ml_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.tree import export_graphviz\n",
    "from sklearn.model_selection import cross_validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('pyzbar_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Color</th>\n",
       "      <th>PPI</th>\n",
       "      <th>Backgrounds</th>\n",
       "      <th>Temperature(Kelvin)</th>\n",
       "      <th>Mirror</th>\n",
       "      <th>Flip</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Orange</td>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Orange</td>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Orange</td>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Orange</td>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Orange</td>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>3000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Color    PPI Backgrounds  Temperature(Kelvin)  Mirror   Flip  Outcome\n",
       "0  Orange  244.6       Black                 1000    True   True  success\n",
       "1  Orange  244.6       Black                 1000    True  False  failure\n",
       "2  Orange  244.6       Black                 1000   False   True  failure\n",
       "3  Orange  244.6       Black                 1000   False  False  success\n",
       "4  Orange  244.6       Black                 3000    True   True  success"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['Color'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>PPI</th>\n",
       "      <th>Backgrounds</th>\n",
       "      <th>Temperature(Kelvin)</th>\n",
       "      <th>Mirror</th>\n",
       "      <th>Flip</th>\n",
       "      <th>Outcome</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>1000</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>failure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>1000</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>244.6</td>\n",
       "      <td>Black</td>\n",
       "      <td>3000</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>success</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PPI Backgrounds  Temperature(Kelvin)  Mirror   Flip  Outcome\n",
       "0  244.6       Black                 1000    True   True  success\n",
       "1  244.6       Black                 1000    True  False  failure\n",
       "2  244.6       Black                 1000   False   True  failure\n",
       "3  244.6       Black                 1000   False  False  success\n",
       "4  244.6       Black                 3000    True   True  success"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = df.columns[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = df.columns[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Outcome'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 6 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   PPI                  1000 non-null   float64\n",
      " 1   Backgrounds          1000 non-null   object \n",
      " 2   Temperature(Kelvin)  1000 non-null   int64  \n",
      " 3   Mirror               1000 non-null   bool   \n",
      " 4   Flip                 1000 non-null   bool   \n",
      " 5   Outcome              1000 non-null   object \n",
      "dtypes: bool(2), float64(1), int64(1), object(2)\n",
      "memory usage: 33.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_df = pd.get_dummies(df,columns=['Backgrounds'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   PPI                      1000 non-null   float64\n",
      " 1   Temperature(Kelvin)      1000 non-null   int64  \n",
      " 2   Mirror                   1000 non-null   bool   \n",
      " 3   Flip                     1000 non-null   bool   \n",
      " 4   Outcome                  1000 non-null   object \n",
      " 5   Backgrounds_Absent       1000 non-null   uint8  \n",
      " 6   Backgrounds_Black        1000 non-null   uint8  \n",
      " 7   Backgrounds_Striped      1000 non-null   uint8  \n",
      " 8   Backgrounds_Surrounding  1000 non-null   uint8  \n",
      " 9   Backgrounds_White        1000 non-null   uint8  \n",
      "dtypes: bool(2), float64(1), int64(1), object(1), uint8(5)\n",
      "memory usage: 30.4+ KB\n"
     ]
    }
   ],
   "source": [
    "encoded_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_df[\"Outcome\"] = encoded_df['Outcome'].replace(\"failure\",0)\n",
    "encoded_df[\"Outcome\"] = encoded_df['Outcome'].replace(\"success\",1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1\n",
       "1      0\n",
       "2      0\n",
       "3      1\n",
       "4      1\n",
       "      ..\n",
       "995    0\n",
       "996    0\n",
       "997    0\n",
       "998    0\n",
       "999    0\n",
       "Name: Outcome, Length: 1000, dtype: int64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.to_numeric(encoded_df['Outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "encoded_df['Mirror'] = encoded_df['Mirror']*1\n",
    "encoded_df['Flip'] = encoded_df['Flip']*1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>PPI</th>\n",
       "      <th>Temperature(Kelvin)</th>\n",
       "      <th>Mirror</th>\n",
       "      <th>Flip</th>\n",
       "      <th>Outcome</th>\n",
       "      <th>Backgrounds_Absent</th>\n",
       "      <th>Backgrounds_Black</th>\n",
       "      <th>Backgrounds_Striped</th>\n",
       "      <th>Backgrounds_Surrounding</th>\n",
