Random Forest & HyperparameterTuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b39bb20",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, RandomizedSearchCV\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6700bad4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('uci breast cancer dataset.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "225335cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "      <th>Unnamed: 32</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>842302</td>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.3001</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>...</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.1622</td>\n",
       "      <td>0.6656</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>842517</td>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.0869</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>...</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.1238</td>\n",
       "      <td>0.1866</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>84300903</td>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.1974</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>...</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.4245</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>84348301</td>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.2414</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>...</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.2098</td>\n",
       "      <td>0.8663</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84358402</td>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.1980</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>...</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.1374</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0    842302         M        17.99         10.38          122.80     1001.0   \n",
       "1    842517         M        20.57         17.77          132.90     1326.0   \n",
       "2  84300903         M        19.69         21.25          130.00     1203.0   \n",
       "3  84348301         M        11.42         20.38           77.58      386.1   \n",
       "4  84358402         M        20.29         14.34          135.10     1297.0   \n",
       "\n",
       "   smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0          0.11840           0.27760          0.3001              0.14710   \n",
       "1          0.08474           0.07864          0.0869              0.07017   \n",
       "2          0.10960           0.15990          0.1974              0.12790   \n",
       "3          0.14250           0.28390          0.2414              0.10520   \n",
       "4          0.10030           0.13280          0.1980              0.10430   \n",
       "\n",
       "   ...  texture_worst  perimeter_worst  area_worst  smoothness_worst  \\\n",
       "0  ...          17.33           184.60      2019.0            0.1622   \n",
       "1  ...          23.41           158.80      1956.0            0.1238   \n",
       "2  ...          25.53           152.50      1709.0            0.1444   \n",
       "3  ...          26.50            98.87       567.7            0.2098   \n",
       "4  ...          16.67           152.20      1575.0            0.1374   \n",
       "\n",
       "   compactness_worst  concavity_worst  concave points_worst  symmetry_worst  \\\n",
       "0             0.6656           0.7119                0.2654          0.4601   \n",
       "1             0.1866           0.2416                0.1860          0.2750   \n",
       "2             0.4245           0.4504                0.2430          0.3613   \n",
       "3             0.8663           0.6869                0.2575          0.6638   \n",
       "4             0.2050           0.4000                0.1625          0.2364   \n",
       "\n",
       "   fractal_dimension_worst  Unnamed: 32  \n",
       "0                  0.11890          NaN  \n",
       "1                  0.08902          NaN  \n",
       "2                  0.08758          NaN  \n",
       "3                  0.17300          NaN  \n",
       "4                  0.07678          NaN  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "12894ce6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 569 entries, 0 to 568\n",
      "Data columns (total 33 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   id                       569 non-null    int64  \n",
      " 1   diagnosis                569 non-null    object \n",
      " 2   radius_mean              569 non-null    float64\n",
      " 3   texture_mean             569 non-null    float64\n",
      " 4   perimeter_mean           569 non-null    float64\n",
      " 5   area_mean                569 non-null    float64\n",
      " 6   smoothness_mean          569 non-null    float64\n",
      " 7   compactness_mean         569 non-null    float64\n",
      " 8   concavity_mean           569 non-null    float64\n",
      " 9   concave points_mean      569 non-null    float64\n",
      " 10  symmetry_mean            569 non-null    float64\n",
      " 11  fractal_dimension_mean   569 non-null    float64\n",
      " 12  radius_se                569 non-null    float64\n",
      " 13  texture_se               569 non-null    float64\n",
      " 14  perimeter_se             569 non-null    float64\n",
      " 15  area_se                  569 non-null    float64\n",
      " 16  smoothness_se            569 non-null    float64\n",
      " 17  compactness_se           569 non-null    float64\n",
      " 18  concavity_se             569 non-null    float64\n",
      " 19  concave points_se        569 non-null    float64\n",
      " 20  symmetry_se              569 non-null    float64\n",
      " 21  fractal_dimension_se     569 non-null    float64\n",
      " 22  radius_worst             569 non-null    float64\n",
      " 23  texture_worst            569 non-null    float64\n",
      " 24  perimeter_worst          569 non-null    float64\n",
      " 25  area_worst               569 non-null    float64\n",
      " 26  smoothness_worst         569 non-null    float64\n",
      " 27  compactness_worst        569 non-null    float64\n",
      " 28  concavity_worst          569 non-null    float64\n",
      " 29  concave points_worst     569 non-null    float64\n",
      " 30  symmetry_worst           569 non-null    float64\n",
      " 31  fractal_dimension_worst  569 non-null    float64\n",
      " 32  Unnamed: 32              0 non-null      float64\n",
      "dtypes: float64(31), int64(1), object(1)\n",
      "memory usage: 146.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1cf57605",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['id','Unnamed: 32'], axis = 'columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6d027a92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>concave points_mean</th>\n",
       "      <th>symmetry_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>concave points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>fractal_dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
       "      <td>122.80</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0.11840</td>\n",
       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>...</td>\n",
       "      <td>25.380</td>\n",
       "      <td>17.33</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>...</td>\n",
       "      <td>24.990</td>\n",
       "      <td>23.41</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>...</td>\n",
       "      <td>23.570</td>\n",
       "      <td>25.53</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>...</td>\n",
       "      <td>14.910</td>\n",
       "      <td>26.50</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>...</td>\n",
       "      <td>22.540</td>\n",
       "      <td>16.67</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>M</td>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>...</td>\n",
       "      <td>25.450</td>\n",
       "      <td>26.40</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>M</td>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>...</td>\n",
       "      <td>23.690</td>\n",
       "      <td>38.25</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>M</td>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>...</td>\n",
       "      <td>18.980</td>\n",
       "      <td>34.12</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>M</td>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>...</td>\n",
       "      <td>25.740</td>\n",
       "      <td>39.42</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>B</td>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>...</td>\n",
       "      <td>9.456</td>\n",
       "      <td>30.37</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diagnosis  radius_mean  texture_mean  perimeter_mean  area_mean  \\\n",
       "0           M        17.99         10.38          122.80     1001.0   \n",
       "1           M        20.57         17.77          132.90     1326.0   \n",
       "2           M        19.69         21.25          130.00     1203.0   \n",
       "3           M        11.42         20.38           77.58      386.1   \n",
       "4           M        20.29         14.34          135.10     1297.0   \n",
       "..        ...          ...           ...             ...        ...   \n",
       "564         M        21.56         22.39          142.00     1479.0   \n",
       "565         M        20.13         28.25          131.20     1261.0   \n",
       "566         M        16.60         28.08          108.30      858.1   \n",
       "567         M        20.60         29.33          140.10     1265.0   \n",
       "568         B         7.76         24.54           47.92      181.0   \n",
       "\n",
       "     smoothness_mean  compactness_mean  concavity_mean  concave points_mean  \\\n",
       "0            0.11840           0.27760         0.30010              0.14710   \n",
       "1            0.08474           0.07864         0.08690              0.07017   \n",
       "2            0.10960           0.15990         0.19740              0.12790   \n",
       "3            0.14250           0.28390         0.24140              0.10520   \n",
       "4            0.10030           0.13280         0.19800              0.10430   \n",
       "..               ...               ...             ...                  ...   \n",
       "564          0.11100           0.11590         0.24390              0.13890   \n",
       "565          0.09780           0.10340         0.14400              0.09791   \n",
       "566          0.08455           0.10230         0.09251              0.05302   \n",
       "567          0.11780           0.27700         0.35140              0.15200   \n",
       "568          0.05263           0.04362         0.00000              0.00000   \n",
       "\n",
       "     symmetry_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0           0.2419  ...        25.380          17.33           184.60   \n",
       "1           0.1812  ...        24.990          23.41           158.80   \n",
       "2           0.2069  ...        23.570          25.53           152.50   \n",
       "3           0.2597  ...        14.910          26.50            98.87   \n",
       "4           0.1809  ...        22.540          16.67           152.20   \n",
       "..             ...  ...           ...            ...              ...   \n",
       "564         0.1726  ...        25.450          26.40           166.10   \n",
       "565         0.1752  ...        23.690          38.25           155.00   \n",
       "566         0.1590  ...        18.980          34.12           126.70   \n",
       "567         0.2397  ...        25.740          39.42           184.60   \n",
       "568         0.1587  ...         9.456          30.37            59.16   \n",
       "\n",
       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0        2019.0           0.16220            0.66560           0.7119   \n",
       "1        1956.0           0.12380            0.18660           0.2416   \n",
       "2        1709.0           0.14440            0.42450           0.4504   \n",
       "3         567.7           0.20980            0.86630           0.6869   \n",
       "4        1575.0           0.13740            0.20500           0.4000   \n",
       "..          ...               ...                ...              ...   \n",
       "564      2027.0           0.14100            0.21130           0.4107   \n",
       "565      1731.0           0.11660            0.19220           0.3215   \n",
       "566      1124.0           0.11390            0.30940           0.3403   \n",
       "567      1821.0           0.16500            0.86810           0.9387   \n",
       "568       268.6           0.08996            0.06444           0.0000   \n",
       "\n",
       "     concave points_worst  symmetry_worst  fractal_dimension_worst  \n",
       "0                  0.2654          0.4601                  0.11890  \n",
       "1                  0.1860          0.2750                  0.08902  \n",
       "2                  0.2430          0.3613                  0.08758  \n",
       "3                  0.2575          0.6638                  0.17300  \n",
       "4                  0.1625          0.2364                  0.07678  \n",
       "..                    ...             ...                      ...  \n",
       "564                0.2216          0.2060                  0.07115  \n",
       "565                0.1628          0.2572                  0.06637  \n",
       "566                0.1418          0.2218                  0.07820  \n",
       "567                0.2650          0.4087                  0.12400  \n",
       "568                0.0000          0.2871                  0.07039  \n",
       "\n",
       "[569 rows x 31 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "429ce428",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='diagnosis', ylabel='count'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='diagnosis',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8ffbc3e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "B    357\n",
       "M    212\n",
       "Name: diagnosis, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.diagnosis.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e728edef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create the countplot\n",
    "plt.figure(figsize=(8, 6))\n",
    "ax = sns.countplot(x='diagnosis', data=df, palette='Set2')\n",
    "\n",
    "# Annotate the bars with their respective counts\n",
    "for p in ax.patches:\n",
    "    height = p.get_height()\n",
    "    ax.annotate(f'{height}', (p.get_x() + p.get_width() / 2, height), ha='center', va='bottom', fontsize=12)\n",
    "\n",
    "# Optionally, you can set the y-axis label\n",
    "plt.ylabel('Count')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "047531b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('diagnosis',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "22cc8d64",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df[['diagnosis']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ccb92e",
   "metadata": {},
   "source": [
    "# Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bed4801b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x,y, random_state=42, test_size=.30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8f9e4006",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "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>diagnosis</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>171 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    diagnosis\n",
       "204         B\n",
       "70          M\n",
       "131         M\n",
       "431         B\n",
       "540         B\n",
       "..        ...\n",
       "69          B\n",
       "542         B\n",
       "176         B\n",
       "501         M\n",
       "247         B\n",
       "\n",
       "[171 rows x 1 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "5e7ecc58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 1)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ytest.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3ae5f71a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(171, 30)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtest.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f77c1153",
   "metadata": {},
   "source": [
    "# Step 3: Decision Tree model and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9a40377e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dt_model = DecisionTreeClassifier(random_state=42)\n",
    "dt_model.fit(xtrain, ytrain)\n",
    "y_pred_dt = dt_model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "73ea27d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'M', 'M', 'B', 'B', 'M', 'M', 'M', 'B', 'B', 'M', 'M', 'B',\n",
       "       'M', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
       "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'M', 'M',\n",
       "       'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'M', 'M', 'B', 'B',\n",
       "       'B', 'M', 'M', 'B', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'M',\n",
       "       'B', 'B', 'M', 'B', 'B', 'M', 'M', 'M', 'M', 'M', 'B', 'B', 'B',\n",
       "       'B', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'M', 'B', 'M', 'M',\n",
       "       'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'B',\n",
       "       'B', 'B', 'M', 'M', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B', 'M',\n",
       "       'M', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'B',\n",
       "       'B', 'M', 'B', 'M', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'M', 'M',\n",
       "       'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B',\n",
       "       'M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "132f51fb",
   "metadata": {},
   "source": [
