZT+9VmCZIWnDWqYfKWlB2TrelHSjpI2XYx0jJP1oeWtrSg6pgpB0OvAr4GJSoPQCrgEObIDFrwf8KyLmN8CyGstHQH9J3crGHQH8q6FWoKRR/ucj4vGI6BwRnYHN8+iupXER8d/GWC/wVF5nF2BPYDYwXtIWjbS+phcR/qnwD+kfbAZwaC3ztCOF2KT88yugXZ42CHgXOAP4EHgfOCpPuwD4HJiX13EMMBy4pWzZ6wMBtMnDRwL/AaYDbwLDysY/Ufa8/sCzwKf5d/+yaWOAi4B/5OWMBrrX8NpK9f8GODmPa53HnQeMKZv3cuAd4DNgPDAgj9+nyut8sayOH+c6ZgMb5nHH5um/Bu4sW/5PgUcA1eP9rPr37ALckN+X94AfAa3ztA2Bv+e/4RTg9jz+sbyMmfn1HF7NepZ4P8rG31/lNd0BTM7reAzYPI8/Pv+9Ps/ruC+PPwd4I79vE4GDK/r5qPQH1D+LPmDzS//UNcxzIfA08AWgB/AkcFGeNig//0JgFWAIMAtYPU8fzpKhVHV40YcK6JQDYJM8rWfZP/WiDwWwBjAN+EZ+3v/m4W55+pj8j74x0CEPX1LDaxtECqT+wNg8bggwCjiWJUPq60C3vM4z8oevfXWvq6yO/5JaN23y32cMi0OqI6m1diQwgBQU69Tz/Vz098zD9wDX5r/tF4BngBPytNuAH5C2atoDu5QtJ4ANa1nPovejyvijgQ+qDK/K4i+6F8qmjQB+VOX5hwJr55oOJwVlz0p9Pry5VwzdgClR++bYMODCiPgwIj4itZC+UTZ9Xp4+LyJGkr4ZN1nBehYCW0jqEBHvR8Qr1cyzH/B6RPw+IuZHxG3Aa8ABZfPcGBH/iojZwJ+ArWtbaUQ8CawhaRPgm8DN1cxzS0RMzeu8lPTBW9brHBERr+TnzKuyvFmk4LsMuAU4NSLeXcby6kzSmsC+wHciYmZEfAj8EvhqnmUeaXN87YiYExFPNMBqJ5G+RACIiN9FxPSImEsK8r6SutT05Ii4IyImRcTCiLgdeB3YvgHqWiEOqWKYCnSX1KaWedYG3i4bfjuPW7SMKiE3C+i8vIVExEzSt+eJwPuSHpC0aR3qKdX0xbLhyStQz++BU4DdgLurTpR0hqRX857KT0ibUt2Xscx3apsYEc+QNm9FCtNqSXqlrJN6wDLWWbIeqfX2vqRPcs3XklpUAN/L630mL//oOi63Nl8EPs41t5Z0iaQ3JH0GvJXnqfFvJumbkl4oq3eL2uZvbA6pYngKmAMcVMs8k0j/8CW98rgVMZO0mVOyVvnEiBgVEXuRNvVeA35bh3pKNb23gjWV/B74FjAyt3IWycFwNnAYaVO2K6mfRaXSa1hmrZf6kHQyqUU2iRQa1S8kYvNY3BH+eF1eDCkg55L647rmn9UiYvO8zMkRcVxErA2cAFxT2x69OjoYKNX3NdLOlz1Jgb5+Hl/t30zSeqT3+xTSpntX4OWy+ZucQ6oAIuJTUgfx1ZIOktRR0iqS9pX0szzbbcC5knpI6p7nX+bu9hq8AAyU1Cs3+/+vNEHSmpK+LKkT6cM1A1hQzTJGAhvnwybaSDoc6EPqtF1hEfEmsCupn6aqVUl9bx8BbSSdB6xWNv0DYP3l2YOXd9f/iLTJ9w3ge5Jq3SxdHhHxPmmnwaWSVpPUStKXJO2a13+opHXy7NNIoVH6e38AbFDH19FaUm9JV5L6+C7Ik1YlvY9TSV9MF1d5atV1dMo1fJSXexSpJVUxDqmCiIjLgNOBc0n/IO+Qvs3uybP8CBgHTABeAp7L41ZkXQ8Dt+dljWfJYGlF6pCeRNpk2JXUsqm6jKnA/nneqaQWyP4RMWVFaqqy7CciorpW4ijgQVJH99uk1mf5plzpQNWpkp5b1nry5vUtwE8j4sWIeB34PvB7Se3q8xqq+CbQlrSnbBpwJ6mVCrAdMFbSDOBe4Ns5qCH1H92UN7sOq2HZO+XnfkbaIbAasF1EvJSn30z6W72X1/90leffAPTJ67gnIiYCl5Ja9x8AW5L2jFaMcm++mVkhuSVlZoXmkDKzQnNImVmhOaTMrNAcUmZWaLUd4WxZ946K9btWugpbHvPWXmvZM1mhTBg/eUpE9Kg63iFVB+t3hbHHV7oKWx6ThzfE2SXWlNbRxVVPswK8uWdmBeeQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZobSpdgFXO1Fkw+Ob0ePIMaN0KenRMwy9+AN/ZEX6xdxq+9EmY8TmcP6h+6zz3EbhlAkybDZ9+f+npf54Ih98BTx8H/dZO4855GB58PT3+wUA4bIsln/PtkTDiheqXtzLq1fonbLplj0XDN9wzlHfe+pRjDryTXht0Ze6c+Xz5q304/fwB9VrP/Xe8ymXDH+f1V6dw/zNH0bdfTwCmTZ3F8UPv4sVn3+fQI7fix1ftXa/1LItDqgXr1hHGn5geXzAGOreFM/qn4U4/gnteg3MGQPeODbfO/TeBk7eHTa9cetr0uXDlWNj+i4vHPfAveH5yqnPufNh9BOyzEazWLk0fNwk+mdtw9TUH7Tu0YfQLxy4x7p23PmX7Aety0/2HMWvm5wze+gb23H9Dttq25wqvZ5MtevDbu77C2Sc8uMT4du3bcNZFu/LPlz/itZc/WuHl15U396xabVrBsdvAr55q2OXuuA70XLX6aef/Dc7cGdqXfXW++hEMXC/V06ktbLUmjPp3mrZgIZz9MFyyZ8PW2Nx17NSWrbZdi7ffmFav5Wy0WXe+tEm3ape//S7r0q5907Rx3JKyGn1re/ifX8NZO9c8z9/ehDNHLT2+wyrwxDF1X9fz78M7n8H+G8NlTy4ev9VacNHf4bs7wax5MOYt2Cxv6Vz9DBywcc2ht7KaM3s+g7e+HoB1e3flhruHLjF92tRZPPf0JL79w12WGD9j+lwOGfD7apd51R8OZOM+PaqdVmmNFlKSArgsIs7Iw2cCnSNieAOv5/sRcXHZ8JMR0b8h19FSrdYOvt43bYJ1WKX6eXbrvXiTcUUtDDhjFPzuoKWnDf4SjHsPBtwA3TvBjuumVtWk6XDnRHj0yPqtuzmqbnMP4JnH32Hv/7mBVq3EyefsxCabLxk6nVdtV+3ziq4xW1JzgUMk/SQipjTier4PLAopB1TD+vaOsN21cMTW1U9viJbU9Lnwyoewx4g0PHkGHHwb3P2/qfP8+wPTD8DX/wwbdkstrzc+hk2uSONnzUuP/3nacr28lUqpT6ombkktbT5wHfBd4AflEyT1AH4D9MqjvhMR/8jj/wB0A54F9gG2jYgpku4B1gXaA5dHxHWSLgE6SHoBeCUihkmaERGdJd0O3BQRI/M6RwD3AfcAlwCDgHbA1RFxbaP9FZq5NTrA0M3hxufhyP9ZenpDtKS6tIcPvrd4ePcR8LPBKaAWLIRP5qRO/gkfwEsfpNZVm1bw3plly7i4ZQdUXTTXllRjd5xfDQyT1KXK+MuBX0bEdsBXgOvz+POBRyNiG+BuFocYwNERsS3QDzhNUreIOAeYHRFbR8SwKuv4I3A4gKS2wB7ASOAY4NO87u2A4yT1rlq4pOMljZM07qNZK/z6Vwqn7wRTGuhvcPbDsN5lqeWz3mVpr2Jt5i2EQTfCllfDiffBTYekgLLG9+Dd/6TfOlfy3FPvccR+tzNs79sWTdtx/au54PS/cseICfRb50r+NbHx9vIpIhpnwYtbNBcC84DZ5D4pSR8Ck8pm7wFsCjwOHBwRb+ZlfAxsnFtSw4GD8/zrA3tHxNOl9VSz3vbA68CGpBbZYbmldSewFVD62HUBToiI0TW9ln5rK8YeX7+/hzWtycNbyEFTK5F1dPH4iOhXdXxT7N37FfAccGPZuFbAThExu3xGSapuAZIGAXvm58ySNIa02VejiJiT59ub1KIqfQ0IODUiqulJMbOiafSGc0R8DPyJtJlVMho4pTQgqdQt+wRwWB43GFg9j+8CTMsBtSmwY9my5kmqYd8TfwSOAgYApVAaBZxUeo6kjSV1WsGXZ2aNrKm27i8FupcNnwb0kzRB0kSg1PV6ATBY0nPAvsD7wHTgIaCNpAnARcDTZcu6Dpgg6dZq1jsaGAj8NSI+z+OuByYCz0l6GbgWHy9mVliN9uEs7yeKiA+AjmXDU8id2lV8Suprmi9pJ2C3iCid9LBvDes5Gzi7hvXOI+0pLJ9/IemwBXdamDUDRWtB9AL+JKkV8DlwXIXrMbMKK1RIRcTrQDVH45hZS+UjTsys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaHVeHNQSVcCUdP0iDitUSoyMytT2x2MxzVZFWZmNagxpCLipvJhSZ0iYmbjl2Rmttgy+6Qk7SRpIvBqHu4r6ZpGr8zMjLp1nP8K2BuYChARLwIDG7MoM7OSOu3di4h3qoxa0Ai1mJktpbaO85J3JPUHQlJb4DTypp+ZWWOrS0vqROBk4IvAe8DWedjMrNEtsyUVEVOAYU1Qi5nZUuqyd28DSfdJ+kjSh5L+ImmDpijOzKwum3t/AP4E9ATWBu4AbmvMoszMSuoSUoqI30fE/PxzC7WcLmNm1pBqO3dvjfzwb5LOAf5ICqfDgQeaoDYzs1o7zseTQkl5+ISyaQFc1FhFmZmV1HbuXu+mLMTMrDp1OZgTSVsAfYD2pXERcXNjFWVmVrLMkJJ0PjCIFFIjgX2BJwCHlJk1urrs3RsK7AFMjoijgL5Au0atyswsq0tIzY6IhcB8SasBHwI+mNPMmkRd+qTGSeoK/Ja0x28G8EyjVmVmltXl3L1v5Ye/kfQQsFpETGjcsszMktoO5tymtmkR8VzjlGRmtlhtLalLa5kWwO4NXEthzVt7LSYPP7rSZdhyaD/n7UqXYA2ktoM5d2vKQszMquObg5pZoTmkzKzQHFJmVmh1uTKnJH1d0nl5uJek7Ru/NDOzurWkrgF2Av43D08Hrm60iszMytTliPMdImIbSc8DRMS0fGsrM7NGV5eW1DxJrcmXDJbUA1jYqFWZmWV1CakrgLuBL0j6MekyLRc3alVmZlldzt27VdJ40uVaBBwUEb6DsZk1ibpc9K4XMAu4r3xcRPy3MQszM4O6dZw/wOIbMrQHegP/BDZvxLrMzIC6be5tWT6cr45wQg2zm5k1qOU+4jxfomW7RqjFzGwpdemTOr1ssBWwDfBRo1VkZlamLn1Sq5Y9nk/qo/pz45RjZrakWkMqH8TZOSLOaqJ6zMyWUGOflKQ2EbGAtHlnZlYRtbWkniEF1AuS7gXuAGaWJkbEXY1cm5lZnfqk1gCmkq5pXjpeKgCHlJk1utpC6gt5z97LLA6nkmjUqszMstpCqjXQmSXDqcQhZWZNoraQej8iLmyySszMqlHbEefVtaDMzJpUbSG1R5NVYWZWgxpDKiI+bspCzMyq41tamVmhOaTMrNAcUmZWaA4pMys0h5SZFZpDyswKzSFlZoXmkDKzQnNImVmhOaTMrNAcUmZWaA4pMys0h5SZFZpDyswKzSFlZoXmkDKzQnNImVmhOaTMrNAcUmZWaA4pMyu0utxm3VZivVr/hE237LFo+IZ7hvLOW59y2G63cuO9h7LXARsBcMT+f+KEM3eg/6D16rW+Yfv8keeffo/tdlmXm+4/bNH4M455gAnj3ici2GDjNfjliAPo1Lltvda1Mvp46lwOGfIIAB9+MJtWrUT3Hu0BeHnCNLbYanXmz1/Ixpt04arrd6JjxxX/iL/+z0859finmfDCx3x/eF9O+W4fAObMWcABez7M558vYP784ICDe3HOD7cC4Pz/e45RI9+jbdtWrN+7M1detxNdutbvfXRLqoVr36ENo184dtHPuut3BaDnOqtyxY//0eDrO+msHbj8919eavzwX+7Jwy8ey18nHMcXe3XhxqvGNfi6VwZrdGvHmLFDGDN2CEccuxEnnrrpouFOndowZuwQnhi/P6u0bcWI375er3V1Xb0dF1/aj5O/s9kS49u1a8XdD+3B35/ZjzFjh/Do6EmMGzsFgEF79OSJ8fvx2LP78aWNVuNXP3+lXjWAQ8pq0KfvmqzWpR2PPfxmgy53lz1602nVpb9ZV12tHQARwZzZ85B8b9r62HHnHrz5n+n1WkaPL7Rnm37daLPKkjEhic6dVwFg3ryFzJu/kNLbtduePWnTJs3fb/vuTHpvVr1qAG/utXhzZs9n8NbXA7Bu767ccPfQRdNOO3dnfnbu3xm4V+8an//rnz/N3be+vNT4HQb24qIrBi9XLacfdT+Pjvw3G/XpznmX7rlcz7XF5s9fyCOj32f3vXouNe2Yrz/OG68vHV4nnbYphw/boM7rWLBgIXv0f4g335jO0SdszLbbd19qnltvfoODhtavewAqFFKSFgAv5fW/ChwREXWOXElrA1dExFBJWwNrR8TIPO3LQJ+IuKQRSl/plDb3qrPDgF4AjH38vzU+/6SzduSks3ZskFouu3F/FixYyA9PHc29t0/k8KP6NshyW4rZsxcwaIeRAOzYvwdfP/JLS81zwy0DGmRdrVu3YszYIXz6yed88/DHePWVT9hs866Lpl/205dp01oc+tX1672uSrWkZkfE1gCSbgVOBC6r65MjYhJQ+srfGugHjMzT7gXubdBqW7DTfrAzV/z4yUVN+KoasiUF6Z//gMM34zc/H+uQWk4dOrRmzNghtc7TUC2pki5d27LzwC/wyOhJi0Lqj7f8h9Ej3+OuB/dokM32ImzuPQ5sJWkN4HfABsAs4PiImCBpV+DyPG8AA4FuwP3ANsCFQAdJuwA/ATqQQusHwIvABhGxUFJH4J95+b2Aq4EeeV3HRcRrTfFim5tdB2/Az3/4GB9Mqr5/oyFaUhHBW29Mo/eGaxAR/PW+f7Phpt3qtUyrXkO0pKZ8NIdVVmlFl65tmT17Po89OplTz0h7/h4ZPYkrLn2Fe0fvVa89i+UqGlKS2gD7Ag8BFwDPR8RBknYHbia1ks4ETo6If0jqDMwpPT8iPpd0HtAvIk7JyzwyT/tU0ovArsDfgAOAURExT9J1wIkR8bqkHYBrgN2b5lU3P6f9oD9HH3hngyzrkAE38+/XpjJzxjz6rXMlv7hhPwbu1ZvvHnE/0z+bCxFs1ndNfvLrfRpkfbbiPpg8mz13fpDp0+fRqpW49qrXePL5A/hg8mxOOe4pFiwIFi4MDvzKeuw9ZB0Azvnus8ydu5Ch+z8KwLbbd+PSK3eoVx2KiHq/mOVe6eI+KUgtqTOAscBXIuI/eZ53gC2Ak4CDgVuBuyLiXUnrA/dHxBY5lKqGVL+IOEXS14CBEXGipLtJYfQU8BGpVVXSLiKW2M8q6XjgeIAv9lpt27Fvn9KwfwRrVO3nvF3pEmw5de9w6/iI6Fd1fMX7pEpU/cZrRMQlkh4AhgBPS9qTstbUMtwL/CRvSm4LPAp0Aj6puv5qVnwdcB1A3349mz7JzQwo1nFSjwHDACQNAqZExGeSvhQRL0XET4FxwKZVnjcdWLW6BUbEDOAZUp/W/RGxICI+A96UdGhelyS5h9asoIoUUsOBfpImAJcAR+Tx35H0cu5fmg08WOV5fwP6SHpB0uHVLPd24Ov5d8kw4Ji8zFeAAxvuZZhZQ6pIn1Rz07dfzxg57uhKl2HLwX1SzU9NfVJFakmZmS3FIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0BxSZlZoDikzKzSHlJkVmkPKzApNEVHpGgpP0kfA25WuoxF0B6ZUughbLivze7ZeRPSoOtIh1YJJGhcR/Spdh9VdS3zPvLlnZoXmkDKzQnNItWzXVboAW24t7j1zn5SZFZpbUmZWaA4pMys0h5SZFZpDyswKzSFlyyRJ+XdPSWtXuh6rWem9Wpl4757ViaSDgO8AnwKvAVdGxLuVrcrKSVLkD7SkzYAZwLvRzD/kbknZMknaEjgd2B94BtiNFFZWIGUBdQpwLXAW8Ehzb105pKwuFgD3A4cC+wFfjYjpkjavbFlWlaS9gYNJ79NnwPzKVlR/DimrkaQ+koYCnwMDgG8B34yI/0jaF/itpLUqWqRV9QnpqPRjge2B/SMiJA2ubFkrrk2lC7BC2xk4MiJ2lvQI6Z9+kKTtgR8AZ0fE5IpWaABIOgpYBXgEGAn8JyK2y9OOBIZIGhsRzW4z3R3ntkip41VS64hYkMf9AXgqIq6UdCywHrAG8JeIGF3eWWtNR1KriFhYNjwIOBMYCnwZ+CXwPWB94CukFvDLTV9p/bklZUjaGOgbEXdI6gfsKumNiLgH+B0wGCAirs/zrxIR8/I4B1QFlAdU9hLpwow7RMSfJC0EdgAC+FpEvNbUNTYU90kZpP+DDyWtCrwDtANOlnQVMI+0qfCNsvmbfWdscyVpc0nfzI/3l/SApE1IneSPA1dJ6hQRd0bEWRHxveYcUOCQMiD/E/+DFFAHRcTFpE2G1sCOQFfgCEmd8/xuPVWApFZAN2CkpN7A34CXgVOBm4HxwN+BfSpWZCPw5l4LJakjsFdE/EXSDqQ9eLsDD0lqHxGXSzoZWAuYA/w7ImZUsOQWTVLbiPgceEzSOsC5wIsRcbak1YFvAjcBvYDuku5aWb5M3HHegkkaAfQjhdBxEfG8pG2AvwLnRsQ1VeZ3J3kFSOpC2tP6GNCftBevLbAH8Cbwq4hYIKkP0JcUXhMrVW9Dc0i1QGV78TYhbTL8NyJ2LJu+DTAWODMiLq9UnQaS2pA2u48ktZa6AZvl9+8AYG9Sh/kvI2Kl7Ct0n1QLUxZQrYD3gZ2AmZIeKs0TEc8BfYCV5tu4OZK0KXBNRMwldYxvCzxFCiqAh0nHRG0KnFKRIpuAW1ItSFlADSZ1iE+OiOvytEeBmcCPgJ8BB0fEx97EqxxJrYHVgQ2BV4GewIHAOqTwejW3hjcBno6IDytWbCNySLUwkvYBLiV9894G/Bn4YQ6k20gfimsi4t4KltmiVXOg5m9JLdshwGrACfn3p0AP0pH/ze5I8rpySLUQefNuVdIeoB8CawI/B94jne91akRMk9Q1Ij5xC6oyqlxuZW/SaS4BXAxsBxwCdAAOAw4CTouIlypUbpNwSK3kyjbxOkbELEndSKe13EQ6abgjqW/qKuCCiJhdwXIty4d/nAoMySd0tyJthm9NugrFlHyoyJyKFtoE3HG+EisLqB2AxyVtGRFTScfHfU7atOsJjAL+7IAqBkkDgGOAgTmg+pHep/NJfVM35/6quRUss8n4YM6VWA6ovUibCK2BUZL2joiXJD0D3Eo6AfWUiHi2gqW2aNVsWs8jHRoyLF+ueT/gXeB7EXGqpDVLJ4C3BG5JrcTyqRNXADdFxNbAb4C/5EvLngtcAHwjIh6uYJktWpU+qHUkdSdd/XQusDFwb0T0Ad4iHXhLRHxQoXIrwi2pldtUYBzpH5yIuFDSRqTNu50j4skK1tailcKpLKBOA75GOgzk38DJpYMzJR1MCqifVqreSnJLaiVSdleXLpK6RMRnpD16h5TNNoK06fCX0gnDVhGLGgi5D+pI0vt0ALAuaVO8dMjIKcAREfFG05dZeW5JrUTKTpU4HZgm6Wng/4Db8kmps0g3UziGtOeoE+mOItaEcj/h0ZJeJLV03wWeBt7PLashkp6W9BXSEeXPrawHataFW1LNXKn1lB/vCHwf+AapX+O4iHiVdEzNu0Bn0rWv1ySdqFr1wmnWyHLL6MfAk6Qvia8Be5JObdmqbNZHSd87s1tyQIFbUs2apB7AQZJuy5dRaQv8hHQ+3oHkK2oCn5euaCCpP+lC/QdFxEcVKLvFkrQGqWV0YETcJ6kX6dinF0it3Osk3UraRD+AtGne4jmkmredSZeIbZcvu9KaFFJTgX3zkeN7ASdKOjGPfxvYIyLerlDNLVY+9egA4GeS/h4R/5UUpKsa/FbSZ6Tz8tYCDo2If1W04IJwSDVDWnyjhPtIwTSIdCjBryXdReqA7ZlPqziPdHxNqdX0XiVqtiQiHsjXHx8vaVM0X98AAAQpSURBVBTpUs1/yNPurGhxBeXTYpqZfNb7scBo4LGImKt0D7x9gYkR8RtJw0lHKHcFfhcRo3wuXrFI2pP0Hq4VER9K6uAj/qvnkGpmJO1KOhr5deBPwAakE4X3IvVJTQJG5D19LeLcruYqf7n8AtitpXeO18Yh1QxJ2oV02/MdSPdUW510a+13SdceGk66FVV1tz6yApF0IOmcvH6kvXn+QFbhkGqm8rfwz4D+ETE9t7C2BI4HvhsRj1S0QKszSZ3DN7mokUOqGZM0BLgS2C4iPs7jSlc+cB+UrRS8d68Zi4iReU/Ra5I2iYhppWByQNnKwi2plYCk/YCZETGm0rWYNTSH1ErEm3i2MnJImVmh+QRjMys0h5SZFZpDyswKzSFlDULSAkkvSHpZ0h2SOtZjWSMkDc2Pr5fUp5Z5B+XLzyzvOt7K1xOv0/gq8yzXgZeShks6c3lrtMQhZQ1ldkRsHRFbkG6XdWL5xHwLpuUWEcdGxMRaZhlEuoCfraQcUtYYHgc2zK2cv0n6A/CSpNaSfi7pWUkTJJ0A6dAJSVdJmijpAeALpQVJGpPvO4ekfSQ9J+lFSY9IWp8Uht/NrbgBknpI+nNex7OSds7P7SZptKTnJV0LiGWQdI+k8ZJekXR8lWmX5loeyRcfRNKXJD2Un/O4pE0b4o/Z4kWEf/xT7x9gRv7dBvgLcBKplTMT6J2nHQ+cmx+3I13fuzfp+lcPk66NtTbptu9D83xjSCff9gDeKVvWGvn3cODMsjr+AOySH/cCXs2PrwDOy4/3I926vHs1r+Ot0viydXQAXga65eEAhuXH5wFX5cePABvlxzsAj1ZXo3+W78enxVhD6SDphfz4ceAG0mbYMxHxZh4/GNiq1N8EdAE2AgYCt0W6kN8kSY9Ws/wdSdfPehPSVS5rqGNPoE/Zpd9Xk7RqXsch+bkPSJpWh9d0mtLtpCDdwWUj0tVNFwK35/G3AHcp3XmnP3BH2brb1WEdtgwOKWsosyPdgHSR/GGdWT4KODUiRlWZbwipdVIb1WEeSF0YO0WVC8jlWup85LKkQaTA2ykiZkkaA7SvYfbI6/2k6t/A6s99UtaURgEnSVoFQNLGkjoBjwFfzX1WPYHdqnnuU8CuSndlLt3UAGA66cYFJaNJ96kjz1cKjceAYXncvqRrcNWmCzAtB9SmpJZcSSug1Br8GvBEpHscvinp0LwOSeq7jHVYHTikrCldD0wEnpP0MnAtqTV/N+lKoy8Bvwb+XvWJka7Rfjxp0+pFFm9u3QccXOo4B04D+uWO+Yks3st4ATBQ0nOkzc7/LqPWh4A2kiYAF5Hui1cyE9hc0nhgd+DCPH4YcEyu7xXSHXusnnzunpkVmltSZlZoDikzKzSHlJkVmkPKzArNIWVmheaQMrNCc0iZWaE5pMys0P4fSaPYnnsUD78AAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAYoAAAEWCAYAAAB42tAoAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjEsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+j8jraAAAgAElEQVR4nO3dd5hV1dXH8e8S6VWKKCJCBGkKKIgdUSJi19iwRhODir3FHo3YW2woIhqMGogSC1aIBTEaX0VBqiACAgqK2CiiMKz3j32GuQwz954pt838Ps9zn7nl3HPWPTOz19377LOOuTsiIiKl2SzbAYiISG5TohARkaSUKEREJCklChERSUqJQkREklKiEBGRpJQopEzMbIaZ9c12HLnCzK4ysxFZ2vZIM7sxG9uubGZ2kpmNL+d79TeZZkoUeczMFpjZz2a20syWRg1Hg3Ru0927uvuEdG6jkJnVNrNbzGxh9Dk/M7PLzMwysf0S4ulrZosTn3P3m939jDRtz8zsfDObbmarzGyxmT1jZjulY3vlZWbXm9mTFVmHuz/l7v1jbGuT5JjJv8nqSoki/x3m7g2AHsDOwJVZjqfMzGzzUl56BugHHAw0BE4BBgH3piEGM7Nc+3+4F7gAOB9oCuwAPA8cUtkbSvI7SLtsblticnfd8vQGLAB+m/D4duDlhMe7A+8BPwCfAH0TXmsK/B34CvgeeD7htUOBKdH73gO6Fd8m0Ar4GWia8NrOwLdAzejxH4BZ0frHAdslLOvAOcBnwPwSPls/YA2wbbHndwMKgPbR4wnALcAHwI/AC8ViSrYPJgA3Ae9Gn6U9cHoU8wpgHnBmtGz9aJn1wMro1gq4HngyWqZt9Ll+DyyM9sXVCdurCzwe7Y9ZwJ+BxaX8bjtEn7N3kt//SGAo8HIU7/8B2ye8fi+wCPgJ+AjYJ+G164ExwJPR62cAvYH/RftqCfAAUCvhPV2B/wDfAV8DVwEDgF+BtdE++SRatjHwaLSeL4EbgRrRa6dF+/xv0bpujJ77b/S6Ra99E/1OpwI7Er4krI22txJ4sfj/AVAjiuvzaJ98RLG/Id3K0dZkOwDdKvDL2/gfpDUwDbg3erwNsJzwbXwz4IDocYvo9ZeBfwFbADWBfaPnd4n+QXeL/ul+H22ndgnbfBP4U0I8dwDDovtHAnOBzsDmwDXAewnLetToNAXqlvDZbgXeLuVzf0FRAz4haoh2JDTm/6ao4U61DyYQGvSuUYw1Cd/Wt48aq32B1cAu0fJ9KdawU3KieISQFLoDvwCdEz9TtM9bRw1gaYniLOCLFL//kYSGtncU/1PA6ITXTwaaRa9dAiwF6iTEvTb6PW0WxduTkFg3jz7LLODCaPmGhEb/EqBO9Hi34vsgYdvPAw9Hv5MtCYm88Hd2GrAOOC/aVl02ThQHEhr4JtHvoTOwdcJnvjHJ/8FlhP+DjtF7uwPNsv2/mu+3rAegWwV+eeEfZCXhm5MDbwBNotcuB54otvw4QsO/NeGb8RYlrPMhYEix52ZTlEgS/ynPAN6M7hvh22uf6PGrwB8T1rEZodHdLnrswP5JPtuIxEav2GvvE31TJzT2tya81oXwjbNGsn2Q8N4bUuzj54ELovt9iZcoWie8/gEwMLo/Dzgw4bUziq8v4bWrgfdTxDYSGJHw+GDg0yTLfw90T4h7Yor1Xwg8F90/AZhcynIb9kH0uCUhQdZNeO4E4K3o/mnAwmLrOI2iRLE/MIeQtDYr4TMnSxSzgSPS8f9WnW+5NiYrZXekuzckNGKdgObR89sBx5rZD4U3YG9CktgW+M7dvy9hfdsBlxR737aEYZbixgB7mFkroA+hkXwnYT33JqzjO0Iy2Sbh/YuSfK5vo1hLsnX0eknr+YLQM2hO8n1QYgxmdpCZvW9m30XLH0zRPo1racL91UDhBINWxbaX7PMvp/TPH2dbmNklZjbLzH6MPktjNv4sxT/7Dmb2UjQx4ifg5oTltyUM58SxHeF3sCRhvz9M6FmUuO1E7v4mYdhrKPC1mQ03s0Yxt12WOCUmJYoqwt3fJnzbujN6ahHh23SThFt9d781eq2pmTUpYVWLgJuKva+eu48qYZs/AOOB44ATgVEefa2L1nNmsfXUdff3EleR5CO9DuxmZtsmPmlmvQmNwZsJTycu04YwpPJtin2wSQxmVpswdHUn0NLdmwCvEBJcqnjjWEIYciop7uLeAFqbWa/ybMjM9iH0qI4j9BybEMb7E2eMFf88DwGfAh3cvRFhrL9w+UWEIbmSFF/PIkKPonnCfm/k7l2TvGfjFbrf5+49CcOCOxCGlFK+L0WcUk5KFFXLPcABZtaDcJDyMDM70MxqmFmdaHpna3dfQhgaetDMtjCzmmbWJ1rHI8BZZrZbNBOovpkdYmYNS9nmP4FTgaOj+4WGAVeaWVcAM2tsZsfG/SDu/jqhsfy3mXWNPsPuhHH4h9z9s4TFTzazLmZWD7gBGOPuBcn2QSmbrQXUBpYB68zsICBxyubXQDMzaxz3cxTzNGGfbGFm2wDnlrZg9PkeBEZFMdeK4h9oZlfE2FZDwnGAZcDmZvYXINW38oaEA9srzawTcHbCay8BW5nZhdG05YZmtlv02tdA28JZY9Hf13jgLjNrZGabmdn2ZrZvjLgxs12jv7+awCrCpIaChG39JsnbRwBDzKxD9PfbzcyaxdmulE6Jogpx92XAP4Br3X0RcAThW+Eywjetyyj6nZ9C+Ob9KeHg9YXROiYBfyJ0/b8nHJA+LclmxxJm6Hzt7p8kxPIccBswOhrGmA4cVMaPdDTwFvAa4VjMk4SZNOcVW+4JQm9qKeFA6/lRDKn2wUbcfUX03qcJn/3E6PMVvv4pMAqYFw2plDQcl8wNwGJgPqHHNIbwzbs051M0BPMDYUjlKODFGNsaR/gyMIcwHLeG5ENdAJcSPvMKwheGfxW+EO2bA4DDCPv5M2C/6OVnop/Lzezj6P6phMQ7k7AvxxBvKA1CQnsket8XhGG4wp7yo0CXaP8/X8J77yb8/sYTkt6jhIPlUgFWNFIgkn/MbALhQGpWzo6uCDM7m3CgO9Y3bZFsUY9CJEPMbGsz2ysaiulImGr6XLbjEkklbYnCzB4zs2/MbHopr5uZ3Wdmc81sqpntkq5YRHJELcLsnxWEg/EvEI5DiOS0tA09RQdHVwL/cPcdS3j9YMJY88GEk7vudffdii8nIiLZlbYehbtPJMydL80RhCTi7v4+0MTM4h7sEhGRDMlmMa5t2HgWxuLouSXFFzSzQYQ6L9SvX79np06dMhKgiEimuENBQcVu69dvut6tWMLWLGUy67919xbliS2biaKkUtEljoO5+3BgOECvXr180qRJ6YxLRKRM1q6FH3+s2G316tTbqVsXGjeGZs3Cz6S3Rk7jJkabKWNpPnk8Wzw59Ivyfr5sJorFbHxmamtCJVMRkYz59deKN/I//5x6O/XqbdqYt2kTo8FPuNWsGeMDff89XHop/OY3cPXVsP/hwOHw5NBy76NsJoqxwLlmNppwMPvH6IxOEZFYfvml4o38mjWpt1O//sYN9hZbQNu28Rv4Ro1iNvIV9dxzMHgwLFsG11xTaatNW6Iws1GEQnXNLVwV7DpCoTDcfRihhs7BhDN/VxOuAyAi1UR5G/kffii6/0uy89ojxRv5pk2hXbtwv0mTeI385rl+aaWvv4bzzoNnnoEePeDll2GXyjvjIG0f391PSPF64YVrRCTPrFlT8W/ycRr5Bg02brSbNQsjKmX5Jp/zjXxlWLQoJIebboLLLqv07kt12IUiEnGvnEb+119Tb6thw40b7RYtoH37sjXyNWqkf5/krS++gBdfhHPPhV69YOHCkEnTQIlCJE8UNvKJQy/lua1dm3pbDRtuPCzTsiXssEP8Rr5hQzXyabN+PTz0EFwRFRE++mjYeuu0JQlQohDJCPcwM6ai3+RTNfJmm36T32or6NhRjXyVMHs2nHEG/Pe/cOCB8PDDIUmkmRKFSAruYY57RRv5deuSb8csDLckNtpbbw2dOpWtkd9MpT6rptWrYe+9w5l1I0fCqaeGP5oMUKKQKs0dVq2qWAP/00+pG/nNNtu0kd9mG+jSJX4j36CBGnkpwZw50KFDOBHjiSfCrKattspoCEoUkrOSNfJxx+l/+il8AUumpEa+dWvo2rXkBr2kKZUNGmTsy51UF2vWwJAhcNttoQdx8skwYEBWQlGikLRwh5UrK/5NPk4jX7zRLuvZrmrkJee8+y788Y/hmMTpp8Mhh2Q1HCUK2cT69ZXTyJdUoCxRjRqbNtrbbVe2Rr5+fTXyUsUMGQLXXRe+8YwbB/37p35PmilRVDHr18OKFRVv5FNdpqSkRr5t23hnuhbe6tVTIy+ygXv4h+jRI5xlfdNNobubA5QockhZGvnSxuhXrEjdyG+++aaNdlnOdlUjL1KJvvsOLroonI147bVw2GHhlkOUKCpJQUHFv8nHaeRr1ty00S7L2a6NG4dSxWrkRXLAmDFwzjkhWVx7bbajKZUSBaGR/+mnijfyqdSqtWmj3aFD2Rr5OnXUyIvkvSVLQumNZ5+Fnj1h/Hjo3j3bUZUq7xPFunUVb+RXrky9nZIa+ZYt4zfwTZqERl5EhK++Cgeqb7sNLr445ysX5nZ0pVizBvbcEz77LF4jX7v2pg331luX/Zu8iEi5LVgQividd17oRSxaFC5skQfyMlEsWwaTJ4dSJ3vskbqRr1072xGLSLVVUABDh8JVV4UTf449NpxZnSdJAvI0URSehDVwIJx2WlZDEREp3axZoYjfe++Fs6offjjj5TcqQ14nClW4FJGctXo19OkT5r3/4x+hBEeezkRRohARqUyffhrquterB089FWYztWyZ7agqJC9rVRZW8szxiQIiUp38/DNcfnmoJvnUU+G5/v3zPkmAehQiIhU3cWI4FvHZZ+HnoYdmO6JKlZc9CiUKEckZf/0r7LtvGOp4/XV45JFw4lQVokQhIlIehfV2evUKtZqmTYN+/bIbU5ooUYiIlMW338Ipp4Ry4BCuFXH33aHmfRWlRCEiEoc7PP10uL7t6NHV6rq1eXkwu3DWkxKFiGTEV1/B4MHwwgthqOn116Fbt2xHlTF5mRILexSaHisiGbF0Kbz5JtxxB/zvf9UqSUCe9ig09CQiaTdvHowdCxdeCLvsAgsXVrnZTHHldY9CiUJEKl1BAfztb7DjjuHa1UuXhueraZIAJQoRkSIzZsBee4VrROy/f3ich0X8KpuGnkREIBTx23ffULjvn/8M5anztIhfZcvLRKFZTyJSaWbOhM6dQxG/0aNDEb8WLbIdVU7J66EnzXoSkXJbvRouuwx22gmefDI899vfKkmUIC+bWg09iUiFTJgAf/oTzJ0LZ54Jhx+e7YhyWl73KJQoRKTMrrsO9tsvnGn95pswbFi4ZrKUSolCRKqHwiJ+vXvDJZfA1KkhYUhKaU0UZjbAzGab2Vwzu6KE1xub2Ytm9omZzTCz0+OsVwezRSS2ZcvgxBPhhhvC40MOgTvvDAevJZa0JQozqwEMBQ4CugAnmFmXYoudA8x09+5AX+AuM6uVat3qUYhISu5hmmvnzjBmDNRK2bRIKdLZo+gNzHX3ee7+KzAaOKLYMg40NDMDGgDfAetSrViznkQkqcWLwwHqk06C9u1h8mS48spsR5W30pkotgEWJTxeHD2X6AGgM/AVMA24wN3XF1+RmQ0ys0lmNmnZsmXqUYhIcsuWhcuT3n03vPtuuI61lFs6E0VJpzR6sccHAlOAVkAP4AEza7TJm9yHu3svd+/VokULJQoR2dTcuaFGE8DOO8OiReHKc2ooKiydiWIxsG3C49aEnkOi04FnPZgLzAc6pVqxEoWIbLBuXTg4vdNO4frVX38dnm+0yXdOKad0JooPgQ5m1i46QD0QGFtsmYVAPwAzawl0BOalWrFmPYkIEK5Tveee4Qzr/v1DEb+WLbMdVZWTtsPB7r7OzM4FxgE1gMfcfYaZnRW9PgwYAow0s2mEoarL3f3bVOtWj0JEWL06nAex2WahRtNxx6mIX5qkdd6Qu78CvFLsuWEJ978C+pd1vZr1JFKNTZ8eDk7Xqwf/+lco4te8ebajqtJ0ZraI5IdVq8J1Irp1Kyri16+fkkQG5OV3ciUKkWrmjTdCEb/582HwYDii+ClZkk5526Mw03CkSLVw7bWh/Pfmm8Pbb8PQoZrRlGF5mSjWrVNvQqTKWx+de7vnnvDnP8Mnn0CfPtmNqZrKy0RRUKBEIVJlffNNuAzpX/8aHh90ENx2G9Stm924qjElChHJDe7hIHXnzvDcc6rumkPyNlFoaqxIFbJoERx6KJxyCnTsGIr4XX55tqOSSN4mCvUoRKqQ5ctD8b5774V33oEuxa9IINmUl9/LlShEqoA5c2DsWLj0UujRI/QqGjbMdlRSgrzsUWjWk0geW7cuHJzu1g1uuqmoiJ+SRM7Ky0ShHoVInvrkE9htN7jiCjj4YJg5U0X88oCGnkQkM1avDiU3Nt88XJr06KOzHZHElLeJQrOeRPLE1KnhWhH16sEzz4Qifk2bZjsqKQMNPYlIeqxcCRdcEA5UP/FEeG6//ZQk8lBefi9XohDJcf/5DwwaBAsWwLnnwlFHZTsiqYC87FFo1pNIDrv66nC1udq1wzkR99+vGU15LnaiMLP66QykLNSjEMlBhUX89t4brrwSpkwJ9yXvpUwUZranmc0EZkWPu5vZg2mPLAklCpEcsnQpHHMMXH99eHzQQXDzzVCnTlbDksoTp0fxN+BAYDmAu38CZLXWr2Y9ieQAdxg5MpTbeOklXSOiCovV3Lr7Itv4KkEF6QknHvUoRLLsiy/Cwerx48Pw0ogRoZifVElxehSLzGxPwM2slpldSjQMlS1KFCJZ9sMP8OGH8MAD4apzShJVWpwexVnAvcA2wGJgPDA4nUGlollPIlkwe3Yo4nfZZeGkuYULoUGDbEclGRCnR9HR3U9y95buvqW7nwx0TndgyahHIZJBa9fCLbeE5HDrreEKdKAkUY3ESRT3x3wuY5QoRDJk8uRQxO+qq+Cww0IRvy23zHZUkmGlDj2Z2R7AnkALM7s44aVGQFabac16EsmA1avhgAOgZk3497/hd7/LdkSSJcma21pAg2iZxNMqfwKOSWdQqahHIZJGkyeH+kz16oUqr927wxZbZDsqyaJSE4W7vw28bWYj3f2LDMaUkhKFSBqsWBHOqB46FB5/HE49Ffr2zXZUkgPiDOCsNrM7gK7AhlMt3X3/tEWVgmY9iVSy116DM88MlyO94AINM8lG4hzMfgr4FGgH/BVYAHyYxphSUo9CpBJdeWUou1G/Prz7Ltxzj2Y0yUbi9CiaufujZnZBwnDU2+kOLBklCpFKUPiP1LdvmB1yzTWh4qtIMXESxdro5xIzOwT4CmidvpBS06wnkQpYsgTOOQe6doUhQ+DAA8NNpBRxhp5uNLPGwCXApcAI4MK0RpWCehQi5eAOf/97KOL36quaySSxpfxe7u4vRXd/BPYDMLO90hlUKkoUImW0YAH86U/w+uuwzz6hiN8OO2Q7KskTyU64qwEcR6jx9Jq7TzezQ4GrgLrAzpkJcVOa9SRSRj/+CB9/DA8+GGY3bZaXF7eULEn21/IocAbQDLjPzP4O3Anc7u6xkoSZDTCz2WY218yuKGWZvmY2xcxmxD1Irh6FSAwzZ4baTFBUxO/ss5UkpMySDT31Arq5+3ozqwN8C7R396VxVhz1SIYCBxCqzn5oZmPdfWbCMk2AB4EB7r7QzGIVkVGiEEni11/h9tvDgeqGDeEPfwj1mernzNWMJc8k+2rxq7uvB3D3NcCcuEki0huY6+7z3P1XYDRwRLFlTgSedfeF0Xa+ibNiJQqRUkyaBLvuCtdeG06aUxE/qQTJehSdzGxqdN+A7aPHBri7d0ux7m2ARQmPFwO7FVtmB6CmmU0g1JO6193/UXxFZjYIGATQpk0bTY8VKcmqVWGaa5068MILcPjh2Y5IqohkzW1FrzlhJTznJWy/J9CPcID8f2b2vrvP2ehN7sOB4QC9evXy5cvVoxDZ4OOPQxG/+vXhueegWzdo0iTbUUkVUurQk7t/kewWY92LgW0THrcmnKxXfJnX3H2Vu38LTAS6p1qxZj2JAD/9BIMHQ8+e8OST4bk+fZQkpNKlc/rDh0AHM2tnZrWAgcDYYsu8AOxjZpubWT3C0FTK63HrGIVUe6+8Es6sfvhhuPhiOProbEckVVjaRvrdfZ2ZnQuMI1zo6DF3n2FmZ0WvD3P3WWb2GjAVWA+McPfpqdatRCHV2uWXh1lNXbqE60XsVvzQn0jlipUozKwu0MbdZ5dl5e7+CvBKseeGFXt8B3BH2darRCHVjDusXx/+8Pv1Cwesr7pKRfwkI1IOPZnZYcAU4LXocQ8zKz6ElDEeHQ7XrCepNr78Eo48Eq67Ljzu3x/++lclCcmYOMcoriecE/EDgLtPAdqmL6R41KOQKs8dHnkkDDGNHw/Nm2c7Iqmm4nwvX+fuP5qVNNs18wp7FEoUUqXNnw9//CO89Va4XsQjj0D79tmOSqqpOIliupmdCNQwsw7A+cB76Q2rdEoUUi2sXAlTp4ZZTWecofpMklVx/vrOI1wv+xfgn4Ry41m9HgUoUUgVNH063HxzuL/TTqGI36BBShKSdXH+Aju6+9Xuvmt0uyaq/ZQV6lFIlfPrr+Hg9C67wN/+Bt9EJc/q1ctuXCKROInibjP71MyGmFnXtEeUgmY9SZXy4YfhzOrrr4djj1URP8lJca5wt5+ZbUW4iNFwM2sE/Mvdb0x7dEmoRyF5b9UqGDAA6taFsWPhsMOyHZFIiWINfrr7Une/DziLcE7FX9IaVdJYwk8lCslbkyaFk+fq1w9VXmfMUJKQnBbnhLvOZna9mU0HHiDMeGqd9shKoUQheevHH8NlSHfdtaiI3957Q+PG2Y1LJIU4I/1/B0YB/d29ePXXrFGikLzy4otw1lmwdClceikcc0y2IxKJLc4xit0zEUhc6lFI3rnsMrjzzjDl9fnnQ49CJI+UmijM7Gl3P87MprHxBYfiXuEurTTrSXKaOxsuxdi/PzRqFKq+1qqV7chEyixZc3tB9PPQTAQSl3oUkvMWL4azzw5XmrvpJjjggHATyVPJrnC3JLo7uISr2w3OTHglxRV+KlFIzlm/PpTc6NIF3nwTttoq2xGJVIo402NL+ip0UGUHUlZKFJJT5s2D/fcPB6x794Zp0+C887IdlUilSHaM4mxCz+E3ZjY14aWGwLvpDqw06lFITlq1KpxVPWIE/OEPkCPVlkUqQ7JjFP8EXgVuAa5IeH6Fu3+X1qiSUKKQnDFtWjhh7pprwoymL74IZ1mLVDHJhp7c3RcA5wArEm6YWdP0h5acEoVkzS+/wF/+Eor43XdfURE/JQmpolL1KA4FPiJMj03sSzvwmzTGVSoVBZSsev/9cEGhmTPhlFNCtddmzbIdlUhaldrcuvuh0c92mQsnNQ09SdasWgWHHBJqNL3yChyU9TkdIhkRp9bTXmZWP7p/spndbWZt0h9ackoUkjH/939FRfxefDEU8VOSkGokzvTYh4DVZtYd+DPwBfBEWqNKQj0KyZgffgiXId1996IifnvuCQ0bZjcukQyLkyjWubsDRwD3uvu9hCmyWaVEIWn1/PPhxLmRI0PpjWOPzXZEIlkT55DwCjO7EjgF2MfMagA10xtW6dSjkLS7+OJwkLp79zDU1LNntiMSyao4ieJ44ETgD+6+NDo+cUd6wyqdZj1JWiQW8Tv44DCT6c9/hppZ+04kkjNSDj25+1LgKaCxmR0KrHH3f6Q9shTUo5BKs3BhmM103XXh8W9/C1dfrSQhEokz6+k44APgWMJ1s//PzLJ21RUNPUmlWb8eHnwQunaFt9+GVq2yHZFIToozgHM1sKu7fwNgZi2A14Ex6QysNEoUUinmzg01md55J5QAHz4c2rbNdlQiOSlOotisMElElhNvtlRaKVFIhaxZA3PmwN//Dr//vYr4iSQRJ1G8ZmbjCNfNhnBw+5X0hZScehRSblOmhCJ+110HO+4ICxZAnTrZjkok58U5mH0Z8DDQDegODHf3y9MdWOnxhJ+a9SSxrVkTDk736gUPPVRUxE9JQiSWZNej6ADcCWwPTAMudfcvMxVYKupRSCzvvReK+H36aRhiuvtuaJr14scieSVZj+Ix4CXgaEIF2fszElEKGnqS2FatgsMOg9Wr4bXXwlnWShIiZZZsAKehuz8S3Z9tZh9nIqBUlCgkpf/9D3bbLRTxe+mlcDxC9ZlEyi1Zj6KOme1sZruY2S5A3WKPUzKzAWY228zmmtkVSZbb1cwKynJ+hhKFbOL778OU1z33hCeiupV77KEkIVJByXoUS4C7Ex4vTXjswP7JVhzVhBoKHAAsBj40s7HuPrOE5W4DxsUJWD0KKdGzz8I558CyZXDllXD88dmOSKTKSHbhov0quO7ewFx3nwdgZqMJFWhnFlvuPODfwK5lWblmPckGF10E99wDPXqECwrtvHO2IxKpUtLZ3G4DLEp4vBjYLXEBM9sGOIrQOyk1UZjZIGAQQOPGnQD1KKq9xCJ+hx4KW24Jl16q+kwiaZDOM6xLOtXViz2+B7jc3QuSrcjdh7t7L3fvVa9efUCJolpbsAAGDIBrrw2P+/ULw01KEiJpkc5EsRjYNuFxa+CrYsv0Akab2QLgGOBBMzsy1YrNVHGhWlq/Hu6/P8xieu892G67bEckUi2kHHoyMwNOAn7j7jdE16PYyt0/SPHWD4EOZtYO+BIYSLiuxQbu3i5hOyOBl9z9+WQrdVdvolr67DM4/XR4993Qmxg2TIlCJEPi9CgeBPYATogeryDMZkrK3dcB5xJmM80Cnnb3GWZ2lpmdVc54lSiqq19/hc8/h3/8IxywVpIQyRhzL37YoNgCZh+7+y5mNtndd46e+8Tdu2ckwmK22qqXr1gxiVWrsrF1yajJk0MRv+uvD49/+QVq185qSCL5ysw+cvde5XlvnB7F2uhcB4821gJYX56NVQZ3TY2t8tasCQend90VHn44nBsBShIiWRInUdwHPJWGCkoAABXlSURBVAdsaWY3Af8Fbk5rVElo6KmK++9/oXt3uPVWOPVUmDkTWrTIdlQi1VrK7+bu/pSZfQT0I0x5PdLdZ6U9siSUKKqolSvhiCOgUSMYPz5ceU5Esi7OrKc2wGrgxcTn3H1hOgMrjXoUVdB//xvqMzVoAC+/HKa/NmiQ7ahEJBJn6OllQrnxl4E3gHnAq+kMKhUliipi+fIwvLTPPkVF/HbfXUlCJMfEGXraKfFxVDn2zLRFlIJ6FFWAO4wZA+eeC999F86wHjgw21GJSCnKPH/I3T82szIV8KtMmvVUBVx0Edx7L/TsGY5FdM/KTGsRiSnOMYqLEx5uBuwCLEtbRDGoR5GH3GHdulCP6fDDoVUruPhiZX2RPBDnGEXDhFttwrGKI9IZVDIaespD8+dD//5FRfz23x/+/GclCZE8kfQ/NTrRroG7X5aheFJSosgjBQXwwANw1VXhl3bssdmOSETKodREYWabu/u6uJc9zSQlijwwZw6cdlq4fvVBB4UzrLfdNuXbRCT3JOtRfEA4HjHFzMYCzwAbKiy5+7Npjq1E6lHkiXXr4Isv4Mkn4cQTVRdeJI/FGSRuCiwnXIXOCWdnO5C1RKGh7Rw1aVIo4jdkCHTpAvPmqT6TSBWQrMndMprxNJ2iBFEoecnZNFOPIsf8/DNcdx3cdRdstRWcf36oz6QkIVIlJJv1VANoEN0aJtwvvGWFhp5yzNtvQ7ducMcd8Mc/wowZKuInUsUk61EscfcbMhZJTEoUOWTlSvjd76BJE3jjjTDtVUSqnGSJImePPipRZNk778Bee4WaTK++Cl27Qv362Y5KRNIk2dBTv4xFUQbqUWTRt9/CySdDnz5FRfx691aSEKniSu1RuPt3mQwkLs16ygJ3ePppOO88+P77cOBaRfxEqo28bHLVo8iwCy6A++8PlyZ94w3YaafU7xGRKiPvEoWGnjLEHdauhVq14KijYLvt4MILtfNFqqE4RQFzjtqqNPv8c+jXD665Jjzebz+45BLteJFqKu8ShXoUaVRQAHffHYaWPvoIOnbMdkQikgM09CTBp5/C738PH3wAhx0GDz0E22yT7ahEJAfkXaIAzXpKi/Xr4auvYNQoOP54FfETkQ3yrslVj6ISffBBKOJ3002hiN/nn4eD1yIiCXSMojpavRouvRT22AMefxyWRVe2VZIQkRLkXaIAJYoKeeutcLD6rrvgT39SET8RSUlDT9XJypXhcqRNmoSE0bdvtiMSkTygHkV1MGFCOFhdWMRv6lQlCRGJLe8ShXoUZbBsGZxwQjhh7sknw3O77gr16mU3LhHJK3k59KTpsSm4h2mu558PK1aES5OqiJ+IlFNeNrnqUaRw3nkwdCjsvjs8+miY+ioiUk55lyg09FSK9eth3bowxfWYY6B9+5AwtLNEpILSeozCzAaY2Wwzm2tmV5Tw+klmNjW6vWdm3VOtU4miBJ99Fi5DevXV4XHfvqr0KiKVJm2JwsxqAEOBg4AuwAlmVnwMZD6wr7t3A4YAw+OsW