       "      <th>Backgrounds_White</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>244.6</td>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>244.6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>244.6</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>244.6</td>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>244.6</td>\n",
       "      <td>3000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     PPI  Temperature(Kelvin)  Mirror  Flip  Outcome  Backgrounds_Absent  \\\n",
       "0  244.6                 1000       1     1        1                   0   \n",
       "1  244.6                 1000       1     0        0                   0   \n",
       "2  244.6                 1000       0     1        0                   0   \n",
       "3  244.6                 1000       0     0        1                   0   \n",
       "4  244.6                 3000       1     1        1                   0   \n",
       "\n",
       "   Backgrounds_Black  Backgrounds_Striped  Backgrounds_Surrounding  \\\n",
       "0                  1                    0                        0   \n",
       "1                  1                    0                        0   \n",
       "2                  1                    0                        0   \n",
       "3                  1                    0                        0   \n",
       "4                  1                    0                        0   \n",
       "\n",
       "   Backgrounds_White  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  0  \n",
       "4                  0  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 10 columns):\n",
      " #   Column                   Non-Null Count  Dtype  \n",
      "---  ------                   --------------  -----  \n",
      " 0   PPI                      1000 non-null   float64\n",
      " 1   Temperature(Kelvin)      1000 non-null   int64  \n",
      " 2   Mirror                   1000 non-null   int32  \n",
      " 3   Flip                     1000 non-null   int32  \n",
      " 4   Outcome                  1000 non-null   int64  \n",
      " 5   Backgrounds_Absent       1000 non-null   uint8  \n",
      " 6   Backgrounds_Black        1000 non-null   uint8  \n",
      " 7   Backgrounds_Striped      1000 non-null   uint8  \n",
      " 8   Backgrounds_Surrounding  1000 non-null   uint8  \n",
      " 9   Backgrounds_White        1000 non-null   uint8  \n",
      "dtypes: float64(1), int32(2), int64(2), uint8(5)\n",
      "memory usage: 36.3 KB\n"
     ]
    }
   ],
   "source": [
    "encoded_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PPI                        0.496503\n",
       "Temperature(Kelvin)        0.037538\n",
       "Mirror                     0.000000\n",
       "Flip                       0.000000\n",
       "Outcome                    1.000000\n",
       "Backgrounds_Absent         0.028044\n",
       "Backgrounds_Black          0.028044\n",
       "Backgrounds_Striped       -0.012019\n",
       "Backgrounds_Surrounding   -0.022035\n",
       "Backgrounds_White         -0.022035\n",
       "dtype: float64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "encoded_df.corrwith(encoded_df['Outcome'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1ab19d5e408>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(encoded_df.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = encoded_df.iloc[:,:]\n",
    "X = X.drop(['Outcome'],axis=1)\n",
    "y = encoded_df['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score=nan,\n",
       "             estimator=DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None,\n",
       "                                              criterion='gini', max_depth=None,\n",
       "                                              max_features=None,\n",
       "                                              max_leaf_nodes=None,\n",
       "                                              min_impurity_decrease=0.0,\n",
       "                                              min_impurity_split=None,\n",
       "                                              min_samples_leaf=1,\n",
       "                                              min_samples_split=2,\n",
       "                                              min_weight_fraction_leaf=0.0,\n",
       "                                              presort='deprecated',\n",
       "                                              random_state=None,\n",
       "                                              splitter='best'),\n",
       "             iid='deprecated', n_jobs=None,\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [4, 5, 6, 7, 8, 9, 10, 11, 12, 15, 20, 30,\n",
       "                                       40, 50, 70, 90, 120, 150]},\n",
       "             pre_dispatch='2*n_jobs', refit=True, return_train_score=False,\n",
       "             scoring=None, verbose=0)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_tree = {'criterion':['gini','entropy'],'max_depth':[4,5,6,7,8,9,10,11,12,15,20,30,40,50,70,90,120,150]}\n",
    "grid_tree = GridSearchCV(DecisionTreeClassifier(),param_tree,cv=5,return_train_score=False)\n",
    "grid_tree.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'entropy', 'max_depth': 5}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_tree.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "tree_df = pd.DataFrame(grid_tree.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>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>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>0.004587</td>\n",