    "# Evaluate Decision Tree model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e66bfd9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree Model Performance:\n",
      "Accuracy: 0.9415204678362573 \n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           B       0.97      0.94      0.95       108\n",
      "           M       0.90      0.95      0.92        63\n",
      "\n",
      "    accuracy                           0.94       171\n",
      "   macro avg       0.93      0.94      0.94       171\n",
      "weighted avg       0.94      0.94      0.94       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Decision Tree Model Performance:\")\n",
    "print(\"Accuracy:\", accuracy_score(ytest, y_pred_dt), '\\n')\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(ytest, y_pred_dt))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c95a47dd",
   "metadata": {},
   "source": [
    "# Step 4: Random Forest model and evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "79881ec8",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_model = RandomForestClassifier(random_state=42)\n",
    "rf_model.fit(xtrain, ytrain)\n",
    "y_pred_rf = rf_model.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7121a731",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'M', 'M', 'B', 'B', 'M', 'M', 'M', 'M', 'B', 'B', 'M', 'B',\n",
       "       'M', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
       "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'M',\n",
       "       'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'M', 'M', 'B', 'B',\n",
       "       'B', 'M', 'M', 'B', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'M', 'B', 'B', 'M', 'M', 'M', 'M', 'M', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'M', 'B', 'M', 'M',\n",
       "       'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'B',\n",
       "       'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B', 'M',\n",
       "       'M', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'M', 'B',\n",
       "       'B', 'M', 'B', 'M', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'M',\n",
       "       'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'M', 'B'], dtype=object)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76a4a6d4",
   "metadata": {},
   "source": [
    "# Evaluate Random Forest model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3a7e863d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Forest Model Performance:\n",
      "Accuracy: 0.9707602339181286 \n",
      "\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           B       0.96      0.99      0.98       108\n",
      "           M       0.98      0.94      0.96        63\n",
      "\n",
      "    accuracy                           0.97       171\n",
      "   macro avg       0.97      0.96      0.97       171\n",
      "weighted avg       0.97      0.97      0.97       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nRandom Forest Model Performance:\") \n",
    "print(\"Accuracy:\", accuracy_score(ytest, y_pred_rf), '\\n') \n",
    "print(\"Classification Report:\") \n",
    "print(classification_report(ytest, y_pred_rf)) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdccf755",
   "metadata": {},
   "source": [
    "# Pre - Pruning for Cost Complexity"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86855322",
   "metadata": {},
   "source": [
    "# Initialized Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3ca4674a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define hyperparameters for Decision Tree \n",
    "dt_params = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'splitter': ['best', 'random'],\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4]\n",
    "}\n",
    "\n",
    "\n",
    "#Define hyperparameters for Random Forest\n",
    "rf_params = {\n",
    "    'n_estimators': [100, 200, 300], #https://numpy.org/doc/stable/reference/random/generated/numpy.random.randint.html\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'max_depth': [None, 10, 20, 30],\n",
    "    'min_samples_split': [2, 5, 10],\n",
    "    'min_samples_leaf': [1, 2, 4],\n",
    "    'max_features': ['auto', 'sqrt', 'log2']\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e9db650d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 10, 20, 30],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform Grid Search for Decision Tree\n",
    "dt_grid_search = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
    "                              param_grid=dt_params, cv=5)\n",
    "dt_grid_search.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "86e05065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.004072  , 0.0020175 , 0.00377855, 0.00194745, 0.00373273,\n",
       "        0.00162997, 0.00402532, 0.00163641, 0.00381355, 0.00160341,\n",
       "        0.00375218, 0.00187564, 0.00365605, 0.00190868, 0.00374069,\n",
       "        0.00170689, 0.00367332, 0.00210142, 0.00409803, 0.00199685,\n",
       "        0.00426407, 0.00161805, 0.00401039, 0.0020052 , 0.00354304,\n",
       "        0.00150399, 0.00379715, 0.00180459, 0.00380654, 0.00160413,\n",
       "        0.00360799, 0.00182943, 0.00380864, 0.00180464, 0.0036087 ,\n",
       "        0.00200477, 0.00390959, 0.00180469, 0.00400953, 0.00180488,\n",
       "        0.00400939, 0.00190535, 0.00401001, 0.00180469, 0.00421052,\n",
       "        0.00180473, 0.00410972, 0.00180478, 0.00360894, 0.00180473,\n",
       "        0.00360909, 0.00180488, 0.00412216, 0.00167284, 0.00403848,\n",
       "        0.00180659, 0.00362492, 0.00191135, 0.00383563, 0.00160365,\n",
       "        0.0038228 , 0.00200534, 0.00411701, 0.00182161, 0.0036078 ,\n",
       "        0.00180464, 0.00360627, 0.0016036 , 0.00359254, 0.00200529,\n",
       "        0.0034709 , 0.001792  , 0.00422363, 0.0015914 , 0.00433059,\n",
       "        0.00200524, 0.00414248, 0.00187235, 0.00394864, 0.00160418,\n",
       "        0.0039978 , 0.00120301, 0.00441127, 0.00160365, 0.00431032,\n",
       "        0.00160499, 0.00421052, 0.00180497, 0.00420957, 0.00160394,\n",
       "        0.00461092, 0.00180478, 0.00462728, 0.00180483, 0.0043366 ,\n",
       "        0.00183873, 0.00460887, 0.00160403, 0.00422568, 0.00191174,\n",
       "        0.00422387, 0.00180478, 0.00402336, 0.00200763, 0.00411439,\n",
       "        0.00159593, 0.00400867, 0.00160413, 0.0042264 , 0.00160694,\n",
       "        0.00422368, 0.00172982, 0.00442419, 0.00155888, 0.0043201 ,\n",
       "        0.0016037 , 0.0042119 , 0.00180478, 0.00441437, 0.00151052,\n",
       "        0.00421729, 0.00200515, 0.00422325, 0.00165849, 0.00417185,\n",
       "        0.00180464, 0.00419893, 0.00200286, 0.00421257, 0.00170474,\n",
       "        0.00421119, 0.00180464, 0.00421095, 0.00180464, 0.00425487,\n",
       "        0.00160408, 0.00423713, 0.00180535, 0.00402665, 0.00170703,\n",
       "        0.00400968, 0.00177188, 0.00403519, 0.00160789]),\n",
       " 'std_fit_time': array([5.09940488e-04, 2.53532124e-05, 3.66747378e-04, 3.66328402e-04,\n",
       "        5.43409751e-04, 5.16136293e-04, 3.19319300e-05, 5.17296162e-04,\n",
       "        7.56611621e-04, 4.91284688e-04, 5.56140258e-04, 4.46427823e-04,\n",
       "        4.90400708e-04, 4.71365400e-04, 5.07275375e-04, 4.40749987e-04,\n",
       "        4.42285833e-04, 9.94139722e-05, 5.31777932e-04, 5.13673713e-04,\n",
       "        1.80942858e-04, 4.73690393e-04, 2.33601546e-07, 3.23406696e-07,\n",
       "        4.52887437e-04, 4.47929299e-04, 4.03553110e-04, 4.01020107e-04,\n",
       "        4.01920958e-04, 4.91186237e-04, 4.90953516e-04, 4.16245503e-04,\n",
       "        4.00972437e-04, 4.00924683e-04, 4.91342064e-04, 8.99694551e-07,\n",
       "        6.65016716e-04, 4.00948535e-04, 6.34143770e-04, 4.01163115e-04,\n",
       "        6.33993020e-04, 1.99627898e-04, 1.78416128e-07, 7.50329028e-04,\n",
       "        4.01115446e-04, 4.00972409e-04, 4.91517618e-04, 4.01115446e-04,\n",
       "        4.91341925e-04, 4.00972381e-04, 4.91264074e-04, 4.01163144e-04,\n",
       "        1.94242389e-04, 4.94384172e-04, 5.38386034e-04, 4.03156825e-04,\n",
       "        4.79870020e-04, 4.99027284e-04, 4.15542734e-04, 4.91965987e-04,\n",
       "        4.08094808e-04, 6.33992992e-04, 1.90734386e-04, 4.11706339e-04,\n",
       "        4.92359048e-04, 4.00924711e-04, 4.88418843e-04, 4.90661798e-04,\n",
       "        4.55054931e-04, 9.53674316e-08, 4.75880163e-04, 3.95508539e-04,\n",
       "        9.93212842e-04, 4.81360921e-04, 7.29965839e-04, 1.90734863e-07,\n",
       "        4.57790727e-04, 4.54262602e-04, 2.13895532e-04, 4.91127796e-04,\n",
       "        8.82724252e-04, 4.01163229e-04, 8.02135482e-04, 4.90603693e-04,\n",
       "        4.00878062e-04, 4.91791393e-04, 4.01234637e-04, 4.01330430e-04,\n",
       "        4.00161743e-04, 4.91712078e-04, 4.90972077e-04, 4.01115503e-04,\n",
       "        4.78432713e-04, 4.01020078e-04, 5.97141861e-04, 3.41503416e-04,\n",
       "        4.88554587e-04, 4.90913741e-04, 3.94951308e-04, 4.99602696e-04,\n",
       "        3.95328442e-04, 4.01115503e-04, 4.77774176e-05, 6.81656355e-05,\n",
       "        7.99149459e-04, 4.84907530e-04, 3.64897279e-06, 4.91088878e-04,\n",
       "        4.25276340e-04, 4.87662856e-04, 7.29982538e-04, 3.91446471e-04,\n",
       "        4.81605284e-04, 4.61674948e-04, 4.06851910e-04, 4.90836993e-04,\n",
       "        4.00784170e-04, 4.01115446e-04, 4.89089049e-04, 4.48596999e-04,\n",
       "        3.99394420e-04, 9.53674316e-08, 4.26938024e-04, 5.44656803e-04,\n",
       "        2.28311952e-04, 4.01043931e-04, 7.37605228e-04, 6.64260005e-06,\n",
       "        7.49940422e-04, 3.97224769e-04, 4.01139270e-04, 4.01043903e-04,\n",
       "        4.01139298e-04, 4.00924711e-04, 3.80272100e-04, 4.91244643e-04,\n",
       "        3.88917998e-04, 4.01284304e-04, 2.43966406e-05, 3.95810097e-04,\n",
       "        1.18155591e-06, 3.86449921e-04, 6.34784753e-04, 4.94307439e-04]),\n",
       " 'mean_score_time': array([0.00100188, 0.0008007 , 0.000806  , 0.000841  , 0.00071459,\n",
       "        0.00061936, 0.00060115, 0.00080538, 0.0006012 , 0.0008018 ,\n",
       "        0.00080452, 0.00107527, 0.00082741, 0.00080357, 0.00080109,\n",
       "        0.00070429, 0.00089188, 0.00105686, 0.00061245, 0.00080795,\n",
       "        0.00085621, 0.00080256, 0.00080218, 0.00060158, 0.00080175,\n",
       "        0.00060105, 0.0006144 , 0.00060492, 0.00061455, 0.00080204,\n",
       "        0.00100369, 0.00060172, 0.00100369, 0.00060163, 0.00100355,\n",
       "        0.00060153, 0.00080276, 0.00080214, 0.0008028 , 0.00060153,\n",
       "        0.00080214, 0.00080147, 0.00080252, 0.00060158, 0.00060191,\n",
       "        0.00080214, 0.00080228, 0.00100255, 0.00040116, 0.00080209,\n",
       "        0.00100298, 0.00080128, 0.00081911, 0.00080385, 0.00042605,\n",
       "        0.0006011 , 0.00060163, 0.00060158, 0.00077639, 0.00060225,\n",
       "        0.00078926, 0.00080194, 0.00060158, 0.00081382, 0.00080171,\n",
       "        0.00060172, 0.00060477, 0.00060472, 0.00072546, 0.00060153,\n",
       "        0.00095339, 0.00060167, 0.0006043 , 0.00081482, 0.00058866,\n",
       "        0.00060163, 0.00087156, 0.00053391, 0.00061712, 0.00080209,\n",
       "        0.00061426, 0.00087705, 0.00060177, 0.00060143, 0.00060191,\n",
       "        0.00080132, 0.00060191, 0.00100265, 0.0010036 , 0.00080204,\n",
       "        0.00080299, 0.00060148, 0.00099053, 0.00060153, 0.00098062,\n",
       "        0.00057092, 0.00080209, 0.00060153, 0.00079017, 0.00060163,\n",
       "        0.00098977, 0.00060158, 0.00098977, 0.00080242, 0.00080509,\n",
       "        0.00101085, 0.00100451, 0.00087161, 0.00099001, 0.00065351,\n",
       "        0.00078945, 0.00087705, 0.00099001, 0.00064683, 0.00020118,\n",
       "        0.00101523, 0.00100241, 0.0010026 , 0.00080199, 0.00067167,\n",
       "        0.00079594, 0.00060163, 0.00078993, 0.00095105, 0.00096092,\n",
       "        0.00060163, 0.00081353, 0.00060463, 0.00080328, 0.00080714,\n",
       "        0.0010025 , 0.00060158, 0.00100269, 0.00080214, 0.00086794,\n",
       "        0.00080218, 0.00057549, 0.00080147, 0.00040107, 0.00060163,\n",
       "        0.00061498, 0.0006217 , 0.00038834, 0.00079837]),\n",
       " 'std_score_time': array([1.10189629e-06, 4.00352506e-04, 4.03117372e-04, 4.24418759e-04,\n",
       "        5.99384391e-04, 5.06849871e-04, 4.90835928e-04, 4.02765495e-04,\n",
       "        4.90875510e-04, 4.00900908e-04, 4.02288512e-04, 1.05885339e-04,\n",
       "        4.16728031e-04, 4.01813774e-04, 4.00543412e-04, 6.03295609e-04,\n",
       "        4.53242320e-04, 1.05572371e-04, 5.00298887e-04, 4.04212314e-04,\n",
       "        4.39906233e-04, 4.01303200e-04, 4.01091615e-04, 4.91186237e-04,\n",
       "        4.00877155e-04, 4.90759219e-04, 5.02197551e-04, 4.93947011e-04,\n",
       "        5.02308252e-04, 4.01020050e-04, 3.16297988e-07, 4.91303015e-04,\n",
       "        3.16297988e-07, 4.91225125e-04, 6.97552626e-07, 4.91147273e-04,\n",
       "        4.01377689e-04, 4.01067748e-04, 4.01401548e-04, 4.91147250e-04,\n",
       "        4.01069619e-04, 4.00735480e-04, 4.01258511e-04, 4.91186191e-04,\n",
       "        4.91458749e-04, 4.01067776e-04, 4.01139610e-04, 1.50789149e-07,\n",
       "        4.91322457e-04, 4.01043903e-04, 1.78416128e-07, 4.00640879e-04,\n",
       "        4.10530336e-04, 4.01968990e-04, 5.23422782e-04, 4.90797704e-04,\n",
       "        4.92878401e-04, 4.91186191e-04, 3.89251305e-04, 4.91733011e-04,\n",
       "        3.95413331e-04, 4.00972381e-04, 4.91186191e-04, 4.07655993e-04,\n",
       "        4.00854160e-04, 4.91302992e-04, 4.93831807e-04, 4.93791818e-04,\n",
       "        3.81400740e-04, 4.91147273e-04, 9.92990227e-05, 4.91264074e-04,\n",
       "        4.93429921e-04, 4.08160592e-04, 4.81203261e-04, 4.91225125e-04,\n",
       "        4.56067724e-04, 4.53116943e-04, 5.04674739e-04, 4.01043903e-04,\n",
       "        5.02074853e-04, 4.61875105e-04, 4.91341925e-04, 4.91069390e-04,\n",
       "        4.91458726e-04, 4.00664845e-04, 4.91458726e-04, 1.16800773e-07,\n",
       "        1.16800773e-07, 4.01020078e-04, 4.01496887e-04, 4.91108370e-04,\n",
       "        2.59438855e-05, 4.91147273e-04, 2.69701896e-05, 4.69506167e-04,\n",
       "        4.01043931e-04, 4.91147273e-04, 3.95888588e-04, 4.91225125e-04,\n",
       "        2.55589165e-05, 4.91186191e-04, 4.78155222e-05, 4.06921069e-04,\n",
       "        4.02582388e-04, 1.63558621e-05, 3.79197986e-06, 4.56081379e-04,\n",
       "        2.55589165e-05, 5.41957180e-04, 3.95508510e-04, 4.61875105e-04,\n",
       "        2.55589165e-05, 5.34550533e-04, 4.02355194e-04, 2.56936923e-05,\n",
       "        1.16800773e-07, 1.78416128e-07, 4.00996219e-04, 5.63201922e-04,\n",
       "        3.99263090e-04, 4.91225125e-04, 3.95767786e-04, 1.10035586e-04,\n",
       "        9.05705206e-05, 4.91225125e-04, 4.07571422e-04, 4.93711884e-04,\n",
       "        4.01700579e-04, 4.03621307e-04, 9.53674316e-08, 4.91186191e-04,\n",
       "        1.90734863e-07, 4.01067748e-04, 4.89511389e-04, 4.01091586e-04,\n",
       "        4.70401377e-04, 4.00735849e-04, 4.91205656e-04, 4.91225171e-04,\n",
       "        5.02635756e-04, 5.08940757e-04, 4.76041760e-04, 3.99249764e-04]),\n",
       " 'param_criterion': masked_array(data=['gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_max_depth': masked_array(data=[None, None, None, None, None, None, None, None, None,\n",
       "                    None, None, None, None, None, None, None, None, None,\n",
       "                    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
       "                    10, 10, 10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n",
       "                    20, 20, 20, 20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30,\n",
       "                    30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, None,\n",
       "                    None, None, None, None, None, None, None, None, None,\n",
       "                    None, None, None, None, None, None, None, None, 10, 10,\n",
       "                    10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,\n",
       "                    10, 10, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20,\n",
       "                    20, 20, 20, 20, 20, 20, 30, 30, 30, 30, 30, 30, 30, 30,\n",
       "                    30, 30, 30, 30, 30, 30, 30, 30, 30, 30],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_min_samples_leaf': masked_array(data=[1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4,\n",
       "                    1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4,\n",
       "                    1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4,\n",
       "                    1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4,\n",
       "                    1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4,\n",
       "                    1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4,\n",
       "                    1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4,\n",
       "                    1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 4, 4, 4, 4, 4, 4],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_min_samples_split': masked_array(data=[2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10,\n",
       "                    10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5,\n",
       "                    10, 10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10, 2, 2,\n",
       "                    5, 5, 10, 10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10,\n",
       "                    2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10,\n",
       "                    10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5,\n",
       "                    10, 10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10, 2, 2,\n",
