Decision-Tree-Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f676b568",
   "metadata": {},
   "source": [
    "# Classification --> Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "343da62f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()   #show current working directory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6389926b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments\\\\data_set'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir(\"F:\\\\Machine-learning\\\\Assignments\\\\data_set\")  #change current working directory\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "105ffc27",
   "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>Days</th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temprature</th>\n",
       "      <th>Routine</th>\n",
       "      <th>Wear Jacket?</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Warm</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Warm</td>\n",
       "      <td>Outdoor</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Warm</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Warm</td>\n",
       "      <td>Indoor</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Days Outlook Temprature  Routine Wear Jacket?\n",
       "0     1   Sunny       Cold  Indoor            No\n",
       "1     2  Cloudy       Cold  Indoor           Yes\n",
       "2     3  Cloudy       Warm  Outdoor           No\n",
       "3     4   Sunny       Cold  Outdoor          Yes\n",
       "4     5  Cloudy       Cold  Outdoor          Yes\n",
       "5     6   Sunny       Warm  Outdoor           No\n",
       "6     7  Cloudy       Warm  Indoor            No\n",
       "7     8   Sunny       Warm  Indoor            No"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"tree.csv\")  #import DataFrame \n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8d7460af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8 entries, 0 to 7\n",
      "Data columns (total 5 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   Days          8 non-null      int64 \n",
      " 1   Outlook       8 non-null      object\n",
      " 2   Temprature    8 non-null      object\n",
      " 3   Routine       8 non-null      object\n",
      " 4   Wear Jacket?  8 non-null      object\n",
      "dtypes: int64(1), object(4)\n",
      "memory usage: 448.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a18f5fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     No\n",
       "1    Yes\n",
       "2     No\n",
       "3    Yes\n",
       "4    Yes\n",
       "Name: Wear Jacket?, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df[\"Wear Jacket?\"]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "318e49f0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Days</th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temprature</th>\n",
       "      <th>Routine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Indoor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Indoor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Warm</td>\n",
       "      <td>Outdoor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Sunny</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Outdoor</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Cloudy</td>\n",
       "      <td>Cold</td>\n",
       "      <td>Outdoor</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   Days Outlook Temprature  Routine\n",
       "0     1   Sunny       Cold  Indoor \n",
       "1     2  Cloudy       Cold  Indoor \n",
       "2     3  Cloudy       Warm  Outdoor\n",
       "3     4   Sunny       Cold  Outdoor\n",
       "4     5  Cloudy       Cold  Outdoor"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = df.drop(\"Wear Jacket?\", axis=1)\n",
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94df7fb2",
   "metadata": {},
   "source": [
    "# Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "92dab818",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label = LabelEncoder()\n",
    "x['Outlook'] = label.fit_transform(df[\"Outlook\"])\n",
    "x['Temprature'] = label.fit_transform(df[\"Temprature\"])\n",
    "x['Routine'] = label.fit_transform(df[\"Routine\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b15a91f5",
   "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>Days</th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temprature</th>\n",
       "      <th>Routine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>5</td>\n",
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       "      <td>1</td>\n",
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      "text/plain": [
       "   Days  Outlook  Temprature  Routine\n",
       "0     1        1           0        0\n",
       "1     2        0           0        0\n",
       "2     3        0           1        1\n",
       "3     4        1           0        1\n",
       "4     5        0           0        1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
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   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "63258e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.drop('Days', axis=1)  # here days column unnecessary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "46986925",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Outlook</th>\n",
       "      <th>Temprature</th>\n",
       "      <th>Routine</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <th>2</th>\n",
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       "      <td>1</td>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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      "text/plain": [
       "   Outlook  Temprature  Routine\n",
       "0        1           0        0\n",
       "1        0           0        0\n",
       "2        0           1        1\n",
       "3        1           0        1\n",
       "4        0           0        1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e1fb0b7c",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b13b103c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb9c5853",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(x, y)  # train with full data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5afd1a4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['No'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[1, 0, 0]])  # sunny-> 1, cold -> 0, indoor -> 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a7d26d91",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['No'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[1, 1, 0]])  # sunny-> 1, warm -> 1, indoor -> 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7f23b22d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\base.py:450: UserWarning: X does not have valid feature names, but DecisionTreeClassifier was fitted with feature names\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['Yes'], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict([[1, 0, 1]])  # sunny-> 1, cold -> 0, outdoor -> 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f20d537b",
   "metadata": {},
   "source": [
    "# Tree structure show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bea7957c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.6666666666666666, 0.875, 'X[1] <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
       " Text(0.5, 0.625, 'X[2] <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
       " Text(0.3333333333333333, 0.375, 'X[0] <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.16666666666666666, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "tree.plot_tree(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9c23d3c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.6666666666666666, 0.875, 'X[1] <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
       " Text(0.5, 0.625, 'X[2] <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
       " Text(0.3333333333333333, 0.375, 'X[0] <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.16666666666666666, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(clf,filled=True,rounded=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "08019772",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Outlook', 'Temprature', 'Routine'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b5c11b4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.6666666666666666, 0.875, 'Temprature <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
       " Text(0.5, 0.625, 'Routine <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
       " Text(0.3333333333333333, 0.375, 'Outlook <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.16666666666666666, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.plot_tree(clf,filled=True,rounded=True, feature_names=x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "e73e1edc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.6666666666666666, 0.875, 'Temprature <= 0.5\\ngini = 0.469\\nsamples = 8\\nvalue = [5, 3]'),\n",
       " Text(0.5, 0.625, 'Routine <= 0.5\\ngini = 0.375\\nsamples = 4\\nvalue = [1, 3]'),\n",
       " Text(0.3333333333333333, 0.375, 'Outlook <= 0.5\\ngini = 0.5\\nsamples = 2\\nvalue = [1, 1]'),\n",
       " Text(0.16666666666666666, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(0.5, 0.125, 'gini = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
       " Text(0.6666666666666666, 0.375, 'gini = 0.0\\nsamples = 2\\nvalue = [0, 2]'),\n",
       " Text(0.8333333333333334, 0.625, 'gini = 0.0\\nsamples = 4\\nvalue = [4, 0]')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# final best outlooking\n",
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(10,10))\n",
    "tree.plot_tree(clf, filled=True, rounded=True, feature_names=x.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d8245ab",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}