       "      <td>4.875607e-04</td>\n",
       "      <td>0.001993</td>\n",
       "      <td>1.089272e-03</td>\n",
       "      <td>gini</td>\n",
       "      <td>4</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 4}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.199138</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003581</td>\n",
       "      <td>4.879691e-04</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>4.883495e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>5</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 5}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.002992</td>\n",
       "      <td>1.784161e-07</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>1.507891e-07</td>\n",
       "      <td>gini</td>\n",
       "      <td>6</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 6}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.011569</td>\n",
       "      <td>1.715426e-02</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>2.611745e-07</td>\n",
       "      <td>gini</td>\n",
       "      <td>7</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 7}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.003590</td>\n",
       "      <td>4.885582e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.988505e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>8</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 8}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.003391</td>\n",
       "      <td>4.886360e-04</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>4.886166e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>9</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 9}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.003790</td>\n",
       "      <td>3.978968e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.988981e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>10</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 10}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.003590</td>\n",
       "      <td>4.884414e-04</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>4.885387e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>11</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 11}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.003790</td>\n",
       "      <td>3.986359e-04</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>4.885776e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>12</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 12}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.003790</td>\n",
       "      <td>3.988505e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.990889e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>15</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 15}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.003591</td>\n",
       "      <td>4.885777e-04</td>\n",
       "      <td>0.001596</td>\n",
       "      <td>4.886361e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>20</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 20}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.003989</td>\n",
       "      <td>9.536743e-08</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>2.431402e-07</td>\n",
       "      <td>gini</td>\n",
       "      <td>30</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 30}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.003790</td>\n",
       "      <td>3.987789e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.988505e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>40</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 40}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.003990</td>\n",
       "      <td>1.091496e-03</td>\n",
       "      <td>0.001196</td>\n",
       "      <td>3.987022e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>50</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 50}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.003791</td>\n",
       "      <td>3.963471e-04</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>2.745025e-06</td>\n",
       "      <td>gini</td>\n",
       "      <td>70</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 70}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.003590</td>\n",
       "      <td>4.902166e-04</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>4.883635e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>90</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 90}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.003588</td>\n",
       "      <td>4.908545e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.981119e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>120</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 120}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.003590</td>\n",
       "      <td>4.885387e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.988982e-04</td>\n",
       "      <td>gini</td>\n",
       "      <td>150</td>\n",
       "      <td>{'criterion': 'gini', 'max_depth': 150}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.003990</td>\n",
       "      <td>3.245997e-06</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>3.200142e-06</td>\n",