       "                    5, 5, 10, 10, 2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10,\n",
       "                    2, 2, 5, 5, 10, 10, 2, 2, 5, 5, 10, 10],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_splitter': masked_array(data=['best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random',\n",
       "                    'best', 'random', 'best', 'random', 'best', 'random'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'gini',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': None,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
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       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
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       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
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       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
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       "   'min_samples_split': 5,\n",
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       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
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       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
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       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
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       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 20,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 1,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 2,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 2,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 5,\n",
       "   'splitter': 'random'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'best'},\n",
       "  {'criterion': 'entropy',\n",
       "   'max_depth': 30,\n",
       "   'min_samples_leaf': 4,\n",
       "   'min_samples_split': 10,\n",
       "   'splitter': 'random'}],\n",
       " 'split0_test_score': array([0.9125, 0.9375, 0.9125, 0.925 , 0.9375, 0.9375, 0.9125, 0.875 ,\n",
       "        0.9125, 0.9375, 0.9375, 0.925 , 0.95  , 0.925 , 0.95  , 0.925 ,\n",
       "        0.9375, 0.925 , 0.9125, 0.9375, 0.9125, 0.925 , 0.9375, 0.9375,\n",
       "        0.9125, 0.875 , 0.9125, 0.9375, 0.9375, 0.925 , 0.95  , 0.925 ,\n",
       "        0.95  , 0.925 , 0.9375, 0.925 , 0.9125, 0.9375, 0.9125, 0.925 ,\n",
       "        0.9375, 0.9375, 0.9125, 0.875 , 0.9125, 0.9375, 0.9375, 0.925 ,\n",
       "        0.95  , 0.925 , 0.95  , 0.925 , 0.9375, 0.925 , 0.9125, 0.9375,\n",
       "        0.9125, 0.925 , 0.9375, 0.9375, 0.9125, 0.875 , 0.9125, 0.9375,\n",
       "        0.9375, 0.925 , 0.95  , 0.925 , 0.95  , 0.925 , 0.9375, 0.925 ,\n",
       "        0.95  , 0.925 , 0.95  , 0.925 , 0.9375, 0.925 , 0.95  , 0.9375,\n",
       "        0.95  , 0.925 , 0.9375, 0.925 , 0.95  , 0.9125, 0.95  , 0.9125,\n",
       "        0.925 , 0.9125, 0.95  , 0.925 , 0.95  , 0.925 , 0.9375, 0.925 ,\n",
       "        0.95  , 0.9375, 0.95  , 0.925 , 0.9375, 0.925 , 0.95  , 0.9125,\n",
       "        0.95  , 0.9125, 0.925 , 0.9125, 0.95  , 0.925 , 0.95  , 0.925 ,\n",
       "        0.9375, 0.925 , 0.95  , 0.9375, 0.95  , 0.925 , 0.9375, 0.925 ,\n",
       "        0.95  , 0.9125, 0.95  , 0.9125, 0.925 , 0.9125, 0.95  , 0.925 ,\n",
       "        0.95  , 0.925 , 0.9375, 0.925 , 0.95  , 0.9375, 0.95  , 0.925 ,\n",
       "        0.9375, 0.925 , 0.95  , 0.9125, 0.95  , 0.9125, 0.925 , 0.9125]),\n",
       " 'split1_test_score': array([0.8875, 0.9   , 0.8875, 0.8875, 0.8875, 0.8875, 0.9125, 0.875 ,\n",
       "        0.9125, 0.9   , 0.9   , 0.8875, 0.9125, 0.875 , 0.9125, 0.875 ,\n",
       "        0.8875, 0.875 , 0.8875, 0.9   , 0.8875, 0.8875, 0.8875, 0.8875,\n",
       "        0.9125, 0.875 , 0.9125, 0.9   , 0.9   , 0.8875, 0.9125, 0.875 ,\n",
       "        0.9125, 0.875 , 0.8875, 0.875 , 0.8875, 0.9   , 0.8875, 0.8875,\n",
       "        0.8875, 0.8875, 0.9125, 0.875 , 0.9125, 0.9   , 0.9   , 0.8875,\n",
       "        0.9125, 0.875 , 0.9125, 0.875 , 0.8875, 0.875 , 0.8875, 0.9   ,\n",
       "        0.8875, 0.8875, 0.8875, 0.8875, 0.9125, 0.875 , 0.9125, 0.9   ,\n",
       "        0.9   , 0.8875, 0.9125, 0.875 , 0.9125, 0.875 , 0.8875, 0.875 ,\n",
       "        0.9   , 0.8875, 0.8875, 0.8875, 0.8875, 0.8875, 0.9   , 0.8875,\n",
       "        0.8875, 0.8875, 0.8875, 0.8875, 0.8875, 0.875 , 0.8875, 0.875 ,\n",
       "        0.8875, 0.875 , 0.9   , 0.8875, 0.8875, 0.8875, 0.8875, 0.8875,\n",
       "        0.9   , 0.8875, 0.8875, 0.8875, 0.8875, 0.8875, 0.8875, 0.875 ,\n",
       "        0.8875, 0.875 , 0.8875, 0.875 , 0.9   , 0.8875, 0.8875, 0.8875,\n",
       "        0.8875, 0.8875, 0.9   , 0.8875, 0.8875, 0.8875, 0.8875, 0.8875,\n",
       "        0.8875, 0.875 , 0.8875, 0.875 , 0.8875, 0.875 , 0.9   , 0.8875,\n",
       "        0.8875, 0.8875, 0.8875, 0.8875, 0.9   , 0.8875, 0.8875, 0.8875,\n",
       "        0.8875, 0.8875, 0.8875, 0.875 , 0.8875, 0.875 , 0.8875, 0.875 ]),\n",
       " 'split2_test_score': array([0.9   , 0.9625, 0.925 , 0.9625, 0.925 , 0.95  , 0.925 , 0.9625,\n",
       "        0.9375, 0.9625, 0.9125, 0.9625, 0.925 , 0.9375, 0.925 , 0.9375,\n",
       "        0.925 , 0.95  , 0.9   , 0.9625, 0.925 , 0.9625, 0.925 , 0.95  ,\n",
       "        0.925 , 0.9625, 0.9375, 0.9625, 0.9125, 0.9625, 0.925 , 0.9375,\n",
       "        0.925 , 0.9375, 0.925 , 0.95  , 0.9   , 0.9625, 0.925 , 0.9625,\n",
       "        0.925 , 0.95  , 0.925 , 0.9625, 0.9375, 0.9625, 0.9125, 0.9625,\n",
       "        0.925 , 0.9375, 0.925 , 0.9375, 0.925 , 0.95  , 0.9   , 0.9625,\n",
       "        0.925 , 0.9625, 0.925 , 0.95  , 0.925 , 0.9625, 0.9375, 0.9625,\n",
       "        0.9125, 0.9625, 0.925 , 0.9375, 0.925 , 0.9375, 0.925 , 0.95  ,\n",
       "        0.95  , 0.925 , 0.95  , 0.9375, 0.975 , 0.9375, 0.95  , 0.8875,\n",
       "        0.95  , 0.9125, 0.975 , 0.95  , 0.9625, 0.9625, 0.9625, 0.9625,\n",
       "        0.9625, 0.925 , 0.95  , 0.925 , 0.95  , 0.9375, 0.975 , 0.9375,\n",
       "        0.95  , 0.8875, 0.95  , 0.9125, 0.975 , 0.95  , 0.9625, 0.9625,\n",
       "        0.9625, 0.9625, 0.9625, 0.925 , 0.95  , 0.925 , 0.95  , 0.9375,\n",
       "        0.975 , 0.9375, 0.95  , 0.8875, 0.95  , 0.9125, 0.975 , 0.95  ,\n",
       "        0.9625, 0.9625, 0.9625, 0.9625, 0.9625, 0.925 , 0.95  , 0.925 ,\n",
       "        0.95  , 0.9375, 0.975 , 0.9375, 0.95  , 0.8875, 0.95  , 0.9125,\n",
       "        0.975 , 0.95  , 0.9625, 0.9625, 0.9625, 0.9625, 0.9625, 0.925 ]),\n",
       " 'split3_test_score': array([0.93670886, 0.91139241, 0.93670886, 0.92405063, 0.91139241,\n",
       "        0.93670886, 0.94936709, 0.93670886, 0.94936709, 0.91139241,\n",
       "        0.92405063, 0.93670886, 0.94936709, 0.93670886, 0.94936709,\n",
       "        0.93670886, 0.92405063, 0.94936709, 0.93670886, 0.91139241,\n",
       "        0.93670886, 0.92405063, 0.91139241, 0.93670886, 0.94936709,\n",
       "        0.93670886, 0.94936709, 0.91139241, 0.92405063, 0.93670886,\n",
       "        0.94936709, 0.93670886, 0.94936709, 0.93670886, 0.92405063,\n",
       "        0.94936709, 0.93670886, 0.91139241, 0.93670886, 0.92405063,\n",
       "        0.91139241, 0.93670886, 0.94936709, 0.93670886, 0.94936709,\n",
       "        0.91139241, 0.92405063, 0.93670886, 0.94936709, 0.93670886,\n",
       "        0.94936709, 0.93670886, 0.92405063, 0.94936709, 0.93670886,\n",
       "        0.91139241, 0.93670886, 0.92405063, 0.91139241, 0.93670886,\n",
       "        0.94936709, 0.93670886, 0.94936709, 0.91139241, 0.92405063,\n",
       "        0.93670886, 0.94936709, 0.93670886, 0.94936709, 0.93670886,\n",
       "        0.92405063, 0.94936709, 0.93670886, 0.96202532, 0.93670886,\n",
       "        0.89873418, 0.94936709, 0.93670886, 0.93670886, 0.93670886,\n",
       "        0.93670886, 0.89873418, 0.94936709, 0.93670886, 0.94936709,\n",
       "        0.94936709, 0.94936709, 0.94936709, 0.94936709, 0.93670886,\n",
       "        0.93670886, 0.96202532, 0.93670886, 0.89873418, 0.94936709,\n",
       "        0.93670886, 0.93670886, 0.93670886, 0.93670886, 0.89873418,\n",
       "        0.94936709, 0.93670886, 0.94936709, 0.94936709, 0.94936709,\n",
       "        0.94936709, 0.94936709, 0.93670886, 0.93670886, 0.96202532,\n",
       "        0.93670886, 0.89873418, 0.94936709, 0.93670886, 0.93670886,\n",
       "        0.93670886, 0.93670886, 0.89873418, 0.94936709, 0.93670886,\n",
       "        0.94936709, 0.94936709, 0.94936709, 0.94936709, 0.94936709,\n",
       "        0.93670886, 0.93670886, 0.96202532, 0.93670886, 0.89873418,\n",
       "        0.94936709, 0.93670886, 0.93670886, 0.93670886, 0.93670886,\n",
       "        0.89873418, 0.94936709, 0.93670886, 0.94936709, 0.94936709,\n",
       "        0.94936709, 0.94936709, 0.94936709, 0.93670886]),\n",
       " 'split4_test_score': array([0.91139241, 0.94936709, 0.93670886, 0.96202532, 0.92405063,\n",
       "        0.93670886, 0.88607595, 0.91139241, 0.92405063, 0.91139241,\n",
       "        0.92405063, 0.93670886, 0.92405063, 0.86075949, 0.92405063,\n",
       "        0.86075949, 0.92405063, 0.86075949, 0.91139241, 0.94936709,\n",
       "        0.93670886, 0.96202532, 0.92405063, 0.93670886, 0.88607595,\n",
       "        0.91139241, 0.92405063, 0.91139241, 0.92405063, 0.93670886,\n",
       "        0.92405063, 0.86075949, 0.92405063, 0.86075949, 0.92405063,\n",
       "        0.86075949, 0.91139241, 0.94936709, 0.93670886, 0.96202532,\n",
       "        0.92405063, 0.93670886, 0.88607595, 0.91139241, 0.92405063,\n",
       "        0.91139241, 0.92405063, 0.93670886, 0.92405063, 0.86075949,\n",
       "        0.92405063, 0.86075949, 0.92405063, 0.86075949, 0.91139241,\n",
       "        0.94936709, 0.93670886, 0.96202532, 0.92405063, 0.93670886,\n",
       "        0.88607595, 0.91139241, 0.92405063, 0.91139241, 0.92405063,\n",
       "        0.93670886, 0.92405063, 0.86075949, 0.92405063, 0.86075949,\n",
       "        0.92405063, 0.86075949, 0.91139241, 0.91139241, 0.92405063,\n",
       "        0.89873418, 0.92405063, 0.93670886, 0.93670886, 0.87341772,\n",
       "        0.93670886, 0.87341772, 0.93670886, 0.93670886, 0.92405063,\n",
       "        0.93670886, 0.92405063, 0.93670886, 0.92405063, 0.86075949,\n",
       "        0.91139241, 0.91139241, 0.92405063, 0.89873418, 0.92405063,\n",
       "        0.93670886, 0.93670886, 0.87341772, 0.93670886, 0.87341772,\n",
       "        0.93670886, 0.93670886, 0.92405063, 0.93670886, 0.92405063,\n",
       "        0.93670886, 0.92405063, 0.86075949, 0.91139241, 0.91139241,\n",
       "        0.92405063, 0.89873418, 0.92405063, 0.93670886, 0.93670886,\n",
       "        0.87341772, 0.93670886, 0.87341772, 0.93670886, 0.93670886,\n",
       "        0.92405063, 0.93670886, 0.92405063, 0.93670886, 0.92405063,\n",
       "        0.86075949, 0.91139241, 0.91139241, 0.92405063, 0.89873418,\n",
       "        0.92405063, 0.93670886, 0.93670886, 0.87341772, 0.93670886,\n",
       "        0.87341772, 0.93670886, 0.93670886, 0.92405063, 0.93670886,\n",
       "        0.92405063, 0.93670886, 0.92405063, 0.86075949]),\n",
       " 'mean_test_score': array([0.90962025, 0.9321519 , 0.91968354, 0.93221519, 0.91708861,\n",
       "        0.92968354, 0.91708861, 0.91212025, 0.92718354, 0.92455696,\n",
       "        0.91962025, 0.92968354, 0.93218354, 0.90699367, 0.93218354,\n",
       "        0.90699367, 0.91962025, 0.91202532, 0.90962025, 0.9321519 ,\n",
       "        0.91968354, 0.93221519, 0.91708861, 0.92968354, 0.91708861,\n",
       "        0.91212025, 0.92718354, 0.92455696, 0.91962025, 0.92968354,\n",
       "        0.93218354, 0.90699367, 0.93218354, 0.90699367, 0.91962025,\n",
       "        0.91202532, 0.90962025, 0.9321519 , 0.91968354, 0.93221519,\n",
       "        0.91708861, 0.92968354, 0.91708861, 0.91212025, 0.92718354,\n",
       "        0.92455696, 0.91962025, 0.92968354, 0.93218354, 0.90699367,\n",
       "        0.93218354, 0.90699367, 0.91962025, 0.91202532, 0.90962025,\n",
       "        0.9321519 , 0.91968354, 0.93221519, 0.91708861, 0.92968354,\n",
       "        0.91708861, 0.91212025, 0.92718354, 0.92455696, 0.91962025,\n",
       "        0.92968354, 0.93218354, 0.90699367, 0.93218354, 0.90699367,\n",
       "        0.91962025, 0.91202532, 0.92962025, 0.92218354, 0.9296519 ,\n",
       "        0.90949367, 0.93468354, 0.92468354, 0.93468354, 0.90452532,\n",
       "        0.93218354, 0.89943038, 0.93721519, 0.92718354, 0.93468354,\n",
       "        0.92721519, 0.93468354, 0.92721519, 0.92968354, 0.90199367,\n",
       "        0.92962025, 0.92218354, 0.9296519 , 0.90949367, 0.93468354,\n",
       "        0.92468354, 0.93468354, 0.90452532, 0.93218354, 0.89943038,\n",
       "        0.93721519, 0.92718354, 0.93468354, 0.92721519, 0.93468354,\n",
       "        0.92721519, 0.92968354, 0.90199367, 0.92962025, 0.92218354,\n",
       "        0.9296519 , 0.90949367, 0.93468354, 0.92468354, 0.93468354,\n",
       "        0.90452532, 0.93218354, 0.89943038, 0.93721519, 0.92718354,\n",
       "        0.93468354, 0.92721519, 0.93468354, 0.92721519, 0.92968354,\n",
       "        0.90199367, 0.92962025, 0.92218354, 0.9296519 , 0.90949367,\n",
       "        0.93468354, 0.92468354, 0.93468354, 0.90452532, 0.93218354,\n",
       "        0.89943038, 0.93721519, 0.92718354, 0.93468354, 0.92721519,\n",
       "        0.93468354, 0.92721519, 0.92968354, 0.90199367]),\n",
       " 'std_test_score': array([0.0162916 , 0.02328379, 0.01841397, 0.02801421, 0.01694472,\n",
       "        0.02168878, 0.02053478, 0.03434843, 0.0144192 , 0.0226135 ,\n",
       "        0.0126063 , 0.02440089, 0.01495252, 0.03255473, 0.01495252,\n",
       "        0.03255473, 0.01684989, 0.03742696, 0.0162916 , 0.02328379,\n",
       "        0.01841397, 0.02801421, 0.01694472, 0.02168878, 0.02053478,\n",
       "        0.03434843, 0.0144192 , 0.0226135 , 0.0126063 , 0.02440089,\n",
       "        0.01495252, 0.03255473, 0.01495252, 0.03255473, 0.01684989,\n",
       "        0.03742696, 0.0162916 , 0.02328379, 0.01841397, 0.02801421,\n",
       "        0.01694472, 0.02168878, 0.02053478, 0.03434843, 0.0144192 ,\n",
       "        0.0226135 , 0.0126063 , 0.02440089, 0.01495252, 0.03255473,\n",
       "        0.01495252, 0.03255473, 0.01684989, 0.03742696, 0.0162916 ,\n",
       "        0.02328379, 0.01841397, 0.02801421, 0.01694472, 0.02168878,\n",
       "        0.02053478, 0.03434843, 0.0144192 , 0.0226135 , 0.0126063 ,\n",
       "        0.02440089, 0.01495252, 0.03255473, 0.01495252, 0.03255473,\n",
       "        0.01684989, 0.03742696, 0.02044764, 0.02417697, 0.02317778,\n",
       "        0.01865506, 0.02894236, 0.01916348, 0.01833216, 0.02709434,\n",
       "        0.02311895, 0.01813338, 0.028451  , 0.02136167, 0.0266957 ,\n",
       "        0.03088306, 0.0266957 , 0.03088306, 0.02568065, 0.02923605,\n",
       "        0.02044764, 0.02417697, 0.02317778, 0.01865506, 0.02894236,\n",
       "        0.01916348, 0.01833216, 0.02709434, 0.02311895, 0.01813338,\n",
       "        0.028451  , 0.02136167, 0.0266957 , 0.03088306, 0.0266957 ,\n",
       "        0.03088306, 0.02568065, 0.02923605, 0.02044764, 0.02417697,\n",
       "        0.02317778, 0.01865506, 0.02894236, 0.01916348, 0.01833216,\n",
       "        0.02709434, 0.02311895, 0.01813338, 0.028451  , 0.02136167,\n",
       "        0.0266957 , 0.03088306, 0.0266957 , 0.03088306, 0.02568065,\n",
       "        0.02923605, 0.02044764, 0.02417697, 0.02317778, 0.01865506,\n",
       "        0.02894236, 0.01916348, 0.01833216, 0.02709434, 0.02311895,\n",
       "        0.01813338, 0.028451  , 0.02136167, 0.0266957 , 0.03088306,\n",
       "        0.0266957 , 0.03088306, 0.02568065, 0.02923605]),\n",
       " 'rank_test_score': array([117,  37,  89,  21, 101,  41, 101, 109,  69,  81,  93,  41,  29,\n",
       "        125,  29, 125,  93, 113, 117,  37,  89,  21, 101,  41, 101, 109,\n",
       "         69,  81,  93,  41,  29, 125,  29, 125,  93, 113, 117,  37,  89,\n",
       "         21, 101,  41, 101, 109,  69,  81,  93,  41,  29, 125,  29, 125,\n",
       "         93, 113, 117,  37,  89,  21, 101,  41, 101, 109,  69,  81,  93,\n",
       "         41,  29, 125,  29, 125,  93, 113,  57,  85,  53, 121,   5,  77,\n",
       "          5, 133,  25, 141,   1,  69,   5,  61,   5,  61,  41, 137,  57,\n",
       "         85,  53, 121,   5,  77,   5, 133,  25, 141,   1,  69,   5,  61,\n",
       "          5,  61,  41, 137,  57,  85,  53, 121,   5,  77,   5, 133,  25,\n",
       "        141,   1,  69,   5,  61,   5,  61,  41, 137,  57,  85,  53, 121,\n",
       "          5,  77,   5, 133,  25, 141,   1,  69,   5,  61,   5,  61,  41,\n",
       "        137])}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_grid_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0d36cacf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_splitter</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.004072</td>\n",
       "      <td>0.000510</td>\n",
       "      <td>0.001002</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.909620</td>\n",
       "      <td>0.016292</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002017</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000801</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.932152</td>\n",
       "      <td>0.023284</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.003779</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000806</td>\n",
       "      <td>0.000403</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.919684</td>\n",
       "      <td>0.018414</td>\n",
       "      <td>89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001947</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.932215</td>\n",
       "      <td>0.028014</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003733</td>\n",
       "      <td>0.000543</td>\n",
       "      <td>0.000715</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>gini</td>\n",
