feature-importance-in-sklearn.ipynb

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  {
   "cell_type": "code",
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   "id": "ee5fe6b4",
   "metadata": {},
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   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "import keras\n",
    "from keras.layers import Dense\n",
    "from keras.utils import to_categorical\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5740bccc",
   "metadata": {},
   "outputs": [
    {
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      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import pandas as pd'); }\n",
       "    "
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      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    }
   ],
   "source": [
    "df=pd.read_csv(\"https://raw.githubusercontent.com/sbussmann/kaggle-mnist/master/Data/train.csv\")"
   ]
  },
  {
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       "       pixel9  ...  pixel774  pixel775  pixel776  pixel777  pixel778  \\\n",
       "0           0  ...         0         0         0         0         0   \n",
       "1           0  ...         0         0         0         0         0   \n",
       "2           0  ...         0         0         0         0         0   \n",
       "3           0  ...         0         0         0         0         0   \n",
       "4           0  ...         0         0         0         0         0   \n",
       "...       ...  ...       ...       ...       ...       ...       ...   \n",
       "41995       0  ...         0         0         0         0         0   \n",
       "41996       0  ...         0         0         0         0         0   \n",
       "41997       0  ...         0         0         0         0         0   \n",
       "41998       0  ...         0         0         0         0         0   \n",
       "41999       0  ...         0         0         0         0         0   \n",
       "\n",
       "       pixel779  pixel780  pixel781  pixel782  pixel783  \n",
       "0             0         0         0         0         0  \n",
       "1             0         0         0         0         0  \n",
       "2             0         0         0         0         0  \n",
       "3             0         0         0         0         0  \n",
       "4             0         0         0         0         0  \n",
       "...         ...       ...       ...       ...       ...  \n",
       "41995         0         0         0         0         0  \n",
       "41996         0         0         0         0         0  \n",
       "41997         0         0         0         0         0  \n",
       "41998         0         0         0         0         0  \n",
       "41999         0         0         0         0         0  \n",
       "\n",
       "[42000 rows x 784 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7bde9227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        1\n",
       "1        0\n",
       "2        1\n",
       "3        4\n",
       "4        0\n",
       "        ..\n",
       "41995    0\n",
       "41996    1\n",
       "41997    7\n",
       "41998    6\n",
       "41999    9\n",
       "Name: label, Length: 42000, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "843d4a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf=RandomForestClassifier()\n",
    "rf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "54d29e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       4.05671653e-04, 2.31805376e-04, 1.65742961e-04, 6.47127810e-05,\n",
       "       1.81184782e-05, 4.99489324e-06, 1.02740692e-06, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 2.99949709e-06, 2.01352699e-05,\n",
       "       7.76959521e-05, 1.22874779e-04, 3.09286541e-04, 7.22603256e-04,\n",
       "       1.28230794e-03, 2.39625117e-03, 3.32216423e-03, 4.08866173e-03,\n",
       "       4.24480695e-03, 4.73400520e-03, 4.93114250e-03, 2.66253598e-03,\n",
       "       1.96445070e-03, 1.24427703e-03, 5.66827317e-04, 3.54315737e-04,\n",
       "       2.49040413e-04, 1.36678125e-04, 8.90761256e-05, 3.24429893e-05,\n",
       "       1.21265652e-05, 3.57400177e-06, 0.00000000e+00, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 3.08303676e-06, 1.31363837e-05,\n",
       "       4.64360958e-05, 7.53360535e-05, 3.13067619e-04, 3.56705948e-04,\n",
       "       5.36413949e-04, 9.68788214e-04, 8.21637721e-04, 1.14659013e-03,\n",
       "       1.55778343e-03, 1.09180016e-03, 8.27036090e-04, 6.71247001e-04,\n",
       "       5.71175781e-04, 4.03970538e-04, 3.91848649e-04, 2.03490568e-04,\n",
       "       1.32466489e-04, 7.74679830e-05, 4.17091978e-05, 2.07462619e-05,\n",
       "       4.55072965e-06, 1.55621519e-06, 1.48161853e-06, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 2.48873764e-06,\n",
       "       7.47287514e-06, 3.96868511e-05, 1.10657937e-04, 2.37982213e-04,\n",
       "       5.93623730e-04, 6.10806681e-04, 9.06063020e-04, 6.79017262e-04,\n",
       "       9.72495694e-04, 1.29263244e-03, 6.77836274e-04, 3.58745114e-04,\n",
       "       3.94143366e-04, 4.54556071e-04, 2.56558988e-04, 1.68630166e-04,\n",
       "       1.09843898e-04, 4.80518255e-05, 1.84357382e-05, 5.65843089e-06,\n",
       "       2.00956770e-06, 1.00355956e-06, 1.27750824e-06, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       5.77454296e-06, 1.29762983e-05, 1.72276645e-05, 1.98313495e-05,\n",
       "       4.77474644e-05, 1.09660008e-04, 1.57389914e-04, 1.37463523e-04,\n",
       "       1.58530054e-04, 1.95732131e-04, 4.37171829e-04, 1.33698543e-04,\n",
       "       1.65846530e-04, 1.24766412e-04, 8.31864482e-05, 6.64993802e-05,\n",
       "       1.14428040e-05, 7.49063024e-06, 4.95703148e-06, 2.29327244e-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, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "       1.04131164e-06, 1.96328048e-06, 3.84771133e-06, 1.93898974e-06,\n",
       "       4.80869516e-06, 2.99221388e-06, 5.93100020e-06, 4.88857183e-06,\n",
       "       5.77505334e-06, 1.67600386e-06, 5.08084805e-07, 1.65661906e-06,\n",
       "       6.32930107e-06, 2.41693383e-06, 4.89373846e-07, 0.00000000e+00,\n",
       "       0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "75686d2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(784,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1c2a41b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 18,
     "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": 19,
   "id": "eac563a4",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "scatter() missing 1 required positional argument: 'y'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\ABDURR~1\\AppData\\Local\\Temp/ipykernel_6380/1096919503.py\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfeature_importances_\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: scatter() missing 1 required positional argument: 'y'"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "81d20f2d",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}