       "      <td>entropy</td>\n",
       "      <td>4</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 4}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.71</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.814</td>\n",
       "      <td>0.141789</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.003989</td>\n",
       "      <td>1.215701e-06</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>1.061967e-06</td>\n",
       "      <td>entropy</td>\n",
       "      <td>5</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 5}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.892</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.003989</td>\n",
       "      <td>1.144409e-06</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>1.617033e-06</td>\n",
       "      <td>entropy</td>\n",
       "      <td>6</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 6}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.002995</td>\n",
       "      <td>6.155818e-06</td>\n",
       "      <td>0.001403</td>\n",
       "      <td>4.943100e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>7</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 7}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.003189</td>\n",
       "      <td>4.000234e-04</td>\n",
       "      <td>0.001192</td>\n",
       "      <td>3.896005e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>8</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 8}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.003391</td>\n",
       "      <td>4.885582e-04</td>\n",
       "      <td>0.001397</td>\n",
       "      <td>4.883055e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>9</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 9}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.003596</td>\n",
       "      <td>4.950514e-04</td>\n",
       "      <td>0.001191</td>\n",
       "      <td>4.022584e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>10</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 10}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.003390</td>\n",
       "      <td>4.883864e-04</td>\n",
       "      <td>0.001204</td>\n",
       "      <td>3.954940e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>11</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 11}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.003590</td>\n",
       "      <td>4.883833e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.987792e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>12</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 12}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.003587</td>\n",
       "      <td>4.934659e-04</td>\n",
       "      <td>0.001398</td>\n",
       "      <td>4.878840e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>15</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 15}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>0.003590</td>\n",
       "      <td>4.885204e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.998285e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>20</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 20}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.003390</td>\n",
       "      <td>4.898050e-04</td>\n",
       "      <td>0.001199</td>\n",
       "      <td>3.977002e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>30</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 30}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>0.003587</td>\n",
       "      <td>4.927903e-04</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>6.503192e-07</td>\n",
       "      <td>entropy</td>\n",
       "      <td>40</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 40}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>0.003790</td>\n",
       "      <td>3.990650e-04</td>\n",
       "      <td>0.001197</td>\n",
       "      <td>3.990889e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>50</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 50}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>0.003790</td>\n",
       "      <td>3.987074e-04</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>4.885777e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>70</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 70}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>0.003590</td>\n",
       "      <td>4.883635e-04</td>\n",
       "      <td>0.001396</td>\n",
       "      <td>4.884609e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>90</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 90}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>0.003989</td>\n",
       "      <td>1.907349e-07</td>\n",
       "      <td>0.000997</td>\n",
       "      <td>1.168008e-07</td>\n",
       "      <td>entropy</td>\n",
       "      <td>120</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 120}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>0.003790</td>\n",
       "      <td>3.989460e-04</td>\n",
       "      <td>0.001795</td>\n",
       "      <td>3.987570e-04</td>\n",
       "      <td>entropy</td>\n",
       "      <td>150</td>\n",
       "      <td>{'criterion': 'entropy', 'max_depth': 150}</td>\n",
       "      <td>0.66</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.139485</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.004587  4.875607e-04         0.001993    1.089272e-03   \n",