       "      <td>None</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': None, 'min_...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.917089</td>\n",
       "      <td>0.016945</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>0.001707</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>entropy</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 30, 'min...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.927215</td>\n",
       "      <td>0.030883</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>0.004010</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.000615</td>\n",
       "      <td>0.000503</td>\n",
       "      <td>entropy</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 30, 'min...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.934684</td>\n",
       "      <td>0.026696</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>0.001772</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>0.000509</td>\n",
       "      <td>entropy</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 30, 'min...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.927215</td>\n",
       "      <td>0.030883</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>0.004035</td>\n",
       "      <td>0.000635</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>0.000476</td>\n",
       "      <td>entropy</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 30, 'min...</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.929684</td>\n",
       "      <td>0.025681</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>0.001608</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.000798</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>entropy</td>\n",
       "      <td>30</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 30, 'min...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.8750</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.860759</td>\n",
       "      <td>0.901994</td>\n",
       "      <td>0.029236</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>144 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0         0.004072      0.000510         0.001002        0.000001   \n",
       "1         0.002017      0.000025         0.000801        0.000400   \n",
       "2         0.003779      0.000367         0.000806        0.000403   \n",
       "3         0.001947      0.000366         0.000841        0.000424   \n",
       "4         0.003733      0.000543         0.000715        0.000599   \n",
       "..             ...           ...              ...             ...   \n",
       "139       0.001707      0.000396         0.000602        0.000491   \n",
       "140       0.004010      0.000001         0.000615        0.000503   \n",
       "141       0.001772      0.000386         0.000622        0.000509   \n",
       "142       0.004035      0.000635         0.000388        0.000476   \n",
       "143       0.001608      0.000494         0.000798        0.000399   \n",
       "\n",
       "    param_criterion param_max_depth param_min_samples_leaf  \\\n",
       "0              gini            None                      1   \n",
       "1              gini            None                      1   \n",
       "2              gini            None                      1   \n",
       "3              gini            None                      1   \n",
       "4              gini            None                      1   \n",
       "..              ...             ...                    ...   \n",
       "139         entropy              30                      4   \n",
       "140         entropy              30                      4   \n",
       "141         entropy              30                      4   \n",
       "142         entropy              30                      4   \n",
       "143         entropy              30                      4   \n",
       "\n",
       "    param_min_samples_split param_splitter  \\\n",
       "0                         2           best   \n",
       "1                         2         random   \n",
       "2                         5           best   \n",
       "3                         5         random   \n",
       "4                        10           best   \n",
       "..                      ...            ...   \n",
       "139                       2         random   \n",
       "140                       5           best   \n",
       "141                       5         random   \n",
       "142                      10           best   \n",
       "143                      10         random   \n",
       "\n",
       "                                                params  split0_test_score  \\\n",
       "0    {'criterion': 'gini', 'max_depth': None, 'min_...             0.9125   \n",
       "1    {'criterion': 'gini', 'max_depth': None, 'min_...             0.9375   \n",
       "2    {'criterion': 'gini', 'max_depth': None, 'min_...             0.9125   \n",
       "3    {'criterion': 'gini', 'max_depth': None, 'min_...             0.9250   \n",
       "4    {'criterion': 'gini', 'max_depth': None, 'min_...             0.9375   \n",
       "..                                                 ...                ...   \n",
       "139  {'criterion': 'entropy', 'max_depth': 30, 'min...             0.9125   \n",
       "140  {'criterion': 'entropy', 'max_depth': 30, 'min...             0.9500   \n",
       "141  {'criterion': 'entropy', 'max_depth': 30, 'min...             0.9125   \n",
       "142  {'criterion': 'entropy', 'max_depth': 30, 'min...             0.9250   \n",
       "143  {'criterion': 'entropy', 'max_depth': 30, 'min...             0.9125   \n",
       "\n",
       "     split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0               0.8875             0.9000           0.936709   \n",
       "1               0.9000             0.9625           0.911392   \n",
       "2               0.8875             0.9250           0.936709   \n",
       "3               0.8875             0.9625           0.924051   \n",
       "4               0.8875             0.9250           0.911392   \n",
       "..                 ...                ...                ...   \n",
       "139             0.8750             0.9625           0.949367   \n",
       "140             0.8875             0.9625           0.949367   \n",
       "141             0.8750             0.9625           0.949367   \n",
       "142             0.8875             0.9625           0.949367   \n",
       "143             0.8750             0.9250           0.936709   \n",
       "\n",
       "     split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0             0.911392         0.909620        0.016292              117  \n",
       "1             0.949367         0.932152        0.023284               37  \n",
       "2             0.936709         0.919684        0.018414               89  \n",
       "3             0.962025         0.932215        0.028014               21  \n",
       "4             0.924051         0.917089        0.016945              101  \n",
       "..                 ...              ...             ...              ...  \n",
       "139           0.936709         0.927215        0.030883               61  \n",
       "140           0.924051         0.934684        0.026696                5  \n",
       "141           0.936709         0.927215        0.030883               61  \n",
       "142           0.924051         0.929684        0.025681               41  \n",
       "143           0.860759         0.901994        0.029236              137  \n",
       "\n",
       "[144 rows x 18 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning_result_dt_gs = pd.DataFrame(dt_grid_search.cv_results_)\n",
    "tuning_result_dt_gs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b126856c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy',\n",
       " 'max_depth': None,\n",
       " 'min_samples_leaf': 2,\n",
       " 'min_samples_split': 10,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_grid_search.best_params_ #clf = dt_grid_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8c21bb2",
   "metadata": {},
   "source": [
    "# Perform Grid Search for Random Forest\n",
    "rf_grid_search = GridSearchCV(estimator=RandomForestClassifier(random_state=42),\n",
    "                              param_grid=rf_params, cv=5)\n",
    "rf_grid_search.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1914352e",
   "metadata": {},
   "source": [
    "# Evaluate the optimized models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f34b4b51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Decision Tree Grid Search Performance:\n",
      "Accuracy: 0.9590643274853801\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           B       0.95      0.98      0.97       108\n",
      "           M       0.97      0.92      0.94        63\n",
      "\n",
      "    accuracy                           0.96       171\n",
      "   macro avg       0.96      0.95      0.96       171\n",
      "weighted avg       0.96      0.96      0.96       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree and Grid Search\n",
    "y_pred_dt_gs = dt_grid_search.predict(xtest)\n",
    "print(\"\\nDecision Tree Grid Search Performance:\")\n",
    "print(\"Accuracy:\", accuracy_score(ytest, y_pred_dt_gs))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(ytest, y_pred_dt_gs))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7a8d8fa",
   "metadata": {},
   "source": [
    "# Random Forest Grid Search\n",
    "y_pred_rf_gs = rf_grid_search.predict(xtest)\n",
    "print(\"\\nRandom Forest Grid Search Performance:\")\n",
    "print(\"Accuracy:\", accuracy_score(ytest, y_pred_rf_gs))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(ytest, y_pred_rf_gs))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b6ecd0d",
   "metadata": {},
   "source": [
    "# Hyperparameter optimization using Randomized Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4bd9f1b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=DecisionTreeClassifier(random_state=42),\n",
       "                   n_iter=100,\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [None, 10, 20, 30],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'splitter': ['best', 'random']},\n",
       "                   random_state=42)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Perform Randomized Search for Decision Tree\n",
    "dt_random_search = RandomizedSearchCV(estimator=DecisionTreeClassifier(random_state=42),\n",
    "                                      param_distributions=dt_params, n_iter=100, cv=5, random_state=42)\n",
    "dt_random_search.fit(xtrain, ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "5ca522f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00170379, 0.00158415, 0.00410838, 0.00160403, 0.00360928,\n",
       "        0.00361533, 0.00451574, 0.00180478, 0.00360942, 0.00380998,\n",
       "        0.00140376, 0.00450807, 0.00441031, 0.00180459, 0.00441031,\n",
       "        0.00400906, 0.00180473, 0.00180478, 0.00180469, 0.00180473,\n",
       "        0.00180373, 0.00200505, 0.00451045, 0.00180464, 0.00200534,\n",
       "        0.00180449, 0.00180283, 0.0041101 , 0.00421014, 0.00421   ,\n",
       "        0.00200706, 0.00451059, 0.00400853, 0.00369382, 0.00416183,\n",
       "        0.00169473, 0.00167174, 0.00394893, 0.00442753, 0.00180626,\n",
       "        0.0044354 , 0.00402055, 0.00381012, 0.00441084, 0.00390906,\n",
       "        0.00400953, 0.0044106 , 0.00400968, 0.00180492, 0.00200505,\n",
       "        0.00160451, 0.00160437, 0.00380998, 0.00160427, 0.0018074 ,\n",
       "        0.0016036 , 0.00380988, 0.00361271, 0.00442748, 0.00414238,\n",
       "        0.00180478, 0.00383563, 0.00421743, 0.00422654, 0.00170817,\n",
       "        0.00180478, 0.00202146, 0.00405097, 0.0038403 , 0.00160685,\n",
       "        0.00160379, 0.00160604, 0.00180483, 0.00180154, 0.00180473,\n",
       "        0.00180306, 0.0016223 , 0.00182014, 0.00355406, 0.00220642,\n",
       "        0.0018055 , 0.00180459, 0.0038065 , 0.00170503, 0.00160422,\n",
       "        0.00180459, 0.00180473, 0.00383067, 0.00160418, 0.00435925,\n",
       "        0.00422339, 0.002005  , 0.00160413, 0.00382261, 0.00433073,\n",
       "        0.00383539, 0.00382094, 0.00160356, 0.00381007, 0.00388641]),\n",
       " 'std_fit_time': array([5.74333609e-04, 4.04535830e-04, 1.73986333e-04, 4.91205656e-04,\n",
       "        4.91127796e-04, 4.98005025e-04, 4.45721212e-04, 4.00996247e-04,\n",
       "        4.91049929e-04, 4.01043903e-04, 4.91186191e-04, 6.29218736e-04,\n",
       "        4.91205679e-04, 4.01020078e-04, 4.90622185e-04, 8.71451706e-07,\n",
       "        4.00972381e-04, 4.01115446e-04, 4.01186982e-04, 4.00972409e-04,\n",
       "        4.02855968e-04, 2.33601546e-07, 4.50172700e-04, 4.00924796e-04,\n",
       "        2.13248060e-07, 4.01568423e-04, 4.00034536e-04, 6.65592887e-04,\n",
       "        4.01067861e-04, 4.01258539e-04, 2.17157317e-06, 6.34823991e-04,\n",
       "        6.36031331e-04, 4.06367301e-04, 9.23445640e-04, 4.97562025e-04,\n",
       "        5.57266602e-04, 3.71439833e-04, 5.09814738e-04, 4.02242683e-04,\n",
       "        4.66062346e-04, 6.34142447e-04, 4.00996219e-04, 4.91848424e-04,\n",
       "        6.65621333e-04, 2.78041453e-07, 4.91264074e-04, 6.34369994e-04,\n",
       "        4.01187067e-04, 1.14242063e-06, 4.91303246e-04, 4.91380921e-04,\n",
       "        4.01043903e-04, 4.91205725e-04, 3.94203398e-04, 4.90758941e-04,\n",
       "        4.01354077e-04, 4.87047281e-04, 4.85372714e-04, 1.96493976e-04,\n",
       "        4.00996247e-04, 4.14852449e-04, 3.85306683e-04, 7.33152050e-04,\n",
       "        3.99227610e-04, 4.00996219e-04, 3.23535330e-05, 5.51820304e-05,\n",
       "        3.84963505e-04, 4.93333028e-04, 4.91790145e-04, 4.94129287e-04,\n",
       "        4.01153072e-04, 3.99661268e-04, 4.00972437e-04, 4.00268983e-04,\n",
       "        5.07205806e-04, 4.09584756e-04, 4.49916779e-04, 4.00736274e-04,\n",
       "        4.01355550e-04, 4.01020078e-04, 4.02529074e-04, 6.02253724e-04,\n",
       "        4.91166823e-04, 4.00900880e-04, 4.00972381e-04, 4.12102828e-04,\n",
       "        4.91225171e-04, 4.22416633e-04, 3.95683496e-04, 2.43140197e-07,\n",
       "        4.91186191e-04, 4.08233834e-04, 3.92122264e-04, 3.82725627e-04,\n",
       "        4.07605758e-04, 4.90720193e-04, 3.69494299e-04, 6.11887995e-04]),\n",
       " 'mean_score_time': array([0.00082755, 0.00108657, 0.0004662 , 0.00080218, 0.00100279,\n",
       "        0.00080209, 0.00060096, 0.00080204, 0.00060158, 0.00080214,\n",
       "        0.00100522, 0.00040145, 0.00060225, 0.00080223, 0.0010035 ,\n",
       "        0.00040145, 0.00080204, 0.00080199, 0.00080204, 0.00080209,\n",
       "        0.00060124, 0.00100279, 0.00100441, 0.00080218, 0.00080199,\n",
       "        0.0008019 , 0.00060182, 0.00060196, 0.0008028 , 0.0010036 ,\n",
       "        0.00080032, 0.0008028 , 0.00080271, 0.00120897, 0.00060058,\n",
       "        0.00060349, 0.00122876, 0.00102015, 0.00100136, 0.00102644,\n",
       "        0.00110354, 0.00080051, 0.00080204, 0.00060196, 0.00100369,\n",
       "        0.00100355, 0.00060215, 0.00060201, 0.00100198, 0.00070233,\n",
       "        0.00080209, 0.00060163, 0.0010026 , 0.00060167, 0.00059834,\n",
       "        0.00070238, 0.00080218, 0.00079885, 0.00040116, 0.00057669,\n",
       "        0.00060158, 0.00077648, 0.00099616, 0.00078931, 0.0006043 ,\n",
       "        0.00080204, 0.00058541, 0.00096216, 0.00097227, 0.00060163,\n",
       "        0.00100574, 0.00059962, 0.00080204, 0.00080528, 0.00080214,\n",
       "        0.0006032 , 0.00084143, 0.00058889, 0.00076385, 0.00080147,\n",
       "        0.00100188, 0.0007988 , 0.00060086, 0.00060153, 0.00080214,\n",
       "        0.00060492, 0.00060153, 0.000982  , 0.00080204, 0.00058866,\n",
       "        0.00078955, 0.00060177, 0.00100269, 0.0007894 , 0.00079212,\n",
       "        0.00079117, 0.00058889, 0.00060215, 0.00100532, 0.0010592 ]),\n",
       " 'std_score_time': array([4.16774116e-04, 1.48649776e-04, 5.80369191e-04, 4.01091586e-04,\n",
       "        1.50789149e-07, 4.03082395e-04, 4.90681159e-04, 4.01020078e-04,\n",
       "        4.91186214e-04, 4.01067748e-04, 6.97454831e-06, 4.91672859e-04,\n",
       "        4.91731277e-04, 4.01115417e-04, 3.69356475e-07, 4.91672859e-04,\n",
       "        4.01020050e-04, 4.00996247e-04, 4.01020135e-04, 4.01043931e-04,\n",
       "        4.90914088e-04, 1.50789149e-07, 2.47955322e-06, 4.01091615e-04,\n",
       "        4.00996276e-04, 4.00948620e-04, 4.91381013e-04, 4.91497652e-04,\n",
       "        4.01401576e-04, 2.43140197e-07, 4.00166630e-04, 4.01401605e-04,\n",
       "        4.01354445e-04, 3.98515080e-04, 4.90368586e-04, 4.92767667e-04,\n",
       "        2.03441410e-04, 1.83983773e-05, 6.39744180e-07, 4.86316397e-05,\n",
       "        2.02354128e-04, 4.00257139e-04, 4.01020078e-04, 4.91497930e-04,\n",
       "        2.33601546e-07, 2.78041453e-07, 4.91653394e-04, 4.91536616e-04,\n",
       "        8.34124359e-07, 4.00273806e-04, 4.01043931e-04, 4.91225148e-04,\n",
       "        1.78416128e-07, 4.91264074e-04, 4.88573519e-04, 6.02285557e-04,\n",
       "        4.01091615e-04, 3.99474455e-04, 4.91322457e-04, 4.71415848e-04,\n",
       "        4.91186191e-04, 3.89283192e-04, 5.08205071e-05, 3.95423624e-04,\n",
       "        4.93429921e-04, 4.01020078e-04, 4.78792606e-04, 5.51669063e-05,\n",
       "        2.86773054e-05, 4.91225125e-04, 6.39139692e-06, 4.89618289e-04,\n",
       "        4.01020050e-04, 4.02688362e-04, 4.01067748e-04, 4.92519351e-04,\n",
       "        4.30358556e-04, 4.81395581e-04, 3.82717696e-04, 4.00735849e-04,\n",
       "        1.22872340e-06, 3.99444487e-04, 4.90602743e-04, 4.91147273e-04,\n",
       "        4.01067748e-04, 4.93948599e-04, 4.91147250e-04, 2.62976449e-05,\n",
       "        4.01020107e-04, 4.81190644e-04, 3.95529240e-04, 4.91341948e-04,\n",
