DT Practical Example (Post And Pre Prunning).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing all the required libraries\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_curve, auc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "cancer = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename'])\n"
     ]
    }
   ],
   "source": [
    "print(cancer.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst radius</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>25.38</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>24.99</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>23.57</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>14.91</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>22.54</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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  mean radius mean texture mean perimeter mean area mean smoothness  \\\n",
       "0       17.99        10.38         122.80    1001.0         0.11840   \n",
       "1       20.57        17.77         132.90    1326.0         0.08474   \n",
       "2       19.69        21.25         130.00    1203.0         0.10960   \n",
       "3       11.42        20.38          77.58     386.1         0.14250   \n",
       "4       20.29        14.34         135.10    1297.0         0.10030   \n",
       "\n",
       "  mean compactness mean concavity mean concave points mean symmetry  \\\n",
       "0          0.27760         0.3001             0.14710        0.2419   \n",
       "1          0.07864         0.0869             0.07017        0.1812   \n",
       "2          0.15990         0.1974             0.12790        0.2069   \n",
       "3          0.28390         0.2414             0.10520        0.2597   \n",
       "4          0.13280         0.1980             0.10430        0.1809   \n",
       "\n",
       "  mean fractal dimension  ... worst radius worst texture worst perimeter  \\\n",
       "0                0.07871  ...        25.38         17.33          184.60   \n",
       "1                0.05667  ...        24.99         23.41          158.80   \n",
       "2                0.05999  ...        23.57         25.53          152.50   \n",
       "3                0.09744  ...        14.91         26.50           98.87   \n",
       "4                0.05883  ...        22.54         16.67          152.20   \n",
       "\n",
       "  worst area worst smoothness worst compactness worst concavity  \\\n",
       "0     2019.0           0.1622            0.6656          0.7119   \n",
       "1     1956.0           0.1238            0.1866          0.2416   \n",
       "2     1709.0           0.1444            0.4245          0.4504   \n",
       "3      567.7           0.2098            0.8663          0.6869   \n",
       "4     1575.0           0.1374            0.2050          0.4000   \n",
       "\n",
       "  worst concave points worst symmetry worst fractal dimension  \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",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.DataFrame(cancer.data, columns=[cancer.feature_names])\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\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>0.2419</td>\n",
       "      <td>0.07871</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\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>0.1812</td>\n",
       "      <td>0.05667</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\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>0.2069</td>\n",
       "      <td>0.05999</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\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>0.2597</td>\n",
       "      <td>0.09744</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>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\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>0.1809</td>\n",
       "      <td>0.05883</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>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  mean radius mean texture mean perimeter mean area mean smoothness  \\\n",
       "0       17.99        10.38         122.80    1001.0         0.11840   \n",
       "1       20.57        17.77         132.90    1326.0         0.08474   \n",
       "2       19.69        21.25         130.00    1203.0         0.10960   \n",
       "3       11.42        20.38          77.58     386.1         0.14250   \n",
       "4       20.29        14.34         135.10    1297.0         0.10030   \n",
       "\n",
       "  mean compactness mean concavity mean concave points mean symmetry  \\\n",
       "0          0.27760         0.3001             0.14710        0.2419   \n",
       "1          0.07864         0.0869             0.07017        0.1812   \n",
       "2          0.15990         0.1974             0.12790        0.2069   \n",
       "3          0.28390         0.2414             0.10520        0.2597   \n",
       "4          0.13280         0.1980             0.10430        0.1809   \n",
       "\n",
       "  mean fractal dimension  ... worst texture worst perimeter worst area  \\\n",
       "0                0.07871  ...         17.33          184.60     2019.0   \n",
       "1                0.05667  ...         23.41          158.80     1956.0   \n",
       "2                0.05999  ...         25.53          152.50     1709.0   \n",
       "3                0.09744  ...         26.50           98.87      567.7   \n",
       "4                0.05883  ...         16.67          152.20     1575.0   \n",
       "\n",
       "  worst smoothness worst compactness worst concavity worst concave points  \\\n",
       "0           0.1622            0.6656          0.7119               0.2654   \n",
       "1           0.1238            0.1866          0.2416               0.1860   \n",
       "2           0.1444            0.4245          0.4504               0.2430   \n",
