Lesson19-使用Decision Trees建模with Gini and Entropy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第十九课 使用Decision Tree建模with Gini and Entropy\n",
    "\n",
    "\n",
    "Python零基础学习建模：https://www.ixigua.com/home/77346806707?utm_source=xiguastudio\n",
    "\n",
    "Python源文件及数据下载链接: https://pan.baidu.com/s/1yuNlG6u9_C31fzhzbzqASA 提取码: ebrh\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import tree\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width    species\n",
       "0             5.1          3.5           1.4          0.2     setosa\n",
       "1             4.9          3.0           1.4          0.2     setosa\n",
       "2             4.7          3.2           1.3          0.2     setosa\n",
       "3             4.6          3.1           1.5          0.2     setosa\n",
       "4             5.0          3.6           1.4          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "145           6.7          3.0           5.2          2.3  virginica\n",
       "146           6.3          2.5           5.0          1.9  virginica\n",
       "147           6.5          3.0           5.2          2.0  virginica\n",
       "148           6.2          3.4           5.4          2.3  virginica\n",
       "149           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=sns.load_dataset('iris')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150, 5)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   sepal_length  150 non-null    float64\n",
      " 1   sepal_width   150 non-null    float64\n",
      " 2   petal_length  150 non-null    float64\n",
      " 3   petal_width   150 non-null    float64\n",
      " 4   species       150 non-null    object \n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 6.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['species'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = data.drop(['species'], axis=1)\n",
    "y = data['species']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width\n",
       "0             5.1          3.5           1.4          0.2\n",
       "1             4.9          3.0           1.4          0.2\n",
       "2             4.7          3.2           1.3          0.2\n",
       "3             4.6          3.1           1.5          0.2\n",
       "4             5.0          3.6           1.4          0.2\n",
       "..            ...          ...           ...          ...\n",
       "145           6.7          3.0           5.2          2.3\n",
       "146           6.3          2.5           5.0          1.9\n",
       "147           6.5          3.0           5.2          2.0\n",
       "148           6.2          3.4           5.4          2.3\n",
       "149           5.9          3.0           5.1          1.8\n",
       "\n",
       "[150 rows x 4 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.30, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "cls_gini = DecisionTreeClassifier(criterion='gini', max_depth=3, random_state=0)\n",
    "cls_entropy = DecisionTreeClassifier(criterion='entropy', random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=3, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_gini.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='entropy',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cls_entropy.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['versicolor', 'setosa', 'virginica', 'versicolor', 'versicolor',\n",
       "       'setosa', 'versicolor', 'virginica', 'versicolor', 'versicolor',\n",
       "       'virginica', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor',\n",
       "       'virginica', 'versicolor', 'versicolor', 'virginica', 'setosa',\n",
       "       'virginica', 'setosa', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'versicolor', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
       "       'setosa', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
       "       'versicolor', 'setosa', 'setosa'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_gini = cls_gini.predict(X_test)\n",
    "y_pred_gini"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['versicolor', 'setosa', 'virginica', 'versicolor', 'versicolor',\n",
       "       'setosa', 'versicolor', 'virginica', 'versicolor', 'versicolor',\n",
       "       'versicolor', 'setosa', 'setosa', 'setosa', 'setosa', 'versicolor',\n",
       "       'virginica', 'versicolor', 'versicolor', 'virginica', 'setosa',\n",
       "       'virginica', 'setosa', 'virginica', 'virginica', 'virginica',\n",
       "       'virginica', 'virginica', 'setosa', 'setosa', 'setosa', 'setosa',\n",
       "       'versicolor', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
       "       'setosa', 'setosa', 'setosa', 'virginica', 'versicolor',\n",