       "        1.16800773e-07, 3.95467392e-04, 3.96974469e-04, 3.96450357e-04,\n",
       "        4.81395581e-04, 4.91654689e-04, 4.94073252e-06, 6.97106897e-05]),\n",
       " 'param_splitter': masked_array(data=['random', 'random', 'best', 'random', 'best', 'best',\n",
       "                    'best', 'random', 'best', 'best', 'random', 'best',\n",
       "                    'best', 'random', 'best', 'best', 'random', 'random',\n",
       "                    'random', 'random', 'random', 'random', 'best',\n",
       "                    'random', 'random', 'random', 'random', 'best', 'best',\n",
       "                    'best', 'random', 'best', 'best', 'best', 'best',\n",
       "                    'random', 'random', 'best', 'best', 'random', 'best',\n",
       "                    'best', 'best', 'best', 'best', 'best', 'best', 'best',\n",
       "                    'random', 'random', 'random', 'random', 'best',\n",
       "                    'random', 'random', 'random', 'best', 'best', 'best',\n",
       "                    'best', 'random', 'best', 'best', 'best', 'random',\n",
       "                    'random', 'random', 'best', 'best', 'random', 'random',\n",
       "                    'random', 'random', 'random', 'random', 'random',\n",
       "                    'random', 'random', 'best', 'random', 'random',\n",
       "                    'random', 'best', 'random', 'random', 'random',\n",
       "                    'random', 'best', 'random', 'best', 'best', 'random',\n",
       "                    'random', 'best', 'best', 'best', 'best', 'random',\n",
       "                    'best', 'best'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_min_samples_split': masked_array(data=[5, 2, 10, 2, 5, 2, 2, 10, 2, 2, 5, 2, 10, 2, 10, 10, 5,\n",
       "                    5, 10, 2, 10, 10, 5, 10, 2, 5, 5, 10, 5, 10, 2, 5, 2,\n",
       "                    10, 10, 5, 5, 2, 10, 2, 2, 2, 2, 2, 10, 10, 5, 2, 2,\n",
       "                    10, 2, 2, 5, 5, 10, 5, 10, 5, 5, 5, 10, 2, 10, 10, 10,\n",
       "                    5, 5, 10, 5, 2, 5, 10, 10, 2, 2, 2, 10, 10, 10, 10, 2,\n",
       "                    2, 10, 10, 10, 5, 5, 5, 2, 2, 2, 5, 10, 5, 2, 2, 2, 10,\n",
       "                    5, 5],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_min_samples_leaf': masked_array(data=[2, 1, 2, 2, 1, 4, 2, 2, 4, 1, 4, 2, 1, 2, 2, 2, 2, 4,\n",
       "                    4, 1, 2, 2, 1, 1, 1, 2, 2, 1, 2, 4, 4, 2, 1, 4, 2, 1,\n",
       "                    1, 2, 2, 1, 2, 2, 1, 4, 2, 1, 4, 2, 2, 1, 2, 4, 2, 4,\n",
       "                    1, 1, 1, 4, 2, 4, 2, 4, 1, 1, 2, 4, 2, 4, 2, 4, 2, 4,\n",
       "                    1, 4, 1, 4, 4, 1, 4, 1, 2, 2, 4, 4, 2, 4, 1, 2, 4, 1,\n",
       "                    2, 1, 4, 1, 1, 4, 2, 2, 1, 4],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_max_depth': masked_array(data=[20, 10, None, 10, 30, None, 30, 30, 30, 10, 20, None,\n",
       "                    10, 30, 10, 30, 10, 30, 20, None, None, 20, 20, 20, 30,\n",
       "                    20, None, None, 10, 20, 10, 10, 20, None, None, 10, 20,\n",
       "                    20, 20, 30, 10, 30, None, None, 10, 20, None, 10, 10,\n",
       "                    10, 20, 30, 20, None, 10, 20, 10, 30, None, 30, 20, 10,\n",
       "                    20, None, 30, 10, None, 30, 30, 30, 30, 20, None, None,\n",
       "                    20, 20, 10, None, 10, 30, None, 20, 30, None, None, 20,\n",
       "                    30, None, None, 20, 20, None, None, 20, None, 20, None,\n",
       "                    10, None, 20],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_criterion': masked_array(data=['entropy', 'gini', 'entropy', 'entropy', 'gini',\n",
       "                    'gini', 'entropy', 'gini', 'gini', 'gini', 'gini',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'gini',\n",
       "                    'gini', 'gini', 'entropy', 'entropy', 'gini',\n",
       "                    'entropy', 'entropy', 'entropy', 'gini', 'gini',\n",
       "                    'gini', 'gini', 'gini', 'entropy', 'gini', 'entropy',\n",
       "                    'gini', 'gini', 'gini', 'entropy', 'entropy', 'gini',\n",
       "                    'entropy', 'entropy', 'entropy', 'gini', 'gini',\n",
       "                    'entropy', 'gini', 'gini', 'entropy', 'gini', 'gini',\n",
       "                    'gini', 'entropy', 'gini', 'gini', 'gini', 'entropy',\n",
       "                    'gini', 'gini', 'gini', 'entropy', 'entropy', 'gini',\n",
       "                    'gini', 'entropy', 'entropy', 'entropy', 'gini',\n",
       "                    'entropy', 'entropy', 'gini', 'entropy', 'entropy',\n",
       "                    'gini', 'gini', 'entropy', 'entropy', 'gini', 'gini',\n",
       "                    'entropy', 'gini', 'entropy', 'gini', 'gini', 'gini',\n",
       "                    'entropy', 'entropy', 'entropy', 'entropy', 'gini',\n",
       "                    'gini', 'entropy', 'entropy', 'gini', 'gini', 'gini',\n",
       "                    'entropy', 'entropy', 'gini', 'entropy', 'gini',\n",
       "                    'entropy'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 30,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 2,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'random',\n",
       "   'min_samples_split': 10,\n",
       "   'min_samples_leaf': 2,\n",
       "   'max_depth': 10,\n",
       "   'criterion': 'entropy'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 1,\n",
       "   'max_depth': None,\n",
       "   'criterion': 'gini'},\n",
       "  {'splitter': 'best',\n",
       "   'min_samples_split': 5,\n",
       "   'min_samples_leaf': 4,\n",
       "   'max_depth': 20,\n",
       "   'criterion': 'entropy'}],\n",
       " 'split0_test_score': array([0.925 , 0.9375, 0.9375, 0.9375, 0.9125, 0.95  , 0.95  , 0.925 ,\n",
       "        0.95  , 0.9125, 0.925 , 0.95  , 0.9375, 0.9375, 0.9375, 0.9375,\n",
       "        0.9375, 0.925 , 0.9125, 0.925 , 0.925 , 0.925 , 0.95  , 0.925 ,\n",
       "        0.9375, 0.9375, 0.9375, 0.9375, 0.9125, 0.925 , 0.925 , 0.95  ,\n",
       "        0.9125, 0.9375, 0.9375, 0.925 , 0.925 , 0.9125, 0.9375, 0.925 ,\n",
       "        0.95  , 0.9125, 0.9125, 0.95  , 0.9375, 0.9375, 0.95  , 0.9125,\n",
       "        0.875 , 0.9375, 0.9375, 0.925 , 0.9125, 0.925 , 0.925 , 0.925 ,\n",
       "        0.9375, 0.95  , 0.95  , 0.95  , 0.925 , 0.95  , 0.9375, 0.9375,\n",
       "        0.925 , 0.925 , 0.925 , 0.925 , 0.9125, 0.9125, 0.925 , 0.925 ,\n",
       "        0.9375, 0.9125, 0.925 , 0.925 , 0.925 , 0.925 , 0.9375, 0.925 ,\n",
       "        0.875 , 0.875 , 0.9375, 0.9125, 0.925 , 0.9125, 0.925 , 0.9125,\n",
       "        0.925 , 0.95  , 0.95  , 0.925 , 0.925 , 0.9125, 0.95  , 0.95  ,\n",
       "        0.9125, 0.925 , 0.9125, 0.95  ]),\n",
       " 'split1_test_score': array([0.8875, 0.9   , 0.8875, 0.8875, 0.8875, 0.9125, 0.9   , 0.8875,\n",
       "        0.9125, 0.8875, 0.875 , 0.9   , 0.8875, 0.8875, 0.8875, 0.9   ,\n",
       "        0.9   , 0.875 , 0.875 , 0.8875, 0.8875, 0.8875, 0.8875, 0.8875,\n",
       "        0.9   , 0.9   , 0.9   , 0.8875, 0.9125, 0.8875, 0.875 , 0.8875,\n",
       "        0.8875, 0.8875, 0.9   , 0.8875, 0.8875, 0.9125, 0.8875, 0.8875,\n",
       "        0.9   , 0.9125, 0.8875, 0.8875, 0.9   , 0.8875, 0.8875, 0.9125,\n",
       "        0.875 , 0.8875, 0.8875, 0.875 , 0.9125, 0.875 , 0.8875, 0.8875,\n",
       "        0.8875, 0.9125, 0.8875, 0.8875, 0.8875, 0.9125, 0.8875, 0.8875,\n",
       "        0.8875, 0.875 , 0.8875, 0.8875, 0.9125, 0.875 , 0.8875, 0.875 ,\n",
       "        0.8875, 0.875 , 0.8875, 0.875 , 0.875 , 0.8875, 0.8875, 0.8875,\n",
       "        0.875 , 0.875 , 0.8875, 0.875 , 0.8875, 0.875 , 0.8875, 0.9125,\n",
       "        0.875 , 0.9   , 0.9   , 0.8875, 0.875 , 0.8875, 0.9   , 0.8875,\n",
       "        0.9125, 0.8875, 0.8875, 0.8875]),\n",
       " 'split2_test_score': array([0.9125, 0.9625, 0.975 , 0.8875, 0.925 , 0.925 , 0.95  , 0.9625,\n",
       "        0.925 , 0.9   , 0.9375, 0.95  , 0.975 , 0.8875, 0.975 , 0.9125,\n",
       "        0.9625, 0.9375, 0.925 , 0.925 , 0.9625, 0.95  , 0.95  , 0.9375,\n",
       "        0.9625, 0.9625, 0.9625, 0.925 , 0.9375, 0.9625, 0.9375, 0.95  ,\n",
       "        0.9   , 0.925 , 0.9125, 0.9375, 0.9375, 0.925 , 0.975 , 0.925 ,\n",
       "        0.95  , 0.925 , 0.9   , 0.9625, 0.9125, 0.925 , 0.9625, 0.925 ,\n",
       "        0.9625, 0.95  , 0.8875, 0.9375, 0.9375, 0.9375, 0.9375, 0.9625,\n",
       "        0.925 , 0.925 , 0.95  , 0.9625, 0.9625, 0.925 , 0.975 , 0.975 ,\n",
       "        0.95  , 0.9375, 0.9125, 0.9625, 0.9375, 0.9625, 0.9125, 0.95  ,\n",
       "        0.95  , 0.9625, 0.925 , 0.9375, 0.95  , 0.9375, 0.925 , 0.9375,\n",
       "        0.9625, 0.9625, 0.925 , 0.925 , 0.95  , 0.9625, 0.9375, 0.9375,\n",
       "        0.9375, 0.95  , 0.95  , 0.9625, 0.95  , 0.925 , 0.95  , 0.9625,\n",
       "        0.925 , 0.95  , 0.925 , 0.9625]),\n",
       " 'split3_test_score': array([0.89873418, 0.91139241, 0.94936709, 0.93670886, 0.93670886,\n",
       "        0.94936709, 0.93670886, 0.93670886, 0.94936709, 0.93670886,\n",
       "        0.93670886, 0.93670886, 0.94936709, 0.93670886, 0.94936709,\n",
       "        0.92405063, 0.91139241, 0.93670886, 0.93670886, 0.96202532,\n",
       "        0.93670886, 0.93670886, 0.93670886, 0.93670886, 0.91139241,\n",
       "        0.91139241, 0.91139241, 0.91139241, 0.94936709, 0.94936709,\n",
       "        0.93670886, 0.93670886, 0.93670886, 0.92405063, 0.92405063,\n",
       "        0.89873418, 0.89873418, 0.94936709, 0.94936709, 0.96202532,\n",
       "        0.93670886, 0.94936709, 0.93670886, 0.94936709, 0.92405063,\n",
       "        0.91139241, 0.94936709, 0.94936709, 0.93670886, 0.93670886,\n",
       "        0.93670886, 0.93670886, 0.94936709, 0.93670886, 0.93670886,\n",
       "        0.92405063, 0.91139241, 0.94936709, 0.93670886, 0.94936709,\n",
       "        0.93670886, 0.94936709, 0.94936709, 0.94936709, 0.93670886,\n",
       "        0.93670886, 0.89873418, 0.94936709, 0.94936709, 0.94936709,\n",
       "        0.89873418, 0.94936709, 0.93670886, 0.94936709, 0.96202532,\n",
       "        0.93670886, 0.94936709, 0.93670886, 0.92405063, 0.93670886,\n",
       "        0.93670886, 0.93670886, 0.92405063, 0.93670886, 0.93670886,\n",
       "        0.94936709, 0.89873418, 0.94936709, 0.93670886, 0.93670886,\n",
       "        0.93670886, 0.92405063, 0.94936709, 0.93670886, 0.93670886,\n",
       "        0.94936709, 0.94936709, 0.93670886, 0.93670886, 0.94936709]),\n",
       " 'split4_test_score': array([0.87341772, 0.94936709, 0.93670886, 0.87341772, 0.93670886,\n",
       "        0.92405063, 0.93670886, 0.93670886, 0.92405063, 0.91139241,\n",
       "        0.86075949, 0.93670886, 0.92405063, 0.87341772, 0.93670886,\n",
       "        0.92405063, 0.91139241, 0.86075949, 0.86075949, 0.91139241,\n",
       "        0.93670886, 0.93670886, 0.92405063, 0.93670886, 0.94936709,\n",
       "        0.91139241, 0.91139241, 0.92405063, 0.92405063, 0.92405063,\n",
       "        0.86075949, 0.93670886, 0.91139241, 0.92405063, 0.92405063,\n",
       "        0.89873418, 0.89873418, 0.88607595, 0.93670886, 0.91139241,\n",
       "        0.93670886, 0.88607595, 0.91139241, 0.92405063, 0.92405063,\n",
       "        0.92405063, 0.92405063, 0.88607595, 0.91139241, 0.93670886,\n",
       "        0.87341772, 0.86075949, 0.92405063, 0.86075949, 0.93670886,\n",
       "        0.96202532, 0.92405063, 0.92405063, 0.93670886, 0.92405063,\n",
       "        0.93670886, 0.92405063, 0.92405063, 0.92405063, 0.93670886,\n",
       "        0.86075949, 0.87341772, 0.92405063, 0.92405063, 0.93670886,\n",
       "        0.87341772, 0.86075949, 0.93670886, 0.93670886, 0.91139241,\n",
       "        0.86075949, 0.86075949, 0.93670886, 0.92405063, 0.93670886,\n",
       "        0.91139241, 0.91139241, 0.92405063, 0.86075949, 0.93670886,\n",
       "        0.93670886, 0.89873418, 0.92405063, 0.86075949, 0.91139241,\n",
       "        0.93670886, 0.96202532, 0.86075949, 0.93670886, 0.91139241,\n",
       "        0.92405063, 0.88607595, 0.93670886, 0.93670886, 0.92405063]),\n",
       " 'mean_test_score': array([0.89943038, 0.9321519 , 0.93721519, 0.90452532, 0.91968354,\n",
       "        0.93218354, 0.93468354, 0.92968354, 0.93218354, 0.90962025,\n",
       "        0.90699367, 0.93468354, 0.93468354, 0.90452532, 0.93721519,\n",
       "        0.91962025, 0.92455696, 0.90699367, 0.90199367, 0.92218354,\n",
       "        0.92968354, 0.92718354, 0.9296519 , 0.92468354, 0.9321519 ,\n",
       "        0.92455696, 0.92455696, 0.91708861, 0.92718354, 0.92968354,\n",
       "        0.90699367, 0.93218354, 0.90962025, 0.91962025, 0.91962025,\n",
       "        0.90949367, 0.90949367, 0.91708861, 0.93721519, 0.92218354,\n",
       "        0.93468354, 0.91708861, 0.90962025, 0.93468354, 0.91962025,\n",
       "        0.91708861, 0.93468354, 0.91708861, 0.91212025, 0.92968354,\n",
       "        0.90452532, 0.90699367, 0.92718354, 0.90699367, 0.92468354,\n",
       "        0.93221519, 0.91708861, 0.93218354, 0.93218354, 0.93468354,\n",
       "        0.92968354, 0.93218354, 0.93468354, 0.93468354, 0.92718354,\n",
       "        0.90699367, 0.89943038, 0.92968354, 0.92718354, 0.92721519,\n",
       "        0.89943038, 0.91202532, 0.92968354, 0.92721519, 0.92218354,\n",
       "        0.90699367, 0.91202532, 0.92468354, 0.91962025, 0.92468354,\n",
       "        0.91212025, 0.91212025, 0.91962025, 0.90199367, 0.92718354,\n",
       "        0.92721519, 0.90949367, 0.92718354, 0.90699367, 0.92962025,\n",
       "        0.93468354, 0.93221519, 0.91202532, 0.91968354, 0.92962025,\n",
       "        0.93468354, 0.91708861, 0.92718354, 0.91968354, 0.93468354]),\n",
       " 'std_test_score': array([0.01813338, 0.02328379, 0.028451  , 0.02709434, 0.01841397,\n",
       "        0.01495252, 0.01833216, 0.02440089, 0.01495252, 0.0162916 ,\n",
       "        0.03255473, 0.01833216, 0.02894236, 0.02709434, 0.028451  ,\n",
       "        0.0126063 , 0.0226135 , 0.03255473, 0.02923605, 0.02417697,\n",
       "        0.02440089, 0.02136167, 0.02317778, 0.01916348, 0.02328379,\n",
       "        0.0226135 , 0.0226135 , 0.01694472, 0.0144192 , 0.02568065,\n",
       "        0.03255473, 0.02311895, 0.0162916 , 0.01684989, 0.0126063 ,\n",
       "        0.01865506, 0.01865506, 0.02053478, 0.028451  , 0.02417697,\n",
       "        0.01833216, 0.02053478, 0.0162916 , 0.0266957 , 0.0126063 ,\n",
       "        0.01694472, 0.0266957 , 0.02053478, 0.03434843, 0.02168878,\n",
       "        0.02709434, 0.03255473, 0.0144192 , 0.03255473, 0.01916348,\n",
       "        0.02801421, 0.01694472, 0.01495252, 0.02311895, 0.0266957 ,\n",
       "        0.02440089, 0.01495252, 0.02894236, 0.02894236, 0.02136167,\n",
       "        0.03255473, 0.01813338, 0.02568065, 0.0144192 , 0.03088306,\n",
       "        0.01813338, 0.03742696, 0.02168878, 0.03088306, 0.02417697,\n",
       "        0.03255473, 0.03742696, 0.01916348, 0.01684989, 0.01916348,\n",
       "        0.03434843, 0.03434843, 0.01684989, 0.02923605, 0.02136167,\n",
       "        0.03088306, 0.01865506, 0.0144192 , 0.03255473, 0.02044764,\n",
       "        0.01833216, 0.02801421, 0.03742696, 0.01841397, 0.02044764,\n",
       "        0.0266957 , 0.02053478, 0.02136167, 0.01841397, 0.0266957 ]),\n",
       " 'rank_test_score': array([98, 24,  1, 93, 57, 20,  4, 26, 20, 79, 85,  4,  4, 93,  1, 60, 51,\n",
       "        85, 96, 54, 26, 39, 33, 47, 24, 51, 51, 66, 39, 26, 85, 18, 79, 60,\n",
       "        60, 82, 82, 66,  1, 54,  4, 66, 79,  4, 60, 66,  4, 66, 73, 26, 93,\n",
       "        85, 39, 85, 47, 16, 66, 20, 18,  4, 26, 20,  4,  4, 39, 85, 98, 26,\n",
       "        39, 36, 98, 76, 26, 36, 54, 85, 76, 47, 60, 47, 73, 73, 60, 96, 39,\n",
       "        36, 82, 39, 85, 34,  4, 16, 76, 57, 34,  4, 66, 39, 57,  4])}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_random_search.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7f5d6463",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_splitter</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001704</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>0.000828</td>\n",
       "      <td>4.167741e-04</td>\n",
       "      <td>random</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'splitter': 'random', 'min_samples_split': 5,...</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.898734</td>\n",
       "      <td>0.873418</td>\n",
       "      <td>0.899430</td>\n",
       "      <td>0.018133</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001584</td>\n",
       "      <td>0.000405</td>\n",
       "      <td>0.001087</td>\n",
       "      <td>1.486498e-04</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'splitter': 'random', 'min_samples_split': 2,...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9000</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.911392</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.932152</td>\n",
       "      <td>0.023284</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.004108</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000466</td>\n",
       "      <td>5.803692e-04</td>\n",
       "      <td>best</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'splitter': 'best', 'min_samples_split': 10, ...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9750</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.937215</td>\n",
       "      <td>0.028451</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>4.010916e-04</td>\n",
       "      <td>random</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'splitter': 'random', 'min_samples_split': 2,...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.873418</td>\n",
       "      <td>0.904525</td>\n",
       "      <td>0.027094</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003609</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.001003</td>\n",