       "1        0.003581  4.879691e-04         0.001406    4.883495e-04   \n",
       "2        0.002992  1.784161e-07         0.000997    1.507891e-07   \n",
       "3        0.011569  1.715426e-02         0.000997    2.611745e-07   \n",
       "4        0.003590  4.885582e-04         0.001197    3.988505e-04   \n",
       "5        0.003391  4.886360e-04         0.001396    4.886166e-04   \n",
       "6        0.003790  3.978968e-04         0.001197    3.988981e-04   \n",
       "7        0.003590  4.884414e-04         0.001396    4.885387e-04   \n",
       "8        0.003790  3.986359e-04         0.001396    4.885776e-04   \n",
       "9        0.003790  3.988505e-04         0.001197    3.990889e-04   \n",
       "10       0.003591  4.885777e-04         0.001596    4.886361e-04   \n",
       "11       0.003989  9.536743e-08         0.000997    2.431402e-07   \n",
       "12       0.003790  3.987789e-04         0.001197    3.988505e-04   \n",
       "13       0.003990  1.091496e-03         0.001196    3.987022e-04   \n",
       "14       0.003791  3.963471e-04         0.000996    2.745025e-06   \n",
       "15       0.003590  4.902166e-04         0.001396    4.883635e-04   \n",
       "16       0.003588  4.908545e-04         0.001197    3.981119e-04   \n",
       "17       0.003590  4.885387e-04         0.001197    3.988982e-04   \n",
       "18       0.003990  3.245997e-06         0.000996    3.200142e-06   \n",
       "19       0.003989  1.215701e-06         0.000997    1.061967e-06   \n",
       "20       0.003989  1.144409e-06         0.000997    1.617033e-06   \n",
       "21       0.002995  6.155818e-06         0.001403    4.943100e-04   \n",
       "22       0.003189  4.000234e-04         0.001192    3.896005e-04   \n",
       "23       0.003391  4.885582e-04         0.001397    4.883055e-04   \n",
       "24       0.003596  4.950514e-04         0.001191    4.022584e-04   \n",
       "25       0.003390  4.883864e-04         0.001204    3.954940e-04   \n",
       "26       0.003590  4.883833e-04         0.001197    3.987792e-04   \n",
       "27       0.003587  4.934659e-04         0.001398    4.878840e-04   \n",
       "28       0.003590  4.885204e-04         0.001197    3.998285e-04   \n",
       "29       0.003390  4.898050e-04         0.001199    3.977002e-04   \n",
       "30       0.003587  4.927903e-04         0.000997    6.503192e-07   \n",
       "31       0.003790  3.990650e-04         0.001197    3.990889e-04   \n",
       "32       0.003790  3.987074e-04         0.001396    4.885777e-04   \n",
       "33       0.003590  4.883635e-04         0.001396    4.884609e-04   \n",
       "34       0.003989  1.907349e-07         0.000997    1.168008e-07   \n",
       "35       0.003790  3.989460e-04         0.001795    3.987570e-04   \n",
       "\n",
       "   param_criterion param_max_depth  \\\n",
       "0             gini               4   \n",
       "1             gini               5   \n",
       "2             gini               6   \n",
       "3             gini               7   \n",
       "4             gini               8   \n",
       "5             gini               9   \n",
       "6             gini              10   \n",
       "7             gini              11   \n",
       "8             gini              12   \n",
       "9             gini              15   \n",
       "10            gini              20   \n",
       "11            gini              30   \n",
       "12            gini              40   \n",
       "13            gini              50   \n",
       "14            gini              70   \n",
       "15            gini              90   \n",
       "16            gini             120   \n",
       "17            gini             150   \n",
       "18         entropy               4   \n",
       "19         entropy               5   \n",
       "20         entropy               6   \n",
       "21         entropy               7   \n",
       "22         entropy               8   \n",
       "23         entropy               9   \n",
       "24         entropy              10   \n",
       "25         entropy              11   \n",
       "26         entropy              12   \n",
       "27         entropy              15   \n",
       "28         entropy              20   \n",
       "29         entropy              30   \n",
       "30         entropy              40   \n",
       "31         entropy              50   \n",
       "32         entropy              70   \n",
       "33         entropy              90   \n",
       "34         entropy             120   \n",
       "35         entropy             150   \n",
       "\n",
       "                                        params  split0_test_score  \\\n",
       "0        {'criterion': 'gini', 'max_depth': 4}               0.66   \n",
       "1        {'criterion': 'gini', 'max_depth': 5}               0.66   \n",
       "2        {'criterion': 'gini', 'max_depth': 6}               0.66   \n",
       "3        {'criterion': 'gini', 'max_depth': 7}               0.66   \n",
       "4        {'criterion': 'gini', 'max_depth': 8}               0.66   \n",
       "5        {'criterion': 'gini', 'max_depth': 9}               0.66   \n",
       "6       {'criterion': 'gini', 'max_depth': 10}               0.66   \n",