+1fZN06uPNO6NYNpkyBzp2zHZGIVEHpHHrqDcx193kAZjYaOAKYWbiAu7+XsPz7QOs4K1aiAGbNglNPhUmT4Igj4MEHoVWrbEclIlVQOoeetgEWJTxeHD1Xmj8Cr5b0gpkNMrNJZjYJNOtpg6+/hn/9C557TklCRNImnU1uSeVHvcQFzfYjJIq9S3rd3YcTDUuZ9fJq26N4//1QxO+WW8Iw0+efQ82a2Y5KRKq4dPYoFgPbJjxuDXxVfCEz6waMAI5w9+VxVlztEsWqVXDRRbDnnvDUU0VF/JQkRCQD0pkoPgQ6mFk7M6sFDATGJi5gZm2AZ4FT3H1O3BVXq0Tx+uuw445wzz0weLCK+IlIxqVt6Mnd15nZucA4oAbwmLvPMLOzoteHAX8BmgEPWrhQzjp375Vq3dUmUaxcGc6obtoUJk6EffbJdkQiUg2l9bCwu78CvFLsuWEJ988Azijreqt8onjzTdh331DEb9y4cGZ13brZjkpEqqm8KwoIVXjW09dfw3HHQb9+RUX8evZUkhCRrMrLRFHlehTu8MQToedQeGnSE0/MdlQiIkAe1nqCKpgozjkHHnooXJr00Ud1hrWI5BQlimxZvx7WroXateH440NyGDy4inw4EalKNPSUDbNnh4PVhUX89t1XlV5FJGcpUWTS2rVw663QvTtMnw477ZTtiEREUsrLoae8nPU0YwaccgpMngy/+124sNBWW2U7KhGRlPKxyc3PHkWNGvDddzBmDBx9dLajERGJTUNP6fTee3D55eF+p04wd66ShIjkHSWKdFi5Es4/H/beO5QB//bb8HxejpmJSHWnRFHZxo8PRfweeADOPTcctG7ePNtRiYiUW15+xc3ZRLFyJZx0EjRrBu+8A3vtle2IREQqTD2KyvCf/0BBQSjiN358uH61koSIVBF5mShyZqh/yZJwcLp//3BBIYCdd4Y6dbIbl4hIJcrLRJH1HoU7jBwZivi9/HI4iU5F/ESkisqV7+ZlkvVEcfbZ8PDDYVbTiBHQsWOWAxLJTWvXrmXx4sWsWbMm26FUG3Xq1KF169bUrMRLJStRxJVYxO/EE6FbNzjrLNgsLztlIhmxePFiGjZsSNu2bYmuYilp5O4sX76cxYsX065du0pbb162chlPFLNmhcuQXnVVeNynT6j0qiQhktSaNWto1qyZkkSGmBnNmjWr9B5cXrZ0GUsUa9fCzTdDjx7w6afhQLWIlImSRGalY3/n5dBTRmY9zZgBJ58cproeeyzcfz+0bJmBDYuI5Bb1KEqz+ebw44/w7LPw9NNKEiJ57LnnnsPM+PTTTzc8N2HCBA499NCNljvttNMYM2YMEA7EX3HFFXTo0IEdd9yR3r178+qrr1Y4lltuuYX27dvTsWNHxo0bV+Iyn3zyCXvssQc77bQThx12GD/99BMACxYsoG7duvTo0YMePXpw1llnVTieOJQoEr3zDlx6abjfsSPMmQNHHZWmjYlIpowaNYq9996b0aNHx37Ptddey5IlS5g+fTrTp0/nxRdfZMWKFRWKY+bMmYwePZoZM2bw2muvMXjwYAoKCjZZ7owzzuDWW29l2rRpHHXUUdxxxx0bXtt+++2ZMmUKU6ZMYdiwYRWKJ668HHqq9ESxYgVccQU8+CC0axfuN2+eQ2f2ieS/Cy8MI7mVqUcPuOee5MusXLmSd999l7feeovDDz+c66+/PuV6V69ezSOPPML8+fOpXbs2AC1btuS4446rULwvvPACAwcOpHbt2rRr14727dvzwQcfsMcee2y03OzZs+nTpw8ABxxwAAceeCBDhgyp0LYrQj2KV1+Frl3hoYfCX/K0aSriJ1KFPP/88wwYMIAddtiBpk2b8vHHH6d8z9y5c2nTpg2NGjVKuexFF120YSgo8XbrrbdusuyXX37Jtttuu+Fx69at+fLLLzdZbscdd2Ts2LEAPPPMMyxatGjDa/Pnz2fnnXdm33335Z133kkZX2XIy6/MlZYoVqyAU0+FLbcM147YffdKWrGIFJfqm3+6jBo1igsvvBCAgQMHMmrUKHbZZZdSZweVddbQ3/72t9jLunus7T322GOcf/753HDDDRx++OHUqlULgK233pqFCxfSrFkzPvroI4488khmzJgRK6FVRF4migqNCLnDuHFwwAHQsCG8/nq4qFDUvRSRqmP58uW8+eabTJ8+HTOjoKAAM+P222+nWbNmfP/99xst/91339G8eXPat2/PwoULWbFiBQ0bNky6jYsuuoi33nprk+cHDhzIFVdcsdFzrVu33qh3sHjxYlq1arXJezt16sT48eMBmDNnDi+//DIAtWvX3jAU1rNnT7bffnvmzJlDr169YuyNCnD3vLpBT1+61Mvnq6/cjzzSHdwff7ycKxGRuGbOnJnV7Q8bNswHDRq00XN9+vTxiRMn+po1a7xt27YbYlywYIG3adPGf/jhB3d3v+yyy/y0007zX375xd3dv/rqK3/iiScqFM/06dO9W7duvmbNGp83b563a9fO161bt8lyX3/9tbu7FxQU+CmnnOKPPvqou7t/8803G5b//PPPvVWrVr58+fJN3l/SfgcmeTnb3epxjMIdHnsMOneG116D229XET+RamDUqFEcVWzm4tFHH80///lPateuzZNPPsnpp59Ojx49OOaYYxgxYgSNGzcG4MYbb6RFixZ06dKFHXfckSOPPJIWLVpUKJ6uXbty3HHH0aVLFwYMGMDQoUOpETVoZ5xxBpMmTdoQ9w477ECnTp1o1aoVp59+OgATJ06kW7dudO/enWOOOYZhw4bRtGnTCsUUh3kJY2a5zKyXf/fdJLbYogxvOvNMGD48lN4YMQI6dEhbfCJSZNasWXTu3DnbYVQ7Je13M/vI3cs1RpWXxyhi9SgKCkIJjjp1whnWO+8MgwapPpOISBnlZauZMlHMmBGuMFdYxG+ffVTpVUSknPKy5Sx11tOvv8KQIaH3MHcu7LprRuMSkU3l2/B2vkvH/q46Q0/TpsFJJ4WfAwfCffdBBQ88iUjF1KlTh+XLl6vUeIZ4dD2KOpV8Oeaqkyhq1YLVq+GFF+DwwzMek4hsqnXr1ixevJhly5ZlO5Rqo/AKd5UpL2c9uYcpZLz9NowdC3fdFR4XFOTAdVJFRHJPRWY9pfUYhZkNMLPZZjbXzK4o4XUzs/ui16ea2S6xVvzTT+G61X37wvPPw7ffhueVJEREKl3aEoWZ1QCGAgcBXYATzKxLscUOAjpEt0HAQ6nW25gfQxG/4cPh4otVxE9EJM3S2aPoDcx193nu/iswGjii2DJHAP+IzjB/H2hiZlsnW2lbFkDjxqGI3113Qb16aQleRESCdB7M3gZYlPB4MbBbjGW2AZYkLmRmgwg9DoBfbMaM6ar0CkBz4NtsB5EjtC+KaF8U0b4o0rG8b0xnoihpLlzxI+dxlsHdhwPDAcxsUnkPyFQ12hdFtC+KaF8U0b4oYmaTyvvedA49LQa2TXjcGviqHMuIiEgWpTNRfAh0MLN2ZlYLGAiMLbbMWODUaPbT7sCP7r6k+IpERCR70jb05O7rzOxcYBxQA3jM3WeY2VnR68OAV4CDgbnAauD0GKsenqaQ85H2RRHtiyLaF0W0L4qUe1/k3Ql3IiKSWXlZFFBERDJHiUJERJLK2USRtvIfeSjGvjgp2gdTzew9M+uejTgzIdW+SFhuVzMrMLNjMhlfJsXZF2bW18ymmNkMM3s70zFmSoz/kcZm9qKZfRLtizjHQ/OOmT1mZt+Y2fRSXi9fu1nei22n80Y4+P058BugFvAJ0KXYMgcDrxLOxdgd+L9sx53FfbEnsEV0/6DqvC8SlnuTMFnimGzHncW/iybATKBN9HjLbMedxX1xFXBbdL8F8B1QK9uxp2Ff9AF2AaaX8nq52s1c7VGkpfxHnkq5L9z9PXf/Pnr4PuF8lKoozt8FwHnAv4FvMhlchsXZFycCz7r7QgB3r6r7I86+cKChhYtiNCAkinWZDTP93H0i4bOVplztZq4mitJKe5R1maqgrJ/zj4RvDFVRyn1hZtsARwHDMhhXNsT5u9gB2MLMJpjZR2Z2asaiy6w4++IBoDPhhN5pwAXuvj4z4eWUcrWbuXrhokor/1EFxP6cZrYfIVHsndaIsifOvrgHuNzdC6r4FdXi7IvNgZ5AP6Au8D8ze9/d56Q7uAyLsy8OBKYA+wPbA/8xs3fc/ad0B5djytVu5mqiUPmPIrE+p5l1A0YAB7n78gzFlmlx9kUvYHSUJJoDB5vZOnd/PjMhZkzc/5Fv3X0VsMrMJgLdgaqWKOLsi9OBWz0M1M81s/lAJ+CDzISYM8rVbubq0JPKfxRJuS/MrA3wLHBKFfy2mCjlvnD3du7e1t3bAmOAwVUwSUC8/5EXgH3MbHMzq0eo3jwrw3FmQpx9sZDQs8LMWhIqqc7LaJS5oVztZk72KDx95T/yTsx98RegGfBg9E16nVfBipkx90W1EGdfuPssM3sNmAqsB0a4e4nTJvNZzL+LIcBIM5tGGH653N2rXPlxMxsF9AWam9li4DqgJlSs3VQJDxERSSpXh55ERCRHKFGIiEhSShQiIpKUEoWIiCSlRCEiIkkpUUhOiiq/Tkm4tU2y7MpK2N5IM5sfbetjM9ujHOsYYWZdovtXFXvtvYrGGK2ncL9Mj6qhNkmxfA8zO7gyti3Vl6bHSk4ys5Xu3qCyl02yjpHAS+4+xsz6A3e6e7cKrK/CMaVar5k9Dsxx95uSLH8a0Mvdz63sWKT6UI9C8oKZNTCzN6Jv+9PMbJOqsWa2tZlNTPjGvU/0fH8z+1/03mfMLFUDPhFoH7334mhd083swui5+mb2cnRtg+lmdnz0/AQz62VmtwJ1ozieil5bGf38V+I3/Kgnc7SZ1TCzO8zsQwvXCTgzxm75H1FBNzPrbeFaJJOjnx2js5RvAI6PYjk+iv2xaDuTS9qPIpvIdv103XQr6QYUEIq4TQGeI1QRaBS91pxwZmlhj3hl9PMS4Orofg2gYbTsRKB+9PzlwF9K2N5IomtXAMcC/0coqDcNqE8oTT0D2Bk4Gngk4b2No58TCN/eN8SUsExhjEcBj0f3axEqedYFBgHXRM/XBiYB7UqIc2XC53sGGBA9bgRsHt3/LfDv6P5pwAMJ778ZODm634RQ96l+tn/fuuX2LSdLeIgAP7t7j8IHZlYTuNnM+hDKUWwDtASWJrznQ+CxaNnn3X2Kme0LdAHejcqb1CJ8Ey/JHWZ2DbCMUIW3H/Cch6J6mNmzwD7Aa8CdZnYbYbjqnTJ8rleB+8ysNjAAmOjuP0fDXd2s6Ip8jYEOwPxi769rZlOAtsBHwH8Sln/czDoQqoHWLGX7/YHDzezS6HEdoA1VswaUVBIlCskXJxGuTNbT3dea2QJCI7eBu0+MEskhwBNmdgfwPfAfdz8hxjYuc/cxhQ/M7LclLeTuc8ysJ6Fmzi1mNt7db4jzIdx9jZlNIJS9Ph4YVbg54Dx3H5diFT+7ew8zawy8BJwD3EeoZfSWux8VHfifUMr7DTja3WfHiVcEdIxC8kdj4JsoSewHbFd8ATPbLlrmEeBRwiUh3wf2MrPCYw71zGyHmNucCBwZvac+YdjoHTNrBax29yeBO6PtFLc26tmUZDShGNs+hEJ2RD/PLnyPme0QbbNE7v4jcD5wafSexsCX0cunJSy6gjAEV2gccJ5F3Ssz27m0bYgUUqKQfPEU0MvMJhF6F5+WsExfYIqZTSYcR7jX3ZcRGs5RZjaVkDg6xdmgu39MOHbxAeGYxQh3nwzsBHwQDQFdDdxYwtuHA1MLD2YXM55wbePXPVy6E8K1RGYCH5vZdOBhUvT4o1g+IZTVvp3Qu3mXcPyi0FtAl8KD2YSeR80otunRY5GkND1WRESSUo9CRESSUqIQEZGklChERCQpJQoREUlKiUJERJJSohARkaSUKEREJKn/BxB2jIFmO+zqAAAAAElFTkSuQmCC\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Confusion matrix\ncm =confusion_matrix(y_test,y_pred1)\n\nplt.clf()\nplt.imshow(cm, interpolation='nearest', cmap=plt.cm.Wistia)\nclassNames = ['Negative','Positive']\nplt.title('Confusion Matrix - Test Data')\nplt.ylabel('True label')\nplt.xlabel('Predicted label')\ntick_marks = np.arange(len(classNames))\nplt.xticks(tick_marks, classNames, rotation=45)\nplt.yticks(tick_marks, classNames)\ns = [['TN','FP'], ['FN', 'TP']]\n \nfor i in range(2):\n    for j in range(2):\n        plt.text(j,i, str(s[i][j])+\" = \"+str(cm[i][j]))\nplt.show()\n\nplot_roc_auc(y_test, y_pred1)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.475367Z","iopub.execute_input":"2023-07-11T19:44:43.475961Z","iopub.status.idle":"2023-07-11T19:44:43.706578Z","shell.execute_reply.started":"2023-07-11T19:44:43.475918Z","shell.execute_reply":"2023-07-11T19:44:43.705599Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":"# Confusion matrix\ncm =confusion_matrix(y_test,y_pred2)\n\nplt.clf()\nplt.imshow(cm, interpolation='nearest', cmap=plt.cm.Wistia)\nclassNames = ['Negative','Positive']\nplt.title('Confusion Matrix - Test Data')\nplt.ylabel('True label')\nplt.xlabel('Predicted label')\ntick_marks = np.arange(len(classNames))\nplt.xticks(tick_marks, classNames, rotation=45)\nplt.yticks(tick_marks, classNames)\ns = [['TN','FP'], ['FN', 'TP']]\n \nfor i in range(2):\n    for j in range(2):\n        plt.text(j,i, str(s[i][j])+\" = \"+str(cm[i][j]))\nplt.show()\n\n\nplot_roc_auc(y_test, y_pred2)","metadata":{"execution":{"iopub.status.busy":"2023-07-11T19:44:43.708467Z","iopub.execute_input":"2023-07-11T19:44:43.708922Z","iopub.status.idle":"2023-07-11T19:44:43.954405Z","shell.execute_reply.started":"2023-07-11T19:44:43.708883Z","shell.execute_reply":"2023-07-11T19:44:43.953459Z"},"trusted":true},"execution_count":24,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure size 432x288 with 1 Axes>","image/png":"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\n"},"metadata":{"needs_background":"light"}}]}]}