11_random_forest.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "from sklearn.datasets import load_iris\n",
    "\n",
    "iris = load_iris()"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "print(dir(iris))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "['DESCR', 'data', 'feature_names', 'filename', 'frame', 'target', 'target_names']\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "import pandas as pd\n",
    "\n",
    "iris_df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "iris_df[\"target\"] = iris.target"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = iris_df.drop([\"target\"], axis=\"columns\")\n",
    "y = iris_df[\"target\"]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "model = RandomForestClassifier(criterion=\"entropy\")\n",
    "model.fit(X_train, y_train)\n",
    "print(\"Score\", model.score(X_test, y_test))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Score 0.9\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_pred)\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sn\n",
    "plt.figure()\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Truth\")\n",
    "plt.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
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   "metadata": {}
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