       "7       {'criterion': 'gini', 'max_depth': 11}               0.66   \n",
       "8       {'criterion': 'gini', 'max_depth': 12}               0.66   \n",
       "9       {'criterion': 'gini', 'max_depth': 15}               0.66   \n",
       "10      {'criterion': 'gini', 'max_depth': 20}               0.66   \n",
       "11      {'criterion': 'gini', 'max_depth': 30}               0.66   \n",
       "12      {'criterion': 'gini', 'max_depth': 40}               0.66   \n",
       "13      {'criterion': 'gini', 'max_depth': 50}               0.66   \n",
       "14      {'criterion': 'gini', 'max_depth': 70}               0.66   \n",
       "15      {'criterion': 'gini', 'max_depth': 90}               0.66   \n",
       "16     {'criterion': 'gini', 'max_depth': 120}               0.66   \n",
       "17     {'criterion': 'gini', 'max_depth': 150}               0.66   \n",
       "18    {'criterion': 'entropy', 'max_depth': 4}               0.66   \n",
       "19    {'criterion': 'entropy', 'max_depth': 5}               0.66   \n",
       "20    {'criterion': 'entropy', 'max_depth': 6}               0.66   \n",
       "21    {'criterion': 'entropy', 'max_depth': 7}               0.66   \n",
       "22    {'criterion': 'entropy', 'max_depth': 8}               0.66   \n",
       "23    {'criterion': 'entropy', 'max_depth': 9}               0.66   \n",
       "24   {'criterion': 'entropy', 'max_depth': 10}               0.66   \n",
       "25   {'criterion': 'entropy', 'max_depth': 11}               0.66   \n",
       "26   {'criterion': 'entropy', 'max_depth': 12}               0.66   \n",
       "27   {'criterion': 'entropy', 'max_depth': 15}               0.66   \n",
       "28   {'criterion': 'entropy', 'max_depth': 20}               0.66   \n",
       "29   {'criterion': 'entropy', 'max_depth': 30}               0.66   \n",
       "30   {'criterion': 'entropy', 'max_depth': 40}               0.66   \n",
       "31   {'criterion': 'entropy', 'max_depth': 50}               0.66   \n",
       "32   {'criterion': 'entropy', 'max_depth': 70}               0.66   \n",
       "33   {'criterion': 'entropy', 'max_depth': 90}               0.66   \n",
       "34  {'criterion': 'entropy', 'max_depth': 120}               0.66   \n",
       "35  {'criterion': 'entropy', 'max_depth': 150}               0.66   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0                0.71                1.0               0.47   \n",
       "1                1.00                1.0               0.73   \n",
       "2                1.00                1.0               0.73   \n",
       "3                1.00                1.0               0.73   \n",
       "4                1.00                1.0               0.73   \n",
       "5                1.00                1.0               0.73   \n",
       "6                1.00                1.0               0.73   \n",
       "7                1.00                1.0               0.73   \n",
       "8                1.00                1.0               0.73   \n",
       "9                1.00                1.0               0.73   \n",
       "10               1.00                1.0               0.73   \n",
       "11               1.00                1.0               0.73   \n",
       "12               1.00                1.0               0.73   \n",
       "13               1.00                1.0               0.73   \n",
       "14               1.00                1.0               0.73   \n",
       "15               1.00                1.0               0.73   \n",
       "16               1.00                1.0               0.73   \n",
       "17               1.00                1.0               0.73   \n",
       "18               0.71                1.0               0.73   \n",
       "19               1.00                1.0               1.00   \n",
       "20               1.00                1.0               0.73   \n",
       "21               1.00                1.0               0.73   \n",
       "22               1.00                1.0               0.73   \n",
       "23               1.00                1.0               0.73   \n",
       "24               1.00                1.0               0.73   \n",
       "25               1.00                1.0               0.73   \n",
       "26               1.00                1.0               0.73   \n",
       "27               1.00                1.0               0.73   \n",
       "28               1.00                1.0               0.73   \n",
       "29               1.00                1.0               0.73   \n",
       "30               1.00                1.0               0.73   \n",
       "31               1.00                1.0               0.73   \n",
       "32               1.00                1.0               0.73   \n",
       "33               1.00                1.0               0.73   \n",
       "34               1.00                1.0               0.73   \n",
       "35               1.00                1.0               0.73   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0                0.97            0.762        0.199138               36  \n",
       "1                0.80            0.838        0.139485                2  \n",
       "2                0.80            0.838        0.139485                2  \n",
