day65-random-forest/how-feature-importance-is-calculated.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "aa83b2fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cf98028b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "98e617f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_classification(n_samples=5, n_classes=2,\n",
    "                               n_features=2, n_informative=2, n_redundant=0,\n",
    "                               random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c2f01ef2",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "62ce1795",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "01e8059b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(133.92000000000002, 181.2, 'X[1] <= -0.894\\ngini = 0.48\\nsamples = 5\\nvalue = [3, 2]'),\n",
       " Text(66.96000000000001, 108.72, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
       " Text(200.88000000000002, 108.72, 'X[0] <= 1.01\\ngini = 0.375\\nsamples = 4\\nvalue = [3, 1]'),\n",
       " Text(133.92000000000002, 36.23999999999998, 'gini = 0.0\\nsamples = 3\\nvalue = [3, 0]'),\n",
       " Text(267.84000000000003, 36.23999999999998, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]')]"
      ]
     },
     "execution_count": 33,
     "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.tree import plot_tree\n",
    "plot_tree(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "55330670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.625, 0.375])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8ffceaa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=2)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "rf = RandomForestClassifier(n_estimators=2)\n",
    "rf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2c7e9f85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.77777778, 0.22222222])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f44ab949",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 0.]\n",
      "[0.55555556 0.44444444]\n",
      "0.7775000000000001\n"
     ]
    }
   ],
   "source": [
    "print(rf.estimators_[0].feature_importances_)\n",
    "print(rf.estimators_[1].feature_importances_)\n",
    "\n",
    "print((1 + 0.555)/2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "5f1fe755",
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = make_classification(n_samples=15, n_classes=2,\n",
    "                               n_features=2, n_informative=2, n_redundant=0,\n",
    "                               random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "ced9e8f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "a701bd11",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "11f6b663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "8a311643",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(111.60000000000001, 190.26, 'X[1] <= -0.383\\ngini = 0.498\\nsamples = 15\\nvalue = [8, 7]'),\n",
       " Text(55.800000000000004, 135.9, 'gini = 0.0\\nsamples = 6\\nvalue = [6, 0]'),\n",
       " Text(167.4, 135.9, 'X[0] <= 0.878\\ngini = 0.346\\nsamples = 9\\nvalue = [2, 7]'),\n",
       " Text(111.60000000000001, 81.53999999999999, 'gini = 0.0\\nsamples = 6\\nvalue = [0, 6]'),\n",
       " Text(223.20000000000002, 81.53999999999999, 'X[0] <= 2.444\\ngini = 0.444\\nsamples = 3\\nvalue = [2, 1]'),\n",
       " Text(167.4, 27.180000000000007, 'gini = 0.0\\nsamples = 2\\nvalue = [2, 0]'),\n",
       " Text(279.0, 27.180000000000007, 'gini = 0.0\\nsamples = 1\\nvalue = [0, 1]')]"
      ]
     },
     "execution_count": 147,
     "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.tree import plot_tree\n",
    "plot_tree(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "554a842c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.41666667, 0.58333333])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "28e5ad5f",
   "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
}