RANDOMFOREST.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_digits\n",
    "digits = load_digits()\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.gray()\n",
    "for i in range (4):\n",
    " plt.matshow(digits.images[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "      <th>21</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "      <th>32</th>\n",
       "      <th>33</th>\n",
       "      <th>34</th>\n",
       "      <th>35</th>\n",
       "      <th>36</th>\n",
       "      <th>37</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
       "      <th>49</th>\n",
       "      <th>50</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>13.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1    2     3     4     5    6   ...   57   58    59    60    61   62   63\n",
       "0  0.0  0.0  5.0  13.0   9.0   1.0  0.0  ...  0.0  6.0  13.0  10.0   0.0  0.0  0.0\n",
       "1  0.0  0.0  0.0  12.0  13.0   5.0  0.0  ...  0.0  0.0  11.0  16.0  10.0  0.0  0.0\n",
       "2  0.0  0.0  0.0   4.0  15.0  12.0  0.0  ...  0.0  0.0   3.0  11.0  16.0  9.0  0.0\n",
       "3  0.0  0.0  7.0  15.0  13.0   1.0  0.0  ...  0.0  7.0  13.0  13.0   9.0  0.0  0.0\n",
       "4  0.0  0.0  0.0   1.0  11.0   0.0  0.0  ...  0.0  0.0   2.0  16.0   4.0  0.0  0.0\n",
       "\n",
       "[5 rows x 64 columns]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(digits.data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "      <th>61</th>\n",
       "      <th>62</th>\n",
       "      <th>63</th>\n",
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       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2     3     4     5  ...    59    60    61   62   63  target\n",
       "0  0.0  0.0  5.0  13.0   9.0   1.0  ...  13.0  10.0   0.0  0.0  0.0       0\n",
       "1  0.0  0.0  0.0  12.0  13.0   5.0  ...  11.0  16.0  10.0  0.0  0.0       1\n",
       "2  0.0  0.0  0.0   4.0  15.0  12.0  ...   3.0  11.0  16.0  9.0  0.0       2\n",
       "3  0.0  0.0  7.0  15.0  13.0   1.0  ...  13.0  13.0   9.0  0.0  0.0       3\n",
       "4  0.0  0.0  0.0   1.0  11.0   0.0  ...   2.0  16.0   4.0  0.0  0.0       4\n",
       "\n",
       "[5 rows x 65 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['target'] = digits.target\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(df.drop(['target'],axis ='columns'),digits.target,test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-10 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-10 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-10 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-10 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-10 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-10 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-10 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-10 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-10 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-10 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-10 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-10 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-10 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-10 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-10\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=50)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" checked><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(n_estimators=50)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(n_estimators=50)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier \n",
    "model = RandomForestClassifier(n_estimators=50)\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9777777777777777"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[35,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0, 29,  0,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0, 31,  0,  0,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0, 34,  0,  0,  0,  0,  1,  0],\n",
       "       [ 0,  0,  0,  0, 32,  0,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  1, 36,  0,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0, 44,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0, 35,  1,  0],\n",
       "       [ 0,  1,  0,  1,  0,  1,  0,  1, 39,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  1, 37]], dtype=int64)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test,y)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(95.72222222222221, 0.5, 'truth')"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.heatmap(cm,annot=True)\n",
    "plt.xlabel('predicted')\n",
    "plt.ylabel('truth')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}