       "       'versicolor', 'setosa', 'setosa'], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_entropy = cls_entropy.predict(X_test)\n",
    "y_pred_entropy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with criterion gini index: 1.0000\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "print('Model accuracy score with criterion gini index: {0:0.4f}'.format(accuracy_score(y_test, y_pred_gini)))\n",
    "# y_pred_gini are the predicted class labels in the test-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model accuracy score with criterion entropy index: 0.9778\n"
     ]
    }
   ],
   "source": [
    "#from sklearn.metrics import accuracy_score\n",
    "print('Model accuracy score with criterion entropy index: {0:0.4f}'.format(accuracy_score(y_test, y_pred_entropy)))\n",
    "# y_pred_entropy are the predicted class labels in the test-set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 0.9524\n",
      "Test set score: 1.0000\n"
     ]
    }
   ],
   "source": [
    "#Check for overfitting and underfitting:\n",
    "print('Training set score: {:.4f}'.format(cls_gini.score(X_train, y_train)))\n",
    "print('Test set score: {:.4f}'.format(cls_gini.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training set score: 1.0000\n",
      "Test set score: 0.9778\n"
     ]
    }
   ],
   "source": [
    "print('Training set score: {:.4f}'.format(cls_entropy.score(X_train, y_train)))\n",
    "print('Test set score: {:.4f}'.format(cls_entropy.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(251.10000000000002, 380.52, 'X[3] <= 0.8\\ngini = 0.664\\nsamples = 105\\nvalue = [31, 37, 37]'),\n",
       " Text(167.4, 271.8, 'gini = 0.0\\nsamples = 31\\nvalue = [31, 0, 0]'),\n",
       " Text(334.8, 271.8, 'X[2] <= 4.75\\ngini = 0.5\\nsamples = 74\\nvalue = [0, 37, 37]'),\n",
       " Text(167.4, 163.07999999999998, 'X[3] <= 1.6\\ngini = 0.059\\nsamples = 33\\nvalue = [0, 32, 1]'),\n",
       " Text(83.7, 54.360000000000014, 'gini = 0.0\\nsamples = 32\\nvalue = [0, 32, 0]'),\n",
       " Text(251.10000000000002, 54.360000000000014, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(502.20000000000005, 163.07999999999998, 'X[3] <= 1.75\\ngini = 0.214\\nsamples = 41\\nvalue = [0, 5, 36]'),\n",
       " Text(418.5, 54.360000000000014, 'gini = 0.5\\nsamples = 8\\nvalue = [0, 4, 4]'),\n",
       " Text(585.9, 54.360000000000014, 'gini = 0.059\\nsamples = 33\\nvalue = [0, 1, 32]')]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot decision tree:\n",
    "plt.figure(figsize=(12,8))\n",
    "tree.plot_tree(cls_gini.fit(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(223.20000000000002, 407.7, 'X[3] <= 0.8\\nentropy = 1.58\\nsamples = 105\\nvalue = [31, 37, 37]'),\n",
       " Text(148.8, 353.34000000000003, 'entropy = 0.0\\nsamples = 31\\nvalue = [31, 0, 0]'),\n",
       " Text(297.6, 353.34000000000003, 'X[2] <= 4.75\\nentropy = 1.0\\nsamples = 74\\nvalue = [0, 37, 37]'),\n",
       " Text(148.8, 298.98, 'X[3] <= 1.6\\nentropy = 0.196\\nsamples = 33\\nvalue = [0, 32, 1]'),\n",
       " Text(74.4, 244.62, 'entropy = 0.0\\nsamples = 32\\nvalue = [0, 32, 0]'),\n",
       " Text(223.20000000000002, 244.62, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(446.40000000000003, 298.98, 'X[2] <= 5.15\\nentropy = 0.535\\nsamples = 41\\nvalue = [0, 5, 36]'),\n",
       " Text(372.0, 244.62, 'X[3] <= 1.75\\nentropy = 0.918\\nsamples = 15\\nvalue = [0, 5, 10]'),\n",
       " Text(223.20000000000002, 190.26, 'X[1] <= 2.35\\nentropy = 0.918\\nsamples = 6\\nvalue = [0, 4, 2]'),\n",
       " Text(148.8, 135.89999999999998, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(297.6, 135.89999999999998, 'X[2] <= 5.05\\nentropy = 0.722\\nsamples = 5\\nvalue = [0, 4, 1]'),\n",
       " Text(223.20000000000002, 81.54000000000002, 'entropy = 0.0\\nsamples = 3\\nvalue = [0, 3, 0]'),\n",
       " Text(372.0, 81.54000000000002, 'X[3] <= 1.55\\nentropy = 1.0\\nsamples = 2\\nvalue = [0, 1, 1]'),\n",
       " Text(297.6, 27.180000000000007, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 0, 1]'),\n",
       " Text(446.40000000000003, 27.180000000000007, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(520.8000000000001, 190.26, 'X[1] <= 3.1\\nentropy = 0.503\\nsamples = 9\\nvalue = [0, 1, 8]'),\n",
       " Text(446.40000000000003, 135.89999999999998, 'entropy = 0.0\\nsamples = 8\\nvalue = [0, 0, 8]'),\n",
       " Text(595.2, 135.89999999999998, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1, 0]'),\n",
       " Text(520.8000000000001, 244.62, 'entropy = 0.0\\nsamples = 26\\nvalue = [0, 0, 26]')]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot decision tree:\n",
    "plt.figure(figsize=(12,8))\n",
    "tree.plot_tree(cls_entropy.fit(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}