       "      <td>1.507891e-07</td>\n",
       "      <td>best</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'splitter': 'best', 'min_samples_split': 5, '...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.919684</td>\n",
       "      <td>0.018414</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.003835</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>3.964504e-04</td>\n",
       "      <td>best</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'splitter': 'best', 'min_samples_split': 2, '...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.934684</td>\n",
       "      <td>0.026696</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.003821</td>\n",
       "      <td>0.000408</td>\n",
       "      <td>0.000589</td>\n",
       "      <td>4.813956e-04</td>\n",
       "      <td>best</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>None</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'splitter': 'best', 'min_samples_split': 2, '...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.886076</td>\n",
       "      <td>0.917089</td>\n",
       "      <td>0.020535</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.001604</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>0.000602</td>\n",
       "      <td>4.916547e-04</td>\n",
       "      <td>random</td>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'splitter': 'random', 'min_samples_split': 10...</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.927184</td>\n",
       "      <td>0.021362</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.003810</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.001005</td>\n",
       "      <td>4.940733e-06</td>\n",
       "      <td>best</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'splitter': 'best', 'min_samples_split': 5, '...</td>\n",
       "      <td>0.9125</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9250</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.919684</td>\n",
       "      <td>0.018414</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.003886</td>\n",
       "      <td>0.000612</td>\n",
       "      <td>0.001059</td>\n",
       "      <td>6.971069e-05</td>\n",
       "      <td>best</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'splitter': 'best', 'min_samples_split': 5, '...</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.8875</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.934684</td>\n",
       "      <td>0.026696</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.001704      0.000574         0.000828    4.167741e-04   \n",
       "1        0.001584      0.000405         0.001087    1.486498e-04   \n",
       "2        0.004108      0.000174         0.000466    5.803692e-04   \n",
       "3        0.001604      0.000491         0.000802    4.010916e-04   \n",
       "4        0.003609      0.000491         0.001003    1.507891e-07   \n",
       "..            ...           ...              ...             ...   \n",
       "95       0.003835      0.000383         0.000791    3.964504e-04   \n",
       "96       0.003821      0.000408         0.000589    4.813956e-04   \n",
       "97       0.001604      0.000491         0.000602    4.916547e-04   \n",
       "98       0.003810      0.000369         0.001005    4.940733e-06   \n",
       "99       0.003886      0.000612         0.001059    6.971069e-05   \n",
       "\n",
       "   param_splitter param_min_samples_split param_min_samples_leaf  \\\n",
       "0          random                       5                      2   \n",
       "1          random                       2                      1   \n",
       "2            best                      10                      2   \n",
       "3          random                       2                      2   \n",
       "4            best                       5                      1   \n",
       "..            ...                     ...                    ...   \n",
       "95           best                       2                      4   \n",
       "96           best                       2                      2   \n",
       "97         random                      10                      2   \n",
       "98           best                       5                      1   \n",
       "99           best                       5                      4   \n",
       "\n",
       "   param_max_depth param_criterion  \\\n",
       "0               20         entropy   \n",
       "1               10            gini   \n",
       "2             None         entropy   \n",
       "3               10         entropy   \n",
       "4               30            gini   \n",
       "..             ...             ...   \n",
       "95              20         entropy   \n",
       "96            None            gini   \n",
       "97              10         entropy   \n",
       "98            None            gini   \n",
       "99              20         entropy   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'splitter': 'random', 'min_samples_split': 5,...             0.9250   \n",
       "1   {'splitter': 'random', 'min_samples_split': 2,...             0.9375   \n",
       "2   {'splitter': 'best', 'min_samples_split': 10, ...             0.9375   \n",
       "3   {'splitter': 'random', 'min_samples_split': 2,...             0.9375   \n",
       "4   {'splitter': 'best', 'min_samples_split': 5, '...             0.9125   \n",
       "..                                                ...                ...   \n",
       "95  {'splitter': 'best', 'min_samples_split': 2, '...             0.9500   \n",
       "96  {'splitter': 'best', 'min_samples_split': 2, '...             0.9125   \n",
       "97  {'splitter': 'random', 'min_samples_split': 10...             0.9250   \n",
       "98  {'splitter': 'best', 'min_samples_split': 5, '...             0.9125   \n",
       "99  {'splitter': 'best', 'min_samples_split': 5, '...             0.9500   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0              0.8875             0.9125           0.898734   \n",
       "1              0.9000             0.9625           0.911392   \n",
       "2              0.8875             0.9750           0.949367   \n",
       "3              0.8875             0.8875           0.936709   \n",
       "4              0.8875             0.9250           0.936709   \n",
       "..                ...                ...                ...   \n",
       "95             0.8875             0.9625           0.949367   \n",
       "96             0.9125             0.9250           0.949367   \n",
       "97             0.8875             0.9500           0.936709   \n",
       "98             0.8875             0.9250           0.936709   \n",
       "99             0.8875             0.9625           0.949367   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.873418         0.899430        0.018133               98  \n",
       "1            0.949367         0.932152        0.023284               24  \n",
       "2            0.936709         0.937215        0.028451                1  \n",
       "3            0.873418         0.904525        0.027094               93  \n",
       "4            0.936709         0.919684        0.018414               57  \n",
       "..                ...              ...             ...              ...  \n",
       "95           0.924051         0.934684        0.026696                4  \n",
       "96           0.886076         0.917089        0.020535               66  \n",
       "97           0.936709         0.927184        0.021362               39  \n",
       "98           0.936709         0.919684        0.018414               57  \n",
       "99           0.924051         0.934684        0.026696                4  \n",
       "\n",
       "[100 rows x 18 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tuning_result_dt_rs = pd.DataFrame(dt_random_search.cv_results_)\n",
    "tuning_result_dt_rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "5870adf4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'splitter': 'best',\n",
       " 'min_samples_split': 10,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_depth': None,\n",
       " 'criterion': 'entropy'}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "322fb989",
   "metadata": {},
   "source": [
    "# Perform Randomized Search for Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fd5bbe96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
       "                   n_iter=100,\n",
       "                   param_distributions={'criterion': ['gini', 'entropy'],\n",
       "                                        'max_depth': [None, 10, 20, 30],\n",
       "                                        'max_features': ['auto', 'sqrt',\n",
       "                                                         'log2'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [100, 200, 300]},\n",
       "                   random_state=42)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random_search = RandomizedSearchCV(estimator=RandomForestClassifier(random_state=42),\n",
    "                                      param_distributions=rf_params, n_iter=100, cv=5, random_state=42)\n",
    "rf_random_search.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e3dd0475",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_n_estimators</th>\n",
       "      <th>param_min_samples_split</th>\n",
       "      <th>param_min_samples_leaf</th>\n",
       "      <th>param_max_features</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_criterion</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.257358</td>\n",
       "      <td>0.006316</td>\n",
       "      <td>0.016741</td>\n",
       "      <td>0.000857</td>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>30</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 300, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.954747</td>\n",
       "      <td>0.006335</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.165267</td>\n",
       "      <td>0.000899</td>\n",
       "      <td>0.012126</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>200</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>log2</td>\n",
       "      <td>20</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.924051</td>\n",
       "      <td>0.944684</td>\n",
       "      <td>0.013000</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.272693</td>\n",
       "      <td>0.003310</td>\n",
       "      <td>0.017232</td>\n",
       "      <td>0.000574</td>\n",
       "      <td>300</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>30</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 300, 'min_samples_split': 10,...</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.962025</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.954778</td>\n",
       "      <td>0.009996</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.091305</td>\n",
       "      <td>0.001192</td>\n",
       "      <td>0.006450</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>10</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 10,...</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.947184</td>\n",
       "      <td>0.012509</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.080434</td>\n",
       "      <td>0.001489</td>\n",
       "      <td>0.006220</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>100</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>None</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 100, 'min_samples_split': 2, ...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.944684</td>\n",
       "      <td>0.010242</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.238214</td>\n",
       "      <td>0.003218</td>\n",
       "      <td>0.016956</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>300</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>log2</td>\n",
       "      <td>None</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 300, 'min_samples_split': 2, ...</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.952215</td>\n",
       "      <td>0.009644</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.256663</td>\n",
       "      <td>0.005423</td>\n",
       "      <td>0.016246</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>300</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>log2</td>\n",
       "      <td>30</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 300, 'min_samples_split': 5, ...</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.952215</td>\n",
       "      <td>0.009644</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.181482</td>\n",
       "      <td>0.001787</td>\n",
       "      <td>0.011430</td>\n",
       "      <td>0.000491</td>\n",
       "      <td>200</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>30</td>\n",
       "      <td>entropy</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 10,...</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.952247</td>\n",
       "      <td>0.009427</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.167110</td>\n",
       "      <td>0.004185</td>\n",
       "      <td>0.011734</td>\n",
       "      <td>0.000599</td>\n",
       "      <td>200</td>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>auto</td>\n",
       "      <td>30</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 10,...</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.952247</td>\n",
       "      <td>0.005134</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.166213</td>\n",
       "      <td>0.001205</td>\n",
       "      <td>0.012380</td>\n",
       "      <td>0.000520</td>\n",
       "      <td>200</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>log2</td>\n",
       "      <td>10</td>\n",
       "      <td>gini</td>\n",
       "      <td>{'n_estimators': 200, 'min_samples_split': 2, ...</td>\n",
       "      <td>0.9375</td>\n",
       "      <td>0.9500</td>\n",
       "      <td>0.9625</td>\n",
       "      <td>0.936709</td>\n",
       "      <td>0.949367</td>\n",
       "      <td>0.947215</td>\n",
       "      <td>0.009495</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.257358      0.006316         0.016741        0.000857   \n",
       "1        0.165267      0.000899         0.012126        0.000220   \n",
       "2        0.272693      0.003310         0.017232        0.000574   \n",
       "3        0.091305      0.001192         0.006450        0.000390   \n",
       "4        0.080434      0.001489         0.006220        0.000399   \n",
       "..            ...           ...              ...             ...   \n",
       "95       0.238214      0.003218         0.016956        0.000206   \n",
       "96       0.256663      0.005423         0.016246        0.000399   \n",
       "97       0.181482      0.001787         0.011430        0.000491   \n",
       "98       0.167110      0.004185         0.011734        0.000599   \n",
       "99       0.166213      0.001205         0.012380        0.000520   \n",
       "\n",
       "   param_n_estimators param_min_samples_split param_min_samples_leaf  \\\n",
       "0                 300                       5                      2   \n",
       "1                 200                       5                      1   \n",
       "2                 300                      10                      4   \n",
       "3                 100                      10                      4   \n",
       "4                 100                       2                      4   \n",
       "..                ...                     ...                    ...   \n",
       "95                300                       2                      1   \n",
       "96                300                       5                      4   \n",
       "97                200                      10                      4   \n",
       "98                200                      10                      4   \n",
       "99                200                       2                      2   \n",
       "\n",
       "   param_max_features param_max_depth param_criterion  \\\n",
       "0                log2              30         entropy   \n",
       "1                log2              20            gini   \n",
       "2                auto              30         entropy   \n",
       "3                auto              10         entropy   \n",
       "4                log2            None            gini   \n",
       "..                ...             ...             ...   \n",
       "95               log2            None            gini   \n",
       "96               log2              30         entropy   \n",
       "97               sqrt              30         entropy   \n",
       "98               auto              30            gini   \n",
       "99               log2              10            gini   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'n_estimators': 300, 'min_samples_split': 5, ...             0.9625   \n",
       "1   {'n_estimators': 200, 'min_samples_split': 5, ...             0.9375   \n",
       "2   {'n_estimators': 300, 'min_samples_split': 10,...             0.9625   \n",
       "3   {'n_estimators': 100, 'min_samples_split': 10,...             0.9625   \n",
       "4   {'n_estimators': 100, 'min_samples_split': 2, ...             0.9375   \n",
       "..                                                ...                ...   \n",
       "95  {'n_estimators': 300, 'min_samples_split': 2, ...             0.9625   \n",
       "96  {'n_estimators': 300, 'min_samples_split': 5, ...             0.9625   \n",
       "97  {'n_estimators': 200, 'min_samples_split': 10,...             0.9625   \n",
       "98  {'n_estimators': 200, 'min_samples_split': 10,...             0.9625   \n",
       "99  {'n_estimators': 200, 'min_samples_split': 2, ...             0.9375   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0              0.9500             0.9625           0.949367   \n",