       "3           0.2098            0.8663          0.6869               0.2575   \n",
       "4           0.1374            0.2050          0.4000               0.1625   \n",
       "\n",
       "  worst symmetry worst fractal dimension target  \n",
       "0         0.4601                 0.11890      0  \n",
       "1         0.2750                 0.08902      0  \n",
       "2         0.3613                 0.08758      0  \n",
       "3         0.6638                 0.17300      0  \n",
       "4         0.2364                 0.07678      0  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.assign(target=pd.Series(cancer.target))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 31)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Independent features and dependent features\n",
    "X=data.iloc[:,:-1]  \n",
    "y=data.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "## train test split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Split the data into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(426, 30)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "classifier=DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Hyperparameter Tunning\n",
    "parameter = {\n",
    "    'criterion': ['gini', 'entropy'],\n",
    "    'splitter': ['best', 'random'],\n",
    "    'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12],\n",
    "    'max_features': ['auto', 'sqrt', 'log2']\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 132 candidates, totalling 660 fits\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=best, score=0.953, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=best, score=0.871, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=best, score=0.929, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=best, score=0.824, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=random, score=0.686, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=random, score=0.882, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=random, score=0.765, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=random, score=0.647, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=auto, splitter=random, score=0.835, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=best, score=0.930, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=best, score=0.871, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=best, score=0.882, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=best, score=0.824, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=random, score=0.628, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=random, score=0.753, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=random, score=0.800, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=random, score=0.871, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=sqrt, splitter=random, score=0.906, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=best, score=0.907, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=best, score=0.871, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=best, score=0.894, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=best, score=0.800, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=random, score=0.628, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=random, score=0.624, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=random, score=0.835, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=random, score=0.800, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=1, max_features=log2, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=1, max_features=log2, splitter=random, score=0.871, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=best, score=0.942, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=best, score=0.894, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=best, score=0.894, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=best, score=0.847, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=random, score=0.802, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=random, score=0.835, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=random, score=0.847, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=random, score=0.976, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=auto, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=auto, splitter=random, score=0.859, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=best, score=0.953, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=best, score=0.847, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=best, score=0.929, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=best, score=0.894, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=random, score=0.733, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=random, score=0.906, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=random, score=0.859, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=random, score=0.894, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=sqrt, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=sqrt, splitter=random, score=0.718, total=   0.0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:    0.0s remaining:    0.0s\n",
      "[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:    0.0s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=log2, splitter=best, score=0.953, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=log2, splitter=best, score=0.859, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=best ...\n",
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      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=log2, splitter=best, score=0.929, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=2, max_features=log2, splitter=best, score=0.788, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=log2, splitter=random, score=0.884, total=   0.0s\n",
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      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=random .\n",
