day65-random-forest/feature-importance-in-sklearn.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8d0d33c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b0aa3f74",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "493ce773",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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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>label</th>\n",
       "      <th>pixel0</th>\n",
       "      <th>pixel1</th>\n",
       "      <th>pixel2</th>\n",
       "      <th>pixel3</th>\n",
       "      <th>pixel4</th>\n",
       "      <th>pixel5</th>\n",
       "      <th>pixel6</th>\n",
       "      <th>pixel7</th>\n",
       "      <th>pixel8</th>\n",
       "      <th>...</th>\n",
       "      <th>pixel774</th>\n",
       "      <th>pixel775</th>\n",
       "      <th>pixel776</th>\n",
       "      <th>pixel777</th>\n",
       "      <th>pixel778</th>\n",
       "      <th>pixel779</th>\n",
       "      <th>pixel780</th>\n",
       "      <th>pixel781</th>\n",
       "      <th>pixel782</th>\n",
       "      <th>pixel783</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "</table>\n",
       "<p>5 rows × 785 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   label  pixel0  pixel1  pixel2  pixel3  pixel4  pixel5  pixel6  pixel7  \\\n",
       "0      1       0       0       0       0       0       0       0       0   \n",
       "1      0       0       0       0       0       0       0       0       0   \n",
       "2      1       0       0       0       0       0       0       0       0   \n",
       "3      4       0       0       0       0       0       0       0       0   \n",
       "4      0       0       0       0       0       0       0       0       0   \n",
       "\n",
       "   pixel8  ...  pixel774  pixel775  pixel776  pixel777  pixel778  pixel779  \\\n",
       "0       0  ...         0         0         0         0         0         0   \n",
       "1       0  ...         0         0         0         0         0         0   \n",
       "2       0  ...         0         0         0         0         0         0   \n",
       "3       0  ...         0         0         0         0         0         0   \n",
       "4       0  ...         0         0         0         0         0         0   \n",
       "\n",
       "   pixel780  pixel781  pixel782  pixel783  \n",
       "0         0         0         0         0  \n",
       "1         0         0         0         0  \n",
       "2         0         0         0         0  \n",
       "3         0         0         0         0  \n",
       "4         0         0         0         0  \n",
       "\n",
       "[5 rows x 785 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4e99c4a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,1:]\n",
    "y = df.iloc[:,0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d5b9af3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 17,
     "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(X.iloc[5].values.reshape(28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fa080863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier()\n",
    "\n",
    "rf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e27797e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       7.93634033e-06, 6.09067869e-06, 9.52705686e-06, 3.45841059e-05,\n",
       "       7.59796956e-05, 7.79661963e-05, 1.13892490e-04, 2.70827276e-04,\n",
       "       1.15536496e-04, 3.34548632e-04, 1.70926183e-04, 1.36570029e-04,\n",
       "       1.04306867e-04, 1.39214892e-04, 7.60117663e-05, 3.62471587e-05,\n",
       "       1.67010501e-05, 1.44607191e-05, 1.63431477e-06, 2.56015342e-06,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 8.94737910e-07, 5.16210616e-07, 5.23797792e-07,\n",
       "       0.00000000e+00, 1.51367122e-06, 3.06963479e-06, 2.04311837e-06,\n",
       "       4.88525117e-07, 2.77654626e-06, 5.71768303e-06, 7.33702495e-06,\n",
       "       6.12617982e-06, 5.94211407e-06, 4.86205097e-06, 3.51396447e-06,\n",
       "       5.10355451e-07, 2.10713701e-06, 9.38360597e-07, 1.04976823e-06,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "83fe2092",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "25abe08d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 9,
     "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(rf.feature_importances_.reshape(28,28))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5236a1a6",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}