       "3                0.80            0.838        0.139485                2  \n",
       "4                0.80            0.838        0.139485                2  \n",
       "5                0.80            0.838        0.139485                2  \n",
       "6                0.80            0.838        0.139485                2  \n",
       "7                0.80            0.838        0.139485                2  \n",
       "8                0.80            0.838        0.139485                2  \n",
       "9                0.80            0.838        0.139485                2  \n",
       "10               0.80            0.838        0.139485                2  \n",
       "11               0.80            0.838        0.139485                2  \n",
       "12               0.80            0.838        0.139485                2  \n",
       "13               0.80            0.838        0.139485                2  \n",
       "14               0.80            0.838        0.139485                2  \n",
       "15               0.80            0.838        0.139485                2  \n",
       "16               0.80            0.838        0.139485                2  \n",
       "17               0.80            0.838        0.139485                2  \n",
       "18               0.97            0.814        0.141789               35  \n",
       "19               0.80            0.892        0.139485                1  \n",
       "20               0.80            0.838        0.139485                2  \n",
       "21               0.80            0.838        0.139485                2  \n",
       "22               0.80            0.838        0.139485                2  \n",
       "23               0.80            0.838        0.139485                2  \n",
       "24               0.80            0.838        0.139485                2  \n",
       "25               0.80            0.838        0.139485                2  \n",
       "26               0.80            0.838        0.139485                2  \n",
       "27               0.80            0.838        0.139485                2  \n",
       "28               0.80            0.838        0.139485                2  \n",
       "29               0.80            0.838        0.139485                2  \n",
       "30               0.80            0.838        0.139485                2  \n",
       "31               0.80            0.838        0.139485                2  \n",
       "32               0.80            0.838        0.139485                2  \n",
       "33               0.80            0.838        0.139485                2  \n",
       "34               0.80            0.838        0.139485                2  \n",
       "35               0.80            0.838        0.139485                2  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "model5 = DecisionTreeClassifier(criterion=\"entropy\",max_depth=5,random_state=42)\n",
    "clf_tree = model5.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature = ['PPI','Temperature(Kelvin)','Mirror','Flip','Backgrounds_absent','Backgrounds_black','Backgrounds_striped','Backgrounds_sorroundin','Backgrounds_white']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "target = ['Outcome']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(279.0, 747.4499999999999, 'X[0] <= 122.3\\nentropy = 0.998\\nsamples = 1000\\nvalue = [528, 472]'),\n",
       " Text(186.0, 611.55, 'entropy = 0.0\\nsamples = 300\\nvalue = [300, 0]'),\n",
       " Text(372.0, 611.55, 'X[0] <= 321.15\\nentropy = 0.91\\nsamples = 700\\nvalue = [228, 472]'),\n",
       " Text(279.0, 475.65, 'X[0] <= 250.77\\nentropy = 0.994\\nsamples = 500\\nvalue = [228, 272]'),\n",
       " Text(186.0, 339.74999999999994, 'X[0] <= 183.395\\nentropy = 0.904\\nsamples = 400\\nvalue = [128, 272]'),\n",
       " Text(93.0, 203.8499999999999, 'entropy = 0.0\\nsamples = 100\\nvalue = [0, 100]'),\n",
       " Text(279.0, 203.8499999999999, 'X[0] <= 244.61\\nentropy = 0.984\\nsamples = 300\\nvalue = [128, 172]'),\n",
       " Text(186.0, 67.94999999999993, 'entropy = 0.943\\nsamples = 200\\nvalue = [128, 72]'),\n",
       " Text(372.0, 67.94999999999993, 'entropy = 0.0\\nsamples = 100\\nvalue = [0, 100]'),\n",
       " Text(372.0, 339.74999999999994, 'entropy = 0.0\\nsamples = 100\\nvalue = [100, 0]'),\n",
       " Text(465.0, 475.65, 'entropy = 0.0\\nsamples = 200\\nvalue = [0, 200]')]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "plt.figure(figsize=(10,15))\n",
    "tree.plot_tree(clf_tree,filled=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "|--- feature_0 <= 122.30\n",
      "|   |--- class: 0\n",
      "|--- feature_0 >  122.30\n",
      "|   |--- feature_0 <= 321.15\n",
      "|   |   |--- feature_0 <= 250.77\n",
      "|   |   |   |--- feature_0 <= 183.39\n",
      "|   |   |   |   |--- class: 1\n",
      "|   |   |   |--- feature_0 >  183.39\n",
      "|   |   |   |   |--- feature_0 <= 244.61\n",
      "|   |   |   |   |   |--- class: 0\n",
      "|   |   |   |   |--- feature_0 >  244.61\n",
      "|   |   |   |   |   |--- class: 1\n",
      "|   |   |--- feature_0 >  250.77\n",
      "|   |   |   |--- class: 0\n",
      "|   |--- feature_0 >  321.15\n",
      "|   |   |--- class: 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(tree.export_text(clf_tree))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}