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      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=random .\n",
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      "[CV] criterion=gini, max_depth=2, max_features=log2, splitter=random .\n",
      "[CV]  criterion=gini, max_depth=2, max_features=log2, splitter=random, score=0.918, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=3, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=3, max_features=auto, splitter=best, score=0.942, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=3, max_features=auto, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=3, max_features=auto, splitter=best, score=0.941, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=3, max_features=auto, splitter=best ...\n",
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      "[CV]  criterion=gini, max_depth=3, max_features=auto, splitter=best, score=0.906, total=   0.0s\n",
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      "[CV] criterion=gini, max_depth=3, max_features=sqrt, splitter=best ...\n",
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      "[CV] criterion=gini, max_depth=3, max_features=sqrt, splitter=best ...\n",
      "[CV]  criterion=gini, max_depth=3, max_features=sqrt, splitter=best, score=0.882, total=   0.0s\n",
      "[CV] criterion=gini, max_depth=3, max_features=sqrt, splitter=best ...\n",
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      "[CV]  criterion=gini, max_depth=3, max_features=sqrt, splitter=best, score=0.871, total=   0.0s\n",
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      "[CV] criterion=gini, max_depth=3, max_features=log2, splitter=best ...\n",
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    {
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     ]
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    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "[CV]  criterion=entropy, max_depth=11, max_features=sqrt, splitter=random, score=0.929, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=best, score=0.942, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=best, score=0.929, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=best, score=0.894, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=random, score=0.942, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=random, score=0.847, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=random \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=random, score=0.906, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=random, score=0.953, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=11, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=11, max_features=log2, splitter=random, score=0.894, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=best, score=0.907, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=best, score=0.894, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=best, score=0.906, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=best, score=0.941, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=best, score=0.835, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=random, score=0.919, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=random, score=0.882, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=random, score=0.941, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=random, score=0.965, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=auto, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=auto, splitter=random, score=0.894, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=best, score=0.977, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=best, score=0.894, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=best, score=0.953, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=best, score=0.988, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=best, score=0.882, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=random, score=0.942, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=random, score=0.918, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=random, score=0.882, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=random, score=0.953, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=sqrt, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=sqrt, splitter=random, score=0.906, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=best, score=0.930, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=best, score=0.941, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=best \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=best, score=0.918, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=random, score=0.942, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=random, score=0.906, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=random, score=0.929, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=random, score=0.976, total=   0.0s\n",
      "[CV] criterion=entropy, max_depth=12, max_features=log2, splitter=random \n",
      "[CV]  criterion=entropy, max_depth=12, max_features=log2, splitter=random, score=0.894, total=   0.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 660 out of 660 | elapsed:    2.5s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [1, 2, 3, 4, 5, 6, 7, 8, 10, 11, 12],\n",
       "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             scoring='accuracy', verbose=4)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the GridSearchCV object\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "# make a GridSearchCV object\n",
    "grid_search = GridSearchCV(estimator = classifier,\n",
    "                  param_grid = parameter,\n",
    "                  scoring = \"accuracy\", #sklearn.metrics.SCORERS.keys()\n",
    "                  cv = 5,\n",
    "                  verbose = 4)\n",
    "\n",
    "# Perform hyperparameter tuning on the classifier\n",
