{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "650e8268",
   "metadata": {},
   "source": [
    "# Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c638f04",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris=load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e711262a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y=iris.data,iris.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "638bbf11",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=150)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1518fbc2",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.5, 2.4, 3.7, 1. ],\n",
       "       [5.7, 2.8, 4.1, 1.3],\n",
       "       [6. , 2.2, 5. , 1.5],\n",
       "       [4.8, 3. , 1.4, 0.1],\n",
       "       [5.4, 3.9, 1.3, 0.4],\n",
       "       [6.4, 3.2, 4.5, 1.5],\n",
       "       [5.1, 3.8, 1.6, 0.2],\n",
       "       [5.5, 2.5, 4. , 1.3],\n",
       "       [6.3, 3.4, 5.6, 2.4],\n",
       "       [5.8, 2.8, 5.1, 2.4],\n",
       "       [4.5, 2.3, 1.3, 0.3],\n",
       "       [5.5, 2.6, 4.4, 1.2],\n",
       "       [7.1, 3. , 5.9, 2.1],\n",
       "       [7.2, 3.6, 6.1, 2.5],\n",
       "       [4.9, 3.6, 1.4, 0.1],\n",
       "       [4.6, 3.4, 1.4, 0.3],\n",
       "       [5. , 3. , 1.6, 0.2],\n",
       "       [5.1, 3.7, 1.5, 0.4],\n",
       "       [5.8, 2.6, 4. , 1.2],\n",
       "       [4.9, 3.1, 1.5, 0.1],\n",
       "       [5.1, 3.3, 1.7, 0.5],\n",
       "       [5. , 3.2, 1.2, 0.2],\n",
       "       [6.5, 2.8, 4.6, 1.5],\n",
       "       [7.9, 3.8, 6.4, 2. ],\n",
       "       [6.1, 3. , 4.9, 1.8],\n",
       "       [5.4, 3. , 4.5, 1.5],\n",
       "       [6.4, 2.7, 5.3, 1.9],\n",
       "       [5.7, 2.9, 4.2, 1.3],\n",
       "       [7.7, 3.8, 6.7, 2.2],\n",
       "       [6.5, 3.2, 5.1, 2. ],\n",
       "       [5.8, 2.7, 3.9, 1.2],\n",
       "       [4.6, 3.6, 1. , 0.2],\n",
       "       [6.9, 3.1, 5.4, 2.1],\n",
       "       [6.7, 3.3, 5.7, 2.1],\n",
       "       [6.3, 2.8, 5.1, 1.5],\n",
       "       [5.5, 4.2, 1.4, 0.2],\n",
       "       [4.4, 3.2, 1.3, 0.2],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [5.4, 3.9, 1.7, 0.4],\n",
       "       [5.5, 3.5, 1.3, 0.2],\n",
       "       [5. , 3.5, 1.6, 0.6],\n",
       "       [6.9, 3.1, 4.9, 1.5],\n",
       "       [6.5, 3. , 5.8, 2.2],\n",
       "       [6.7, 3.3, 5.7, 2.5],\n",
       "       [6.1, 2.6, 5.6, 1.4],\n",
       "       [5.4, 3.7, 1.5, 0.2],\n",
       "       [6. , 3.4, 4.5, 1.6],\n",
       "       [5.9, 3.2, 4.8, 1.8],\n",
       "       [4.6, 3.1, 1.5, 0.2],\n",
       "       [6.8, 2.8, 4.8, 1.4],\n",
       "       [4.9, 2.4, 3.3, 1. ],\n",
       "       [6.2, 2.8, 4.8, 1.8],\n",
       "       [5.1, 3.5, 1.4, 0.2],\n",
       "       [6. , 2.9, 4.5, 1.5],\n",
       "       [5.6, 3. , 4.1, 1.3],\n",
       "       [6. , 2.7, 5.1, 1.6],\n",
       "       [7. , 3.2, 4.7, 1.4],\n",
       "       [6.2, 2.2, 4.5, 1.5],\n",
       "       [5.7, 3. , 4.2, 1.2],\n",
       "       [6.4, 2.8, 5.6, 2.2],\n",
       "       [5.7, 2.5, 5. , 2. ],\n",
       "       [4.3, 3. , 1.1, 0.1],\n",
       "       [6.3, 2.5, 4.9, 1.5],\n",
       "       [5.1, 3.5, 1.4, 0.3],\n",
       "       [6.4, 2.9, 4.3, 1.3],\n",
       "       [7.2, 3. , 5.8, 1.6],\n",
       "       [6.4, 3.1, 5.5, 1.8],\n",
       "       [4.9, 2.5, 4.5, 1.7],\n",
       "       [5.6, 2.9, 3.6, 1.3],\n",
       "       [5.7, 3.8, 1.7, 0.3],\n",
       "       [5.1, 3.8, 1.9, 0.4],\n",
       "       [4.4, 3. , 1.3, 0.2],\n",
       "       [5.1, 3.4, 1.5, 0.2],\n",
       "       [5.6, 2.8, 4.9, 2. ],\n",
       "       [5.3, 3.7, 1.5, 0.2],\n",
       "       [4.8, 3.1, 1.6, 0.2],\n",
       "       [6.3, 3.3, 4.7, 1.6],\n",
       "       [5.2, 3.5, 1.5, 0.2],\n",
       "       [6.7, 3.1, 5.6, 2.4],\n",
       "       [6.1, 2.9, 4.7, 1.4],\n",
       "       [6.9, 3.1, 5.1, 2.3],\n",
       "       [5.1, 3.8, 1.5, 0.3],\n",
       "       [5.8, 2.7, 5.1, 1.9],\n",
       "       [7.6, 3. , 6.6, 2.1],\n",
       "       [4.7, 3.2, 1.3, 0.2],\n",
       "       [5.5, 2.4, 3.8, 1.1],\n",
       "       [6.1, 2.8, 4. , 1.3],\n",
       "       [5.7, 2.8, 4.5, 1.3],\n",
       "       [6.8, 3.2, 5.9, 2.3],\n",
       "       [5.9, 3. , 4.2, 1.5],\n",
       "       [6.7, 3.1, 4.4, 1.4],\n",
       "       [4.6, 3.2, 1.4, 0.2],\n",
       "       [5. , 3.3, 1.4, 0.2],\n",
       "       [5. , 3.4, 1.5, 0.2],\n",
       "       [6.5, 3. , 5.2, 2. ],\n",
       "       [5.2, 2.7, 3.9, 1.4],\n",
       "       [6.1, 3. , 4.6, 1.4],\n",
       "       [5. , 3.6, 1.