       "1              0.9500             0.9625           0.949367   \n",
       "2              0.9375             0.9625           0.962025   \n",
       "3              0.9375             0.9625           0.936709   \n",
       "4              0.9500             0.9625           0.936709   \n",
       "..                ...                ...                ...   \n",
       "95             0.9500             0.9625           0.949367   \n",
       "96             0.9500             0.9625           0.936709   \n",
       "97             0.9375             0.9625           0.949367   \n",
       "98             0.9500             0.9500           0.949367   \n",
       "99             0.9500             0.9625           0.936709   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.949367         0.954747        0.006335               11  \n",
       "1            0.924051         0.944684        0.013000               94  \n",
       "2            0.949367         0.954778        0.009996                7  \n",
       "3            0.936709         0.947184        0.012509               85  \n",
       "4            0.936709         0.944684        0.010242               95  \n",
       "..                ...              ...             ...              ...  \n",
       "95           0.936709         0.952215        0.009644               43  \n",
       "96           0.949367         0.952215        0.009644               39  \n",
       "97           0.949367         0.952247        0.009427               25  \n",
       "98           0.949367         0.952247        0.005134               25  \n",
       "99           0.949367         0.947215        0.009495               81  \n",
       "\n",
       "[100 rows x 19 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random_search.cv_results_\n",
    "tuning_result_rf_rs = pd.DataFrame(rf_random_search.cv_results_)\n",
    "tuning_result_rf_rs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "0b75aa76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 100,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 1,\n",
       " 'max_features': 'auto',\n",
       " 'max_depth': 10,\n",
       " 'criterion': 'entropy'}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random_search.best_params_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2dd79115",
   "metadata": {},
   "source": [
    "# Evaluate the optimized models for Randomized Search"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9a47346",
   "metadata": {},
   "source": [
    "# Decision Tree Randomized Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "91469ffd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Decision Tree Randomized Search Performance:\n",
      "Accuracy: 0.9590643274853801\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           B       0.95      0.98      0.97       108\n",
      "           M       0.97      0.92      0.94        63\n",
      "\n",
      "    accuracy                           0.96       171\n",
      "   macro avg       0.96      0.95      0.96       171\n",
      "weighted avg       0.96      0.96      0.96       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_dt_rs = dt_random_search.predict(xtest)\n",
    "print(\"\\n Decision Tree Randomized Search Performance:\")\n",
    "print(\"Accuracy:\", accuracy_score(ytest, y_pred_dt_rs))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(ytest, y_pred_dt_rs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03716f55",
   "metadata": {},
   "source": [
    "# Random Forest Randomized Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b9366d05",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Random Forest Randomized Search Performance:\n",
      "Accuracy: 0.9707602339181286\n",
      "Classification Report:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           B       0.96      0.99      0.98       108\n",
      "           M       0.98      0.94      0.96        63\n",
      "\n",
      "    accuracy                           0.97       171\n",
      "   macro avg       0.97      0.96      0.97       171\n",
      "weighted avg       0.97      0.97      0.97       171\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred_rf_rs = rf_random_search.predict(xtest)\n",
    "print(\"\\n Random Forest Randomized Search Performance:\")\n",
    "print(\"Accuracy:\", accuracy_score(ytest, y_pred_rf_rs))\n",
    "print(\"Classification Report:\")\n",
    "print(classification_report(ytest, y_pred_rf_rs))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2da8c8a0",
   "metadata": {},
   "source": [
    "# Post Pruning for Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5f54b398",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(criterion='entropy', min_samples_leaf=2,\n",
       "                       min_samples_split=10, random_state=42)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt_clf = dt_random_search.best_estimator_\n",
    "dt_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "18827f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.59375, 0.9166666666666666, 'concave points_mean <= 0.051\\nentropy = 0.954\\nsamples = 398\\nvalue = [249, 149]'),\n",
       " Text(0.3125, 0.75, 'radius_worst <= 16.83\\nentropy = 0.299\\nsamples = 245\\nvalue = [232, 13]'),\n",
       " Text(0.1875, 0.5833333333333334, 'area_se <= 48.7\\nentropy = 0.151\\nsamples = 230\\nvalue = [225, 5]'),\n",
       " Text(0.125, 0.4166666666666667, 'texture_worst <= 30.145\\nentropy = 0.101\\nsamples = 227\\nvalue = [224, 3]'),\n",
       " Text(0.0625, 0.25, 'entropy = 0.0\\nsamples = 197\\nvalue = [197, 0]'),\n",
       " Text(0.1875, 0.25, 'radius_worst <= 14.43\\nentropy = 0.469\\nsamples = 30\\nvalue = [27, 3]'),\n",
       " Text(0.125, 0.08333333333333333, 'entropy = 0.0\\nsamples = 21\\nvalue = [21, 0]'),\n",
       " Text(0.25, 0.08333333333333333, 'entropy = 0.918\\nsamples = 9\\nvalue = [6, 3]'),\n",
       " Text(0.25, 0.4166666666666667, 'entropy = 0.918\\nsamples = 3\\nvalue = [1, 2]'),\n",
       " Text(0.4375, 0.5833333333333334, 'texture_worst <= 19.91\\nentropy = 0.997\\nsamples = 15\\nvalue = [7, 8]'),\n",
       " Text(0.375, 0.4166666666666667, 'entropy = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
       " Text(0.5, 0.4166666666666667, 'fractal_dimension_worst <= 0.065\\nentropy = 0.722\\nsamples = 10\\nvalue = [2, 8]'),\n",
       " Text(0.4375, 0.25, 'entropy = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(0.5625, 0.25, 'entropy = 0.0\\nsamples = 8\\nvalue = [0, 8]'),\n",
       " Text(0.875, 0.75, 'perimeter_worst <= 114.45\\nentropy = 0.503\\nsamples = 153\\nvalue = [17, 136]'),\n",
       " Text(0.8125, 0.5833333333333334, 'texture_worst <= 25.655\\nentropy = 0.988\\nsamples = 39\\nvalue = [17, 22]'),\n",
       " Text(0.75, 0.4166666666666667, 'concave points_worst <= 0.166\\nentropy = 0.485\\nsamples = 19\\nvalue = [17, 2]'),\n",
       " Text(0.6875, 0.25, 'entropy = 0.0\\nsamples = 17\\nvalue = [17, 0]'),\n",
       " Text(0.8125, 0.25, 'entropy = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.875, 0.4166666666666667, 'entropy = 0.0\\nsamples = 20\\nvalue = [0, 20]'),\n",
       " Text(0.9375, 0.5833333333333334, 'entropy = 0.0\\nsamples = 114\\nvalue = [0, 114]')]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "plt.figure(figsize=(15,10))\n",
    "tree.plot_tree(dt_clf, filled=True, feature_names = x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6d9a8855",
   "metadata": {},
   "outputs": [],
   "source": [
    "#https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html\n",
    "pruning_path = dt_clf.cost_complexity_pruning_path(xtrain, ytrain) # clf = DecisionTreeClassifier  \n",
    "ccp_alpha_values = pruning_path.ccp_alphas "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "271fd245",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.01458594, 0.0181389 , 0.01942863, 0.02249811,\n",
       "       0.02254598, 0.02317526, 0.05933369, 0.07364967, 0.0966387 ,\n",
       "       0.57628768])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccp_alpha_values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d4619b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "clfs_list = []  # save diffrent classifiers\n",
    "\n",
    "for c in ccp_alpha_values:\n",
    "    clf = DecisionTreeClassifier(random_state=42, ccp_alpha=c)\n",
    "    clf.fit(xtrain, ytrain)\n",
    "    clfs_list.append(clf)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b3209bdf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[DecisionTreeClassifier(random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.014585942967809122, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.01813889685646639, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.019428626961936996, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.022498112473096667, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.0225459777507582, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.023175262447669234, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.059333693848085844, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.073649668769294, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.09663869999075987, random_state=42),\n",
       " DecisionTreeClassifier(ccp_alpha=0.5762876765620236, random_state=42)]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clfs_list "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "7ed03c7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_scores = [ clf.score(xtrain, ytrain) for clf in clfs_list ] \n",
    "test_scores = [ clf.score(xtest, ytest) for clf in clfs_list ] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "677abc36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.0,\n",
       " 0.9597989949748744,\n",
       " 0.9422110552763819,\n",
       " 0.9396984924623115,\n",
       " 0.9396984924623115,\n",
       " 0.9396984924623115,\n",
       " 0.9396984924623115,\n",
       " 0.9246231155778895,\n",
       " 0.9246231155778895,\n",
       " 0.9246231155778895,\n",
       " 0.6256281407035176]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "4b5c2a06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.9415204678362573,\n",
       " 0.9415204678362573,\n",
       " 0.9298245614035088,\n",
       " 0.9064327485380117,\n",
       " 0.9064327485380117,\n",
       " 0.9064327485380117,\n",
       " 0.9064327485380117,\n",
       " 0.8947368421052632,\n",
       " 0.8947368421052632,\n",
       " 0.8947368421052632,\n",
       " 0.631578947368421]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f7fd1948",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "50a08088",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1aa0b622040>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VlKkuodXlslYiorrQ1x2yqW7Ky8vh5uaGMWPG4LPPPjN2OUYzaNAguLm5YfXq1dX24R2yiYiIGqArV67gwIED6N+/P4qLi7FmzRpkZWVh/Pjxxi7NKO7cuYMDBw7g0KFDtX7eqiExHBEREdUzsViM2NhYzJgxA4IgwMPDAwcPHlTNH3ra9OrVC3fv3kVkZCQ6d+5s7HIYjoiIiOqbk5MTjh8/buwyTEZ9XzH4KJyQTURERKSG4YiIiIhIDcMRERERkRqGIyIiIiI1DEdEREREahiOiIiIauHvv/+GSCSq1fPG6MnGcERERPpVrgSykoA/vq/4s7x2D4WtqwEDBiAsLEyv6wwJCdF6hIWTkxPkcjk8PDz0ui0yPbzPERER6U9mArBvFqBQeyq7bWtgcCTg/pLx6tIDMzMz2NvbG7sMoygpKYFEIjF2GfWGI0dERKQfmQnAzomawQgAFPKK9swEvW8yJCQER44cwapVqyASiSASiVQ3FMzMzMTQoUPRuHFj2NnZITg4GHl5eaplv//+e3Tt2hVWVlZo3rw5Bg4ciPv372P+/PnYvHkzfvzxR9U6Dx8+rHVa7fDhwxCJRPj555/h5eUFa2tr+Pn54fz58xo1Llq0CK1atYKNjQ0mT56M2bNno0ePHtXuk1KpRGhoKFxdXWFlZYXOnTtj1apVWv1iYmLwzDPPwNLSEg4ODnj//fdVn927dw9vvfUW7OzsIJVK4eHhgZ9++gkAMH/+fK3tr1y5Ei4uLhrHdeTIkYiIiEDr1q3RqVMnAMC2bdvg5eUFGxsb2NvbY/z48aoH21Y6e/YsXnzxRdja2sLGxgZ9+/bFpUuXcPToUVhYWCAnJ0ej/0cffYR+/fpVezyMgeGIiIiqJghAyf3avR4ogL0zAVT1LPP/tu2bVdHvUevS4Xnoq1atgq+vL958803I5XLI5XLV6a/+/fujR48eSE1Nxb59+3Dz5k2MGTMGACCXyzFu3DhMmjQJ586dw+HDh/Hyyy9DEATMmDEDY8aMweDBg1Xr9PPzq7aGOXPmYNmyZUhNTYW5uTkmTZqk+iwuLg6ff/45IiMjkZaWhrZt2yI6OrrGfSovL4ejoyN27tyJzMxMfPrpp/jXv/6FnTt3qvpER0fjvffew1tvvYU//vgDCQkJ6NChg2r5IUOGIDk5Gdu2bUNmZiaWLFkCMzOzWh9XAPj5559x7tw5JCYmqoJVSUkJPvvsM5w+fRo//PADsrKyEBISolrm+vXr6NevH6RSKQ4dOoS0tDRMmjQJZWVl6NevH9q1a4etW7eq+peVlWHbtm144403dKrN0HhajYiIqlZaBCxuraeVCRUjSkucHt31XzcASaNarVUmk0EikcDa2lrjlFd0dDR69eqFxYsXq9piYmLg5OSECxcuoLCwEGVlZXj55Zfh7OwMAOjatauqr5WVFYqLi2t1Gu3zzz9H//79AQCzZ8/Giy++iAcPHkAqlSIqKgqhoaGqL/9PP/0UBw4cQGFhYbXrs7CwwIIFC1TvXV1dkZycjJ07d6rC3aJFi/DRRx9h2rRpqn69e/cGABw8eBApKSk4d+6casSnXbt2j9yPhzVq1AjffPONxuk09eDXrl07rF69Gt7e3igsLETjxo2xdu1ayGQy7NixAxYWFgCgqgEAQkNDsWnTJnz88ccAgP/7v/9DUVGRar9MBUeOiIiowUlLS8Mvv/yCxo0bq15dunQBAFy6dAndu3fHCy+8gK5du+LVV1/Fhg0bcPfu3Tptq1u3bqqfHRwcAEB1qun8+fPw9vbW6P/w+6p89dVX8PLyQsuWLdG4cWNs2LAB2dnZqnXfuHEDL7zwQpXLZmRkwNHRUSOU1EXXrl215hmlp6djxIgRcHZ2ho2NDQYMGAAAqtoyMjLQt29fVTB6WEhICP766y/8+uuvACoC65gxY9CoUe3CcH3hyBEREVXNwrpiFKc2riQDcaMf3e+17wHn6k9Rqbb7mMrLyzF8+HBERkZqfebg4AAzMzMkJiYiOTkZBw4cQFRUFObMmYOTJ0/C1dVVp22pBwGRSKTa/sNtlYRHnDbcuXMnPvzwQyxbtgy+vr6wsbHBF198gZMnTwKoGNWqyaM+F4vFWjWUlpZq9Xs4sNy/fx8BAQEICAjAtm3b0LJlS2RnZyMwMBAlJSW12narVq0wfPhwbNq0Ce3atcOePXtw+PDhGpcxBoYjIiKqmkhU69NbaP98xVVpCjmqnnckqvi8/fOAWLe5L48ikUigVGreLqBXr17YtWsXXFxcYG5e9VedSCSCv78//P398emnn8LZ2Rm7d+/G9OnTq1xnXXTu3BkpKSkIDg5WtaWmpta4TFJSEvz8/PDuu++q2i5duqT62cbGBi4uLvj555/x3HPPaS3frVs3XLt2DRcuXKhy9Khly5bIycmBIAiq4Fabezf9+eefyMvLw5IlS+Dk5FTlvnTr1g2bN29GaWlptaNHkydPxtixY+Ho6Ij27dvD39//kduubzytRkREj09sVnG5PgBA9NCH/30/eInegxEAuLi44OTJk/j777+Rl5eH8vJyvPfee7hz5w7GjRuHlJQUXL58GQcOHMCkSZOgVCpx8uRJLF68GKmpqcjOzsa///1v3Lp1C25ubqp1/v777zh//jzy8vKqHFmpjQ8++AAbN27E5s2bcfHiRSxatAi///671miSug4dOiA1NRX79+/HhQsXMHfuXPz2228afebPn49ly5Zh9erVuHjxIk6dOoWoqCgAQP/+/dGvXz+88sorSExMRFZWFvbu3Yt9+/YBqLgv1K1bt7B06VJcunQJa9euxd69ex+5L23btoVEIkFUVBQuX76MhIQEfPbZZxp93n//fSgUCowdOxapqam4ePEitm7dqnEFX2BgIGQyGRYtWmRyE7ErMRwREZF+uL8EjNkC2Dpottu2rmg30H2OZsyYATMzM7i7u6tO9bRu3RrHjx+HUqlEYGAgPDw8MG3aNMhkMojFYtja2uLo0aMYOnQoOnXqhE8++QTLli3DkCFDAABvvvkmOnfurJr3c/z48TrV9tprryE8PBwzZsxAr169VFd3SaXSapeZMmUKXn75ZQQFBcHHxwe3b9/WGEUCgNdffx0rV67EunXr8Mwzz2DYsGG4ePGi6vNdu3ahd+/eGDduHNzd3TFz5kzVSJibmxvWrVuHtWvXonv37khJScGMGTMeuS8tW7ZEbGwsvvvuO7i7u2PJkiX48ssvNfo0b94chw4dQmFhIfr37w9PT09s2LBBYxRJLBYjJCQESqUSEydOrNVxrG8i4VEnP59CCoUCMpkM+fn5sLW1NUoNRSVlcP90PwAgc2EgrCU8A0pEhvPgwQNkZWXB1dW1xi/uWilXVsxBKrwJNLarmGNkgBGjJ9WgQYNgb2+vcUn70+bNN9/EzZs3kZCg+72vavpd1df3N79xiYhIv8RmgGtfY1dhEoqKivDVV18hMDAQZmZm2L59Ow4ePIjExERjl2YU+fn5+O233xAXF4cff/zR2OVUi+GIiIjIQEQiEfbs2YNFixahuLgYnTt3xq5duzBw4EBjl2YUI0aMQEpKCt5++20MGjTI2OVUi+HIRCnL/3e2MyXrDvp2bAkzcfUT+IiIyPRYWVnh4MGDxi7DZJjiZftV4YRsE7TvjBwDlx9RvQ/Z9BuejTyEfWfkRqyKiIjo6cBwZGL2nZHjnW2ncFNRrNGek/8A72w7xYBERAbFa3TI1NXH7yjDkQlRlgtY8J/Mmh7biAX/ydQ45UZEpA+Vl1oXFRUZuRKimlXejVvXB+nqgnOOTEhK1h3I8x9U+7kAQJ7/AClZd+Dbvnn9FUZEDZ6ZmRmaNGmieiaYtbV1jTcqJDKG8vJy3Lp1C9bW1tXe+VwfGI5MSG5B9cGoLv2IiHRR+QT6yoBEZIrEYjHatm1r0PDOcGRCWtnU7sZrte1HRKQLkUgEBwcHtGrVqs6PyyAyNIlEArHYsLOCGI5MiLdrMzjIpMjJf1DlvCMAsLeVwtu1Wb3WRURPFzMzM4PO5yAydZyQbULMxCLMG+4OQPuxjZXCh3bh/Y6IiIgMiOHIxAz2cED0hF6wl1V96myQu109V0RERPR0MXo4WrdunerhcZ6enkhKSqqx/9q1a+Hm5gYrKyt07twZW7Zs0fg8NjYWIpFI6/XgwZMziXmwhwOOzXoe29/sg1VjeyD2jd7GLomIiOipYdQ5R/Hx8QgLC8O6devg7++Pr7/+GkOGDEFmZibatm2r1T86Ohrh4eHYsGEDevfujZSUFLz55pto2rQphg8frupna2uL8+fPayz72E+ZrmdmYpHqcv2ikjIjV0NERPT0MGo4Wr58OUJDQzF58mQAwMqVK7F//35ER0cjIiJCq//WrVvx9ttvIygoCADQrl07/Prrr4iMjNQIRyKRSHVJKhEREZEujHZaraSkBGlpaQgICNBoDwgIQHJycpXLFBcXa40AWVlZISUlReOy08LCQjg7O8PR0RHDhg1Denp6jbUUFxdDoVBovIiIiOjpZLRwlJeXB6VSCTs7zQnGdnZ2yMnJqXKZwMBAfPPNN0hLS4MgCEhNTUVMTAxKS0uRl5cHAOjSpQtiY2ORkJCA7du3QyqVwt/fHxcvXqy2loiICMhkMtXLyclJfztKRERETxSjT8h++A6XgiBUe9fLuXPnYsiQIejTpw8sLCwwYsQIhISEAPjfM1b69OmDCRMmoHv37ujbty927tyJTp06ISoqqtoawsPDkZ+fr3pdvXpVPztHRERETxyjhaMWLVrAzMxMa5QoNzdXazSpkpWVFWJiYlBUVIS///4b2dnZcHFxgY2NDVq0aFHlMmKxGL17965x5MjS0hK2trYaL4MoVwJZScAf31f8Wa6sdV8xygFUPH+ND54lIiIyHKNNyJZIJPD09ERiYiJGjRqlak9MTMSIESNqXNbCwgKOjo4AgB07dmDYsGHV3kpcEARkZGSga9eu+iu+LjITgH2zAMWN/7XZtgYGRwLuL9XY1xrAMctmWFA6ESGbAAeZFPOGu2Owh0P91U9ERPSUMOpptenTp+Obb75BTEwMzp07hw8//BDZ2dmYMmUKgIrTXRMnTlT1v3DhArZt24aLFy8iJSUFY8eOxZkzZ7B48WJVnwULFmD//v24fPkyMjIyEBoaioyMDNU6jSIzAdg5UTMYAYBCXtGemfDIvva4g2iLlQgUp0Ce/wBTtp3CmK+SkXThFkeSiIiI9Miol/IHBQXh9u3bWLhwIeRyOTw8PLBnzx44OzsDAORyObKzs1X9lUolli1bhvPnz8PCwgLPPfcckpOT4eLioupz7949vPXWW8jJyYFMJkPPnj1x9OhReHt71/fuVShXVowCVfm0NAGAqOLzdgMqmvbOrLKvWASUC8A8i61ILPZCOcRI+fsugmNS0EhihmVjunMkiYiISA9EgiBw2OEhCoUCMpkM+fn5jz//KCsJ2DxMP4X919iST/BrubtW+1cTejEgERHRU0tf399GHTl6KhTe1PsqW+Fele3zEs7Cv0MLgz2Y1srCrNorCYmIiBoKhiNDa1zLB8W+9n3Fn3GjH9k1F02qbL+pKEbX+QdqWZjuvJyb4rspvgxIRETUoDEcGZqzX8VVaQo5qp53JKr4vP3zFW9r7AvIhWZIKe9iqGprlHrlLv4pVcJawl8bIiJquPgtZ2his4rL9XdOBCCCZuj57wjM4CUV/YAa+laIKB2H8houMox9oze8XZvpqfgKRSVKeC06qNd1EhERmSqGo/rg/hIwZks19zlaonmfo+r6/ldiuWe1m3GQSdG3Y0uDzTkiIiJ6GjAc1Rf3l4AuLwJXkismaTe2qzjlVjliVE3f0+f+xPJjudgsWfrITcwb7s5gRERE9JgYjuqT2Axw7atT3+6ufTHB4QLwY0U46i3+E8fKu2mcWmtkaYZlr/I+R0RERPrAcGTqMhMw6NBM1dstkqXIt2iJuKbv4WKzAXi5lyP8DHj5PhER0dOG4ciUVT5K5KGJ2bLSPLybuwAY0B7o1NM4tRERETVQRn22GtXgkY8dAbBvdkU/IiIi0huGI1N1JbnKq9X+RwAU1yv6ERERkd7wtJqpqu1jRwzweJKHKcv/N3qVknWnxtsFKMsFpGTdQW7BA7SykcLbtVmd5kPpaz1ERES6YjgyVbV97Eht+9XRvjNyzEs4q3ofsuk3OMikmDfcXevquH1n5Fjwn0zI8x+o2qrr+6ht6mM9REREdcHTaqaq8rEjqG60RATYtqnoZyD7zsjxzrZTuKko1mjPyX+Ad7adwr4zcq2+6oGmur612ebjroeIiKiuOHJkqnR97IieKcsFLPhPZrXTwUUA5idkwr9DCwDAvISztepb06kxZbmgl/UQEZHps7IwM9kHmYsEQaj6CadPMYVCAZlMhvz8fNja2hq3mMyEah47Eqn52BE9O3HpNsZt+NVg6ycioqebl3NTfDfFV68BSV/f3xw5MnWVjxK5dAiIG13R9u5JQGrY0JZb8ODRnYiIiOoo9cpd/FOqhLXE9KKI6VVE2sRmmnOLDHQqTV0rG2mt+sW+0RtAxUTt2vT1dm1W7ecpWXf0sh4iIjJdRSVKeC06aOwyasRwRFXydm0GB5kUOfkPqpwDJAJgL5Oib8eWAFDrvjXNFerbsaVe1kNERPQ4eLUaVclMLMK84e4AtK+Xq3w/b7g7zMQinfrqa5tERESGwnBE1Rrs4YDoCb1gL9M8xWYvkyJ6Qi+New7p0ldf2yQiIjIEnlajGg32cMAgd/ta3a1al7762iYREZG+MRzRI5mJRfBt31zvfetjPURERLriaTUiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqeGE7CdFufJ/P++dBRTdBixtgG7jgHb96uWu2URERE8DhqMnQWYCsHfm/96nb/3fz7/HA5LGwMhogz6IloiI6GnB02qmLjMB2DkRKJBX36ekENgZXNGXiIiIHgtHjkxZuRLYNwuo8kljVdg7E2g3wHCn2CysARFvxEhERA0bw5Epu5IMKG7Uvn+BHFjiZLh6nPoAk/YxIBERUYPG02qmrPCmsSvQdPVXoLTI2FUQEREZFEeOTFljO92Xee17wNlPv3WUFAFfdtDvOomIiEyU0UeO1q1bB1dXV0ilUnh6eiIpKanG/mvXroWbmxusrKzQuXNnbNmyRavPrl274O7uDktLS7i7u2P37t2GKt+wnP0A29a172/bBmj/PCBppOeXteH2kYiIyMQYNRzFx8cjLCwMc+bMQXp6Ovr27YshQ4YgOzu7yv7R0dEIDw/H/PnzcfbsWSxYsADvvfce/vOf/6j6nDhxAkFBQQgODsbp06cRHByMMWPG4OTJk/W1W/ojNgMGRwKo5RyfwUt4vyMiIqLHJBIEoZaXQumfj48PevXqhejoaFWbm5sbRo4ciYiICK3+fn5+8Pf3xxdffKFqCwsLQ2pqKo4dOwYACAoKgkKhwN69e1V9Bg8ejKZNm2L79u21qkuhUEAmkyE/Px+2trZ13T39yUyouGqtusnZhr7PUcl9YPF/R7D+daNiNImIiKgOikrK4P7pfgBA5sJAWEv0N8NHX9/fRptzVFJSgrS0NMyePVujPSAgAMnJyVUuU1xcDKlUqtFmZWWFlJQUlJaWwsLCAidOnMCHH36o0ScwMBArV66stpbi4mIUFxer3isUCh33xsDcXwK6vPjfq9euA1dPAgU59XeHbPW7c19Jrjh1V932ypUVfQpvVsyZcvarW236Wg8REZGOjBaO8vLyoFQqYWenOenYzs4OOTk5VS4TGBiIb775BiNHjkSvXr2QlpaGmJgYlJaWIi8vDw4ODsjJydFpnQAQERGBBQsWPP5OGZLYDHDtW/Fz97H1t92H784dN7piHtTgSO2RqqpGuKrr+6ht6mM9REREdWD0Cdmih+6ZIwiCVluluXPnYsiQIejTpw8sLCwwYsQIhISEAADMzP43qqDLOgEgPDwc+fn5qtfVq1fruDcNTHV351bIK9rV78hd2ffhU39V9a3NNh93PURERHVktJGjFi1awMzMTGtEJzc3V2vkp5KVlRViYmLw9ddf4+bNm3BwcMD69ethY2ODFi1aAADs7e11WicAWFpawtLS8jH3qIGp8e7cAgBRxeftBlQ07Z1Zu741nRorV+pnPUREZLpKymCFB/gHpvu9a7RwJJFI4OnpicTERIwaNUrVnpiYiBEjRtS4rIWFBRwdHQEAO3bswLBhwyAWVwyC+fr6IjExUWPe0YEDB+Dnp+d7/zR0j7w7t1Dxea3uyK1L3/pYDxERGYs1gHNS4LfyToAQaOxyqmTUm0BOnz4dwcHB8PLygq+vL9avX4/s7GxMmTIFQMXpruvXr6vuZXThwgWkpKTAx8cHd+/exfLly3HmzBls3rxZtc5p06ahX79+iIyMxIgRI/Djjz/i4MGDqqvZqJZM7e7cRETUoPQWX0BRaRFgKTN2KVqMGo6CgoJw+/ZtLFy4EHK5HB4eHtizZw+cnZ0BAHK5XOOeR0qlEsuWLcP58+dhYWGB5557DsnJyXBxcVH18fPzw44dO/DJJ59g7ty5aN++PeLj4+Hj41Pfu/dkq+3duV/7vuLPuNG161vT3buvJOtnPUREZLKK7itgvaqLscuokVHvc2SqTO4+R8ZQrgRWelRMhK5yDpCo4gqysD8q3ta276PmHOljPUREZLKKCvNh/WXbip9nZMO6sf5GjvT1/W30q9XIRKnuzg1o36H7v+8r78itS199bZOIiMhAGI6oeu4vAWO2ALYOmu22rSva1e85pEtffW2TiIjIAIw654ieAOp3537U3ap16auvbRIREekZwxE9mvrdufXZtz7WQ0REpCOeViMiIiJSw3BEREREpIbhiIiIiEgNwxERERGRGoYjIiIiIjUMR0RERERqGI6IiIiI1DAcEREREalhOCIiIiJSw3BEREREpIbhiIiIiEgNwxERERGRGoYjIiIiIjUMR0RERERqGI6IiIiI1DAcEREREalhOCIiIiJSw3BEREREpIbhiIiIiEgNwxERERGRGoYjIiIiIjUMR0RERERqGI6IiIiI1DAcEREREalhOCIiIiJSw3BEREREpIbhiIiIiEgNwxERERGRGoYjIiIiIjUMR0RERERqjB6O1q1bB1dXV0ilUnh6eiIpKanG/nFxcejevTusra3h4OCAN954A7dv31Z9HhsbC5FIpPV68OCBoXeFiIiIGgCjhqP4+HiEhYVhzpw5SE9PR9++fTFkyBBkZ2dX2f/YsWOYOHEiQkNDcfbsWXz33Xf47bffMHnyZI1+tra2kMvlGi+pVFofu0RERERPOKOGo+XLlyM0NBSTJ0+Gm5sbVq5cCScnJ0RHR1fZ/9dff4WLiwumTp0KV1dXPPvss3j77beRmpqq0U8kEsHe3l7jRURERFQbRgtHJSUlSEtLQ0BAgEZ7QEAAkpOTq1zGz88P165dw549eyAIAm7evInvv/8eL774oka/wsJCODs7w9HREcOGDUN6enqNtRQXF0OhUGi8iIiI6OlktHCUl5cHpVIJOzs7jXY7Ozvk5ORUuYyfnx/i4uIQFBQEiUQCe3t7NGnSBFFRUao+Xbp0QWxsLBISErB9+3ZIpVL4+/vj4sWL1dYSEREBmUymejk5OelnJ4mIiOiJY/QJ2SKRSOO9IAhabZUyMzMxdepUfPrpp0hLS8O+ffuQlZWFKVOmqPr06dMHEyZMQPfu3dG3b1/s3LkTnTp10ghQDwsPD0d+fr7qdfXqVf3sHBERET1xzI214RYtWsDMzExrlCg3N1drNKlSREQE/P398fHHHwMAunXrhkaNGqFv375YtGgRHBwctJYRi8Xo3bt3jSNHlpaWsLS0fIy9ISIioobCaCNHEokEnp6eSExM1GhPTEyEn59flcsUFRVBLNYs2czMDEDFiFNVBEFARkZGlcGJiIiI6GFGGzkCgOnTpyM4OBheXl7w9fXF+vXrkZ2drTpNFh4ejuvXr2PLli0AgOHDh+PNN99EdHQ0AgMDIZfLERYWBm9vb7Ru3RoAsGDBAvTp0wcdO3aEQqHA6tWrkZGRgbVr1xptP4mIiOjJoXM4cnFxwaRJkxASEoK2bds+1saDgoJw+/ZtLFy4EHK5HB4eHtizZw+cnZ0BAHK5XOOeRyEhISgoKMCaNWvw0UcfoUmTJnj++ecRGRmp6nPv3j289dZbyMnJgUwmQ8+ePXH06FF4e3s/Vq1ERET0dBAJ1Z2PqkZUVBRiY2Nx+vRpPPfccwgNDcWoUaMa1JwdhUIBmUyG/Px82NraGrscIiKiBqOoMB/WX1YMrhTNyIZ1Y5ne1q2v72+d5xx98MEHSEtLQ1paGtzd3TF16lQ4ODjg/fffx6lTp+pcCBEREZEpqPOE7O7du2PVqlW4fv065s2bh2+++Qa9e/dG9+7dERMTU+0EaSIiIiJTVucJ2aWlpdi9ezc2bdqExMRE9OnTB6Ghobhx4wbmzJmDgwcP4ttvv9VnrUREREQGp3M4OnXqFDZt2oTt27fDzMwMwcHBWLFiBbp06aLqExAQgH79+um1UCIiIqL6oHM46t27NwYNGoTo6GiMHDkSFhYWWn3c3d0xduxYvRRIREREVJ90DkeXL19WXWpfnUaNGmHTpk11LoqIiIjIWHSekJ2bm4uTJ09qtZ88eRKpqal6KYqIiIjIWHQOR++9916VD2a9fv063nvvPb0URURERGQsOoejzMxM9OrVS6u9Z8+eyMzM1EtRRERERMaicziytLTEzZs3tdrlcjnMzY36qDYiIiKix6ZzOBo0aBDCw8ORn5+vart37x7+9a9/YdCgQXotjoiIiKi+6TzUs2zZMvTr1w/Ozs7o2bMnACAjIwN2dnbYunWr3gskIiIiqk86h6M2bdrg999/R1xcHE6fPg0rKyu88cYbGDduXJX3PCIiIiJ6ktRpklCjRo3w1ltv6bsWIiIiIqOr8wzqzMxMZGdno6SkRKP9pZdeeuyiiIiIiIylTnfIHjVqFP744w+IRCIIggAAEIlEAAClUqnfComIiIjqkc5Xq02bNg2urq64efMmrK2tcfbsWRw9ehReXl44fPiwAUokIiIiqj86jxydOHEChw4dQsuWLSEWiyEWi/Hss88iIiICU6dORXp6uiHqJCIiIqoXOo8cKZVKNG7cGADQokUL3LhxAwDg7OyM8+fP67c6IiIionqm88iRh4cHfv/9d7Rr1w4+Pj5YunQpJBIJ1q9fj3bt2hmiRiIiIqJ6o3M4+uSTT3D//n0AwKJFizBs2DD07dsXzZs3R3x8vN4LJCIiIqpPOoejwMBA1c/t2rVDZmYm7ty5g6ZNm6quWCMiIiJ6Uuk056isrAzm5uY4c+aMRnuzZs0YjIiIiKhB0CkcmZubw9nZmfcyIiIiogZL56vVPvnkE4SHh+POnTuGqIeIiIjIqHSec7R69Wr89ddfaN26NZydndGoUSONz0+dOqW34oiIiIjqm87haOTIkQYog4iIiMg06ByO5s2bZ4g6iIiIiEyCznOOiIiIiBoynUeOxGJxjZft80o2IiIiepLpHI52796t8b60tBTp6enYvHkzFixYoLfCiIiIiIxB53A0YsQIrbbRo0fjmWeeQXx8PEJDQ/VSGBEREZEx6G3OkY+PDw4ePKiv1REREREZhV7C0T///IOoqCg4OjrqY3VERERERqNzOGratCmaNWumejVt2hQ2NjaIiYnBF198oXMB69atg6urK6RSKTw9PZGUlFRj/7i4OHTv3h3W1tZwcHDAG2+8gdu3b2v02bVrF9zd3WFpaQl3d3eteVJERERE1dF5ztGKFSs0rlYTi8Vo2bIlfHx80LRpU53WFR8fj7CwMKxbtw7+/v74+uuvMWTIEGRmZqJt27Za/Y8dO4aJEydixYoVGD58OK5fv44pU6Zg8uTJqgB04sQJBAUF4bPPPsOoUaOwe/dujBkzBseOHYOPj4+uu0tERERPGZEgCIKxNu7j44NevXohOjpa1ebm5oaRI0ciIiJCq/+XX36J6OhoXLp0SdUWFRWFpUuX4urVqwCAoKAgKBQK7N27V9Vn8ODBaNq0KbZv315lHcXFxSguLla9VygUcHJyQn5+PmxtbR97P4mIiKhCUWE+rL+sGAApmpEN68Yyva1boVBAJpM99ve3zqfVNm3ahO+++06r/bvvvsPmzZtrvZ6SkhKkpaUhICBAoz0gIADJyclVLuPn54dr165hz549EAQBN2/exPfff48XX3xR1efEiRNa6wwMDKx2nQAQEREBmUymejk5OdV6P4iIiKhh0TkcLVmyBC1atNBqb9WqFRYvXlzr9eTl5UGpVMLOzk6j3c7ODjk5OVUu4+fnh7i4OAQFBUEikcDe3h5NmjRBVFSUqk9OTo5O6wSA8PBw5Ofnq16Vo1BERET09NE5HF25cgWurq5a7c7OzsjOzta5gIfvti0IQrV34M7MzMTUqVPx6aefIi0tDfv27UNWVhamTJlS53UCgKWlJWxtbTVeRERE9HTSeUJ2q1at8Pvvv8PFxUWj/fTp02jevHmt19OiRQuYmZlpjejk5uZqjfxUioiIgL+/Pz7++GMAQLdu3dCoUSP07dsXixYtgoODA+zt7XVaJxEREZE6nUeOxo4di6lTp+KXX36BUqmEUqnEoUOHMG3aNIwdO7bW65FIJPD09ERiYqJGe2JiIvz8/KpcpqioCGKxZslmZmYAKkaHAMDX11drnQcOHKh2nURERETqdB45WrRoEa5cuYIXXngB5uYVi5eXl2PixIk6zTkCgOnTpyM4OBheXl7w9fXF+vXrkZ2drTpNFh4ejuvXr2PLli0AgOHDh+PNN99EdHQ0AgMDIZfLERYWBm9vb7Ru3RoAMG3aNPTr1w+RkZEYMWIEfvzxRxw8eBDHjh3TdVeJiIjoKaRzOJJIJIiPj8eiRYuQkZEBKysrdO3aFc7OzjpvPCgoCLdv38bChQshl8vh4eGBPXv2qNYll8s15jGFhISgoKAAa9aswUcffYQmTZrg+eefR2RkpKqPn58fduzYgU8++QRz585F+/btER8fz3scERERUa0Y9T5Hpkpf90kgIiIiTQ3yPkejR4/GkiVLtNq/+OILvPrqq3UuhIiIiMgU6ByOjhw5onHTxUqDBw/G0aNH9VIUERERkbHoHI4KCwshkUi02i0sLKBQKPRSFBEREZGx6ByOPDw8EB8fr9W+Y8cOuLu766UoIiIiImPR+Wq1uXPn4pVXXsGlS5fw/PPPAwB+/vlnfPvtt/j+++/1XiARERFRfdI5HL300kv44YcfsHjxYnz//fewsrJC9+7dcejQIV7ZRURERE88ncMRALz44ouqSdn37t1DXFwcwsLCcPr0aSiVSr0WSERERFSfdJ5zVOnQoUOYMGECWrdujTVr1mDo0KFITU3VZ21ERERE9U6nkaNr164hNjYWMTExuH//PsaMGYPS0lLs2rWLk7GJiIioQaj1yNHQoUPh7u6OzMxMREVF4caNG4iKijJkbURERET1rtYjRwcOHMDUqVPxzjvvoGPHjoasiYiIiMhoaj1ylJSUhIKCAnh5ecHHxwdr1qzBrVu3DFkbERERUb2rdTjy9fXFhg0bIJfL8fbbb2PHjh1o06YNysvLkZiYiIKCAkPWSURERFQvdL5azdraGpMmTcKxY8fwxx9/4KOPPsKSJUvQqlUrvPTSS4aokYiIiKje1PlSfgDo3Lkzli5dimvXrmH79u36qomIiIjIaB4rHFUyMzPDyJEjkZCQoI/VERERERmNXsIRERERUUPBcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkxujhaN26dXB1dYVUKoWnpyeSkpKq7RsSEgKRSKT1euaZZ1R9YmNjq+zz4MGD+tgdIiIiesIZNRzFx8cjLCwMc+bMQXp6Ovr27YshQ4YgOzu7yv6rVq2CXC5Xva5evYpmzZrh1Vdf1ehna2ur0U8ul0MqldbHLhEREdETztyYG1++fDlCQ0MxefJkAMDKlSuxf/9+REdHIyIiQqu/TCaDTCZTvf/hhx9w9+5dvPHGGxr9RCIR7O3ta11HcXExiouLVe8VCoWuu0JEREQNhNFGjkpKSpCWloaAgACN9oCAACQnJ9dqHRs3bsTAgQPh7Oys0V5YWAhnZ2c4Ojpi2LBhSE9Pr3E9ERERquAlk8ng5OSk284QERFRg2G0cJSXlwelUgk7OzuNdjs7O+Tk5Dxyeblcjr1796pGnSp16dIFsbGxSEhIwPbt2yGVSuHv74+LFy9Wu67w8HDk5+erXlevXq3bThEREdETz6in1YCKU2DqBEHQaqtKbGwsmjRpgpEjR2q09+nTB3369FG99/f3R69evRAVFYXVq1dXuS5LS0tYWlrqXjwRERE1OEYbOWrRogXMzMy0Rolyc3O1RpMeJggCYmJiEBwcDIlEUmNfsViM3r171zhyRERERFTJaOFIIpHA09MTiYmJGu2JiYnw8/OrcdkjR47gr7/+Qmho6CO3IwgCMjIy4ODg8Fj1EhER0dPBqKfVpk+fjuDgYHh5ecHX1xfr169HdnY2pkyZAqBiLtD169exZcsWjeU2btwIHx8feHh4aK1zwYIF6NOnDzp27AiFQoHVq1cjIyMDa9eurZd9IiIioiebUcNRUFAQbt++jYULF0Iul8PDwwN79uxRXX0ml8u17nmUn5+PXbt2YdWqVVWu8969e3jrrbeQk5MDmUyGnj174ujRo/D29jb4/hAREdGTTyQIgmDsIkyNQqGATCZDfn4+bG1tjV0OERFRg1FUmA/rL9tW/DwjG9aNZY9Yovb09f1t9MeHEBEREZkShiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNUYPR+vWrYOrqyukUik8PT2RlJRUbd+QkBCIRCKt1zPPPKPRb9euXXB3d4elpSXc3d2xe/duQ+8GERERNRBGDUfx8fEICwvDnDlzkJ6ejr59+2LIkCHIzs6usv+qVasgl8tVr6tXr6JZs2Z49dVXVX1OnDiBoKAgBAcH4/Tp0wgODsaYMWNw8uTJ+totIiIieoKJBEEQjLVxHx8f9OrVC9HR0ao2Nzc3jBw5EhEREY9c/ocffsDLL7+MrKwsODs7AwCCgoKgUCiwd+9eVb/BgwejadOm2L59e5XrKS4uRnFxseq9QqGAk5MT8vPzYWtrW9fdIyIioocUFebD+su2FT/PyIZ1Y5ne1q1QKCCTyR77+9toI0clJSVIS0tDQECARntAQACSk5NrtY6NGzdi4MCBqmAEVIwcPbzOwMDAGtcZEREBmUymejk5OemwJ0RERNSQGC0c5eXlQalUws7OTqPdzs4OOTk5j1xeLpdj7969mDx5skZ7Tk6OzusMDw9Hfn6+6nX16lUd9oSIiIgaEnNjFyASiTTeC4Kg1VaV2NhYNGnSBCNHjnzsdVpaWsLS0rJ2BRMREVGDZrSRoxYtWsDMzExrRCc3N1dr5OdhgiAgJiYGwcHBkEgkGp/Z29vXaZ1EREREgBHDkUQigaenJxITEzXaExMT4efnV+OyR44cwV9//YXQ0FCtz3x9fbXWeeDAgUeuk4iIiAgw8mm16dOnIzg4GF5eXvD19cX69euRnZ2NKVOmAKiYC3T9+nVs2bJFY7mNGzfCx8cHHh4eWuucNm0a+vXrh8jISIwYMQI//vgjDh48iGPHjtXLPhEREdGTzajhKCgoCLdv38bChQshl8vh4eGBPXv2qK4+k8vlWvc8ys/Px65du7Bq1aoq1+nn54cdO3bgk08+wdy5c9G+fXvEx8fDx8fH4PtDRERETz6j3ufIVOnrPglERESkifc5IiIiInrCMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUGD0crVu3Dq6urpBKpfD09ERSUlKN/YuLizFnzhw4OzvD0tIS7du3R0xMjOrz2NhYiEQirdeDBw8MvStERETUAJgbc+Px8fEICwvDunXr4O/vj6+//hpDhgxBZmYm2rZtW+UyY8aMwc2bN7Fx40Z06NABubm5KCsr0+hja2uL8+fPa7RJpVKD7QcRERE1HEYNR8uXL0doaCgmT54MAFi5ciX279+P6OhoREREaPXft28fjhw5gsuXL6NZs2YAABcXF61+IpEI9vb2Bq2diIiIGiajnVYrKSlBWloaAgICNNoDAgKQnJxc5TIJCQnw8vLC0qVL0aZNG3Tq1AkzZszAP//8o9GvsLAQzs7OcHR0xLBhw5Cenl5jLcXFxVAoFBovIiIiejoZbeQoLy8PSqUSdnZ2Gu12dnbIycmpcpnLly/j2LFjkEql2L17N/Ly8vDuu+/izp07qnlHXbp0QWxsLLp27QqFQoFVq1bB398fp0+fRseOHatcb0REBBYsWKDfHSQiIqInktEnZItEIo33giBotVUqLy+HSCRCXFwcvL29MXToUCxfvhyxsbGq0aM+ffpgwoQJ6N69O/r27YudO3eiU6dOiIqKqraG8PBw5Ofnq15Xr17V3w4SERHRE8VoI0ctWrSAmZmZ1ihRbm6u1mhSJQcHB7Rp0wYymUzV5ubmBkEQcO3atSpHhsRiMXr37o2LFy9WW4ulpSUsLS3ruCdERETUkBht5EgikcDT0xOJiYka7YmJifDz86tyGX9/f9y4cQOFhYWqtgsXLkAsFsPR0bHKZQRBQEZGBhwcHPRXPBERETVYRj2tNn36dHzzzTeIiYnBuXPn8OGHHyI7OxtTpkwBUHG6a+LEiar+48ePR/PmzfHGG28gMzMTR48exccff4xJkybBysoKALBgwQLs378fly9fRkZGBkJDQ5GRkaFaJxEREVFNjHopf1BQEG7fvo2FCxdCLpfDw8MDe/bsgbOzMwBALpcjOztb1b9x48ZITEzEBx98AC8vLzRv3hxjxozBokWLVH3u3buHt956Czk5OZDJZOjZsyeOHj0Kb2/vet8/IiIievKIBEEQjF2EqVEoFJDJZMjPz4etra2xyyEiImowigrzYf1lxY2ei2Zkw7qx7BFL1J6+vr+NfrUaERERkSlhOCIiIiJSw3BEREREpIbhiIiIiEgNwxERERGRGoYjIiIiqj/lStWP4uwTGu9NBcMRERER1Y/MBEjX/+8pGNKdQcBKDyAzwYhFaWM4IiIiIsPLTAB2ToSoUK7ZrpADOyeaVEBiOCIiIiLDKlcC+2YBECDS+vC/96LeN9tkTrExHBEREZFhXUkGFDdq6CAAiusV/UwAwxEREREZVuFN/fYzMIYjIiIiMqzGdvrtZ2AMR0RERGRYzn74x8oe5dU86r5cAP6xsgec/aruUM8YjoiIiMiglBBjQelEANAKSJXvF5ROhNJEYolpVEFEREQNVkrWHewo7IF3SsOQg2Yan+WgOd4pDcOOwh5IybpjpAo1mRu7ACIiImrYcgseAAD2l3sjsdgL3uI/0Qr3kIsmSCnvgvL/jtVU9jM2hiMiIiIyqFY2UtXP5RDj13L3R/YzJp5WIyIiIoPydm0GB5m0ihtAVhABcJBJ4e3arJoe9YvhiIiIiAzKTCzCvOEVo0UPB6TK9/OGu8NMXF18ql8MR0RERGRwgz0cED2hF+xlmqfO7GVSRE/ohcEeDkaqTBvnHBEREVG9GOzhgEHu9kjJuoPcggdoZVNxKs1URowqMRwRERFRvTETi+Dbvrmxy6gRT6sRERERqWE4IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqeEdsqsgCAIAQKFQGLkSIiIiqq3K7+3K7/G6YjiqQkFBAQDAycnJyJUQERGRrgoKCiCTyeq8vEh43HjVAJWXl+PGjRuwsbGBSKTfh+EpFAo4OTnh6tWrsLW11eu6GzIet7rhcas7Hru64XGrGx63unn4uAmCgIKCArRu3Rpicd1nDnHkqApisRiOjo4G3YatrS3/AtQBj1vd8LjVHY9d3fC41Q2PW92oH7fHGTGqxAnZRERERGoYjoiIiIjUMBzVM0tLS8ybNw+WlpbGLuWJwuNWNzxudcdjVzc8bnXD41Y3hjpunJBNREREpIYjR0RERERqGI6IiIiI1DAcEREREalhOCIiIiJSw3BkAOvWrYOrqyukUik8PT2RlJRUY/8jR47A09MTUqkU7dq1w1dffVVPlZoWXY6bXC7H+PHj0blzZ4jFYoSFhdVfoSZGl+P273//G4MGDULLli1ha2sLX19f7N+/vx6rNR26HLdjx47B398fzZs3h5WVFbp06YIVK1bUY7WmRdd/4yodP34c5ubm6NGjh2ELNFG6HLfDhw9DJBJpvf788896rNg06Pr7VlxcjDlz5sDZ2RmWlpZo3749YmJidNuoQHq1Y8cOwcLCQtiwYYOQmZkpTJs2TWjUqJFw5cqVKvtfvnxZsLa2FqZNmyZkZmYKGzZsECwsLITvv/++nis3Ll2PW1ZWljB16lRh8+bNQo8ePYRp06bVb8EmQtfjNm3aNCEyMlJISUkRLly4IISHhwsWFhbCqVOn6rly49L1uJ06dUr49ttvhTNnzghZWVnC1q1bBWtra+Hrr7+u58qNT9djV+nevXtCu3bthICAAKF79+71U6wJ0fW4/fLLLwIA4fz584JcLle9ysrK6rly46rL79tLL70k+Pj4CImJiUJWVpZw8uRJ4fjx4zptl+FIz7y9vYUpU6ZotHXp0kWYPXt2lf1nzpwpdOnSRaPt7bffFvr06WOwGk2RrsdNXf/+/Z/acPQ4x62Su7u7sGDBAn2XZtL0cdxGjRolTJgwQd+lmby6HrugoCDhk08+EebNm/dUhiNdj1tlOLp79249VGe6dD1ue/fuFWQymXD79u3H2i5Pq+lRSUkJ0tLSEBAQoNEeEBCA5OTkKpc5ceKEVv/AwECkpqaitLTUYLWakrocN9LPcSsvL0dBQQGaNWtmiBJNkj6OW3p6OpKTk9G/f39DlGiy6nrsNm3ahEuXLmHevHmGLtEkPc7vXM+ePeHg4IAXXngBv/zyiyHLNDl1OW4JCQnw8vLC0qVL0aZNG3Tq1AkzZszAP//8o9O2+eBZPcrLy4NSqYSdnZ1Gu52dHXJycqpcJicnp8r+ZWVlyMvLg4ODg8HqNRV1OW6kn+O2bNky3L9/H2PGjDFEiSbpcY6bo6Mjbt26hbKyMsyfPx+TJ082ZKkmpy7H7uLFi5g9ezaSkpJgbv50fuXU5bg5ODhg/fr18PT0RHFxMbZu3YoXXngBhw8fRr9+/eqjbKOry3G7fPkyjh07BqlUit27dyMvLw/vvvsu7ty5o9O8o6fzN9XARCKRxntBELTaHtW/qvaGTtfjRhXqety2b9+O+fPn48cff0SrVq0MVZ7JqstxS0pKQmFhIX799VfMnj0bHTp0wLhx4wxZpkmq7bFTKpUYP348FixYgE6dOtVXeSZLl9+5zp07o3Pnzqr3vr6+uHr1Kr788sunJhxV0uW4lZeXQyQSIS4uDjKZDACwfPlyjB49GmvXroWVlVWttslwpEctWrSAmZmZVqLNzc3VSr6V7O3tq+xvbm6O5s2bG6xWU1KX40aPd9zi4+MRGhqK7777DgMHDjRkmSbncY6bq6srAKBr1664efMm5s+f/1SFI12PXUFBAVJTU5Geno73338fQMWXlyAIMDc3x4EDB/D888/XS+3GpK9/4/r06YNt27bpuzyTVZfj5uDggDZt2qiCEQC4ublBEARcu3YNHTt2rNW2OedIjyQSCTw9PZGYmKjRnpiYCD8/vyqX8fX11ep/4MABeHl5wcLCwmC1mpK6HDeq+3Hbvn07QkJC8O233+LFF180dJkmR1+/b4IgoLi4WN/lmTRdj52trS3++OMPZGRkqF5TpkxB586dkZGRAR8fn/oq3aj09TuXnp7+VEy1qFSX4+bv748bN26gsLBQ1XbhwgWIxWI4OjrWfuOPNZ2btFRedrhx40YhMzNTCAsLExo1aiT8/fffgiAIwuzZs4Xg4GBV/8pL+T/88EMhMzNT2Lhx41N9KX9tj5sgCEJ6erqQnp4ueHp6CuPHjxfS09OFs2fPGqN8o9H1uH377beCubm5sHbtWo3Lg+/du2esXTAKXY/bmjVrhISEBOHChQvChQsXhJiYGMHW1laYM2eOsXbBaOryd1Xd03q1mq7HbcWKFcLu3buFCxcuCGfOnBFmz54tABB27dplrF0wCl2PW0FBgeDo6CiMHj1aOHv2rHDkyBGhY8eOwuTJk3XaLsORAaxdu1ZwdnYWJBKJ0KtXL+HIkSOqz15//XWhf//+Gv0PHz4s9OzZU5BIJIKLi4sQHR1dzxWbBl2PGwCtl7Ozc/0WbQJ0OW79+/ev8ri9/vrr9V+4kely3FavXi0888wzgrW1tWBrayv07NlTWLdunaBUKo1QufHp+ndV3dMajgRBt+MWGRkptG/fXpBKpULTpk2FZ599Vvi///s/I1RtfLr+vp07d04YOHCgYGVlJTg6OgrTp08XioqKdNqmSBD+O/uXiIiIiDjniIiIiEgdwxERERGRGoYjIiIiIjUMR0RERERqGI6IiIiI1DAcEREREalhOCIiIiJSw3BEREREpIbhiIgajMOHD0MkEuHevXu1Xmb+/Pno0aOHwWoioicPwxERPXGSk5NhZmaGwYMHG7sUImqAGI6I6IkTExODDz74AMeOHUN2draxyyGiBobhiIieKPfv38fOnTvxzjvvYNiwYYiNja22b2xsLJo0aYIffvgBnTp1glQqxaBBg3D16lWtvlu3boWLiwtkMhnGjh2LgoIC1Wf79u3Ds88+iyZNmqB58+YYNmwYLl26ZIjdIyITwHBERE+U+Ph4dO7cGZ07d8aECROwadMm1PT87KKiInz++efYvHkzjh8/DoVCgbFjx2r0uXTpEn744Qf89NNP+Omnn3DkyBEsWbJE9fn9+/cxffp0/Pbbb/j5558hFosxatQolJeXG2w/ich4zI1dABGRLjZu3IgJEyYAAAYPHozCwkL8/PPPGDhwYJX9S0tLsWbNGvj4+AAANm/eDDc3N6SkpMDb2xsAUF5ejtjYWNjY2AAAgoOD8fPPP+Pzzz8HALzyyitaNbRq1QqZmZnw8PAwyH4SkfFw5IiInhjnz59HSkqKauTH3NwcQUFBiImJqXYZc3NzeHl5qd536dIFTZo0wblz51RtLi4uqmAEAA4ODsjNzVW9v3TpEsaPH4927drB1tYWrq6uAMD5TkQNFEeOiOiJsXHjRpSVlaFNmzaqNkEQYGFhgbt371a7nEgkqrHNwsJC6zP1U2bDhw+Hk5MTNmzYgNatW6O8vBweHh4oKSl5nN0hIhPFkSMieiKUlZVhy5YtWLZsGTIyMlSv06dPw9nZGXFxcdUul5qaqnp//vx53Lt3D126dKnVdm/fvo1z587hk08+wQsvvAA3N7cagxgRPfk4ckRET4SffvoJd+/eRWhoKGQymcZno0ePxsaNG7FixQqt5SwsLPDBBx9g9erVsLCwwPvvv48+ffqo5hs9StOmTdG8eXOsX78eDg4OyM7OxuzZs/WyT0RkmjhyRERPhI0bN2LgwIFawQiomDCdkZGBU6dOaX1mbW2NWbNmYfz48fD19YWVlRV27NhR6+2KxWLs2LEDaWlp8PDwwIcffogvvvjisfaFiEybSKjpGlgioidYbGwswsLCdHqcCBERR46IiIiI1DAcEREREanhaTUiIiIiNRw5IiIiIlLDcERERESkhuGIiIiISA3DEREREZEahiMiIiIiNQxHRERERGoYjoiIiIjUMBwRERERqfl/TscuKs+8ny4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.set_xlabel(\"Alpha\")\n",
    "ax.set_ylabel(\"Accuracy\")\n",
    "\n",
    "ax.plot(ccp_alpha_values, train_scores, marker='o', label=\"training accuracy\", drawstyle=\"steps-post\")\n",
    "ax.plot(ccp_alpha_values, test_scores, marker='o', label=\"testing accuracy\", drawstyle=\"steps-post\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3785723d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.01, random_state=42)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = DecisionTreeClassifier(random_state=42, ccp_alpha=0.01)\n",
    "clf.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c1883e46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9649122807017544"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=clf.predict(xtest)\n",
    "accuracy_score(ytest, pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "e9f2bc5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.4230769230769231, 0.9, 'concave points_mean <= 0.051\\ngini = 0.468\\nsamples = 398\\nvalue = [249, 149]'),\n",
       " Text(0.15384615384615385, 0.7, 'radius_worst <= 16.83\\ngini = 0.1\\nsamples = 245\\nvalue = [232, 13]'),\n",
       " Text(0.07692307692307693, 0.5, 'gini = 0.043\\nsamples = 230\\nvalue = [225, 5]'),\n",
       " Text(0.23076923076923078, 0.5, 'texture_mean <= 16.19\\ngini = 0.498\\nsamples = 15\\nvalue = [7, 8]'),\n",
       " Text(0.15384615384615385, 0.3, 'gini = 0.0\\nsamples = 5\\nvalue = [5, 0]'),\n",
       " Text(0.3076923076923077, 0.3, 'gini = 0.32\\nsamples = 10\\nvalue = [2, 8]'),\n",
       " Text(0.6923076923076923, 0.7, 'texture_worst <= 20.405\\ngini = 0.198\\nsamples = 153\\nvalue = [17, 136]'),\n",
       " Text(0.5384615384615384, 0.5, 'radius_worst <= 18.145\\ngini = 0.408\\nsamples = 14\\nvalue = [10, 4]'),\n",
       " Text(0.46153846153846156, 0.3, 'gini = 0.0\\nsamples = 10\\nvalue = [10, 0]'),\n",
       " Text(0.6153846153846154, 0.3, 'gini = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
       " Text(0.8461538461538461, 0.5, 'area_worst <= 718.65\\ngini = 0.096\\nsamples = 139\\nvalue = [7, 132]'),\n",
       " Text(0.7692307692307693, 0.3, 'texture_worst <= 25.925\\ngini = 0.497\\nsamples = 13\\nvalue = [7, 6]'),\n",
       " Text(0.6923076923076923, 0.1, 'gini = 0.0\\nsamples = 7\\nvalue = [7, 0]'),\n",
       " Text(0.8461538461538461, 0.1, 'gini = 0.0\\nsamples = 6\\nvalue = [0, 6]'),\n",
       " Text(0.9230769230769231, 0.3, 'gini = 0.0\\nsamples = 126\\nvalue = [0, 126]')]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,10))\n",
    "tree.plot_tree(clf, filled=True, feature_names = x.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9991a16",
   "metadata": {},
   "source": [
    "# XGBoost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "780a295d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#random & grid\n",
    "#before performance & after performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6034df5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56d7b50a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "0ab2fed7",
   "metadata": {},
   "source": [
    "# ADA Boost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f9443f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#random & grid\n",
    "#before performance & after performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c01c4d75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9a61d1c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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