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'criterion': 'entropy', 'max_depth': 6, 'max_features': 'log2', 'splitter': 'best'}\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9436114911080713\n"
     ]
    }
   ],
   "source": [
    "print(grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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_criterion</th>\n",
       "      <th>param_max_depth</th>\n",
       "      <th>param_max_features</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>100</th>\n",
       "      <td>0.002467</td>\n",
       "      <td>0.000585</td>\n",
       "      <td>0.000770</td>\n",
       "      <td>0.000412</td>\n",
       "      <td>entropy</td>\n",
       "      <td>6</td>\n",
       "      <td>log2</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 6, 'max_...</td>\n",
       "      <td>0.965116</td>\n",
       "      <td>0.929412</td>\n",
       "      <td>0.929412</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.905882</td>\n",
       "      <td>0.943611</td>\n",
       "      <td>0.029259</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.002011</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>0.001056</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>gini</td>\n",
       "      <td>11</td>\n",
       "      <td>log2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 11, 'max_fe...</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>0.976471</td>\n",
       "      <td>0.929412</td>\n",
       "      <td>0.905882</td>\n",
       "      <td>0.941231</td>\n",
       "      <td>0.029828</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>0.002796</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.000855</td>\n",
       "      <td>0.000293</td>\n",
       "      <td>entropy</td>\n",
       "      <td>12</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 12, 'max...</td>\n",
       "      <td>0.976744</td>\n",
       "      <td>0.894118</td>\n",
       "      <td>0.952941</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.938878</td>\n",
       "      <td>0.043050</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>0.002049</td>\n",
       "      <td>0.000056</td>\n",
       "      <td>0.001209</td>\n",
       "      <td>0.000390</td>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 10, 'max...</td>\n",
       "      <td>0.918605</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.917647</td>\n",
       "      <td>0.936662</td>\n",
       "      <td>0.027311</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.000392</td>\n",
       "      <td>0.001105</td>\n",
       "      <td>0.000128</td>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>auto</td>\n",
       "      <td>best</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 5, 'max_...</td>\n",
       "      <td>0.953488</td>\n",
       "      <td>0.894118</td>\n",
       "      <td>0.952941</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.936580</td>\n",
       "      <td>0.021904</td>\n",
       "      <td>5</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.001992</td>\n",
       "      <td>0.000014</td>\n",
       "      <td>0.001026</td>\n",
       "      <td>0.000050</td>\n",
       "      <td>gini</td>\n",
       "      <td>1</td>\n",
       "      <td>auto</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 1, 'max_fea...</td>\n",
       "      <td>0.686047</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.835294</td>\n",
       "      <td>0.763092</td>\n",
       "      <td>0.088135</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000795</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>gini</td>\n",
       "      <td>1</td>\n",
       "      <td>log2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 1, 'max_fea...</td>\n",
       "      <td>0.627907</td>\n",
       "      <td>0.623529</td>\n",
       "      <td>0.835294</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>0.870588</td>\n",
       "      <td>0.751464</td>\n",
       "      <td>0.105078</td>\n",
       "      <td>129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>0.002210</td>\n",
       "      <td>0.000413</td>\n",
       "      <td>0.001212</td>\n",
       "      <td>0.000431</td>\n",
       "      <td>entropy</td>\n",
       "      <td>1</td>\n",
       "      <td>auto</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 1, 'max_...</td>\n",
       "      <td>0.720930</td>\n",
       "      <td>0.811765</td>\n",
       "      <td>0.635294</td>\n",
       "      <td>0.835294</td>\n",
       "      <td>0.658824</td>\n",
       "      <td>0.732421</td>\n",
       "      <td>0.079826</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>0.002256</td>\n",
       "      <td>0.000377</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.000319</td>\n",
       "      <td>entropy</td>\n",
       "      <td>1</td>\n",
       "      <td>sqrt</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 1, 'max_...</td>\n",
       "      <td>0.744186</td>\n",
       "      <td>0.517647</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.658824</td>\n",
       "      <td>0.847059</td>\n",
       "      <td>0.730014</td>\n",
       "      <td>0.132118</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>0.001631</td>\n",
       "      <td>0.000802</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.000428</td>\n",
       "      <td>entropy</td>\n",
       "      <td>1</td>\n",
       "      <td>log2</td>\n",
       "      <td>random</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 1, 'max_...</td>\n",
       "      <td>0.895349</td>\n",
       "      <td>0.576471</td>\n",
       "      <td>0.635294</td>\n",
       "      <td>0.835294</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.729658</td>\n",
       "      <td>0.119623</td>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>132 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "100       0.002467      0.000585         0.000770        0.000412   \n",
       "59        0.002011      0.000021         0.001056        0.000075   \n",
       "128       0.002796      0.000397         0.000855        0.000293   \n",
       "116       0.002049      0.000056         0.001209        0.000390   \n",
       "90        0.002789      0.000392         0.001105        0.000128   \n",
       "..             ...           ...              ...             ...   \n",
       "1         0.001992      0.000014         0.001026        0.000050   \n",
       "5         0.001677      0.000399         0.000795        0.000398   \n",
       "67        0.002210      0.000413         0.001212        0.000431   \n",
       "69        0.002256      0.000377         0.000900        0.000319   \n",
       "71        0.001631      0.000802         0.001249        0.000428   \n",
       "\n",
       "    param_criterion param_max_depth param_max_features param_splitter  \\\n",
       "100         entropy               6               log2           best   \n",
       "59             gini              11               log2         random   \n",
       "128         entropy              12               sqrt           best   \n",
       "116         entropy              10               sqrt           best   \n",
       "90          entropy               5               auto           best   \n",
       "..              ...             ...                ...            ...   \n",
       "1              gini               1               auto         random   \n",
       "5              gini               1               log2         random   \n",
       "67          entropy               1               auto         random   \n",
       "69          entropy               1               sqrt         random   \n",
       "71          entropy               1               log2         random   \n",
       "\n",
       "                                                params  split0_test_score  \\\n",
       "100  {'criterion': 'entropy', 'max_depth': 6, 'max_...           0.965116   \n",
       "59   {'criterion': 'gini', 'max_depth': 11, 'max_fe...           0.976744   \n",
       "128  {'criterion': 'entropy', 'max_depth': 12, 'max...           0.976744   \n",
       "116  {'criterion': 'entropy', 'max_depth': 10, 'max...           0.918605   \n",
       "90   {'criterion': 'entropy', 'max_depth': 5, 'max_...           0.953488   \n",
       "..                                                 ...                ...   \n",
       "1    {'criterion': 'gini', 'max_depth': 1, 'max_fea...           0.686047   \n",
       "5    {'criterion': 'gini', 'max_depth': 1, 'max_fea...           0.627907   \n",
       "67   {'criterion': 'entropy', 'max_depth': 1, 'max_...           0.720930   \n",
       "69   {'criterion': 'entropy', 'max_depth': 1, 'max_...           0.744186   \n",
       "71   {'criterion': 'entropy', 'max_depth': 1, 'max_...           0.895349   \n",
       "\n",
       "     split1_test_score  split2_test_score  split3_test_score  \\\n",
       "100           0.929412           0.929412           0.988235   \n",
       "59            0.917647           0.976471           0.929412   \n",
       "128           0.894118           0.952941           0.988235   \n",
       "116           0.917647           0.941176           0.988235   \n",
       "90            0.894118           0.952941           0.941176   \n",
       "..                 ...                ...                ...   \n",
       "1             0.882353           0.764706           0.647059   \n",
       "5             0.623529           0.835294           0.800000   \n",
       "67            0.811765           0.635294           0.835294   \n",
       "69            0.517647           0.882353           0.658824   \n",
       "71            0.576471           0.635294           0.835294   \n",
       "\n",
       "     split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "100           0.905882         0.943611        0.029259                1  \n",
       "59            0.905882         0.941231        0.029828                2  \n",
       "128           0.882353         0.938878        0.043050                3  \n",
       "116           0.917647         0.936662        0.027311                4  \n",
       "90            0.941176         0.936580        0.021904                5  \n",
       "..                 ...              ...             ...              ...  \n",
       "1             0.835294         0.763092        0.088135              128  \n",
       "5             0.870588         0.751464        0.105078              129  \n",
       "67            0.658824         0.732421        0.079826              130  \n",
       "69            0.847059         0.730014        0.132118              131  \n",
       "71            0.705882         0.729658        0.119623              132  \n",
       "\n",
       "[132 rows x 17 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(grid_search.cv_results_)\n",
    "df = df.sort_values(\"rank_test_score\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "##prediction\n",
    "y_pred=grid_search.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## accuracy score\n",
    "from sklearn.metrics import accuracy_score,classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9440559440559441\n"
     ]
    }
   ],
   "source": [
    "score=accuracy_score(y_pred,y_test)\n",
    "print(score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.94      0.93        53\n",
      "           1       0.97      0.94      0.96        90\n",
      "\n",
      "    accuracy                           0.94       143\n",
      "   macro avg       0.94      0.94      0.94       143\n",
      "weighted avg       0.94      0.94      0.94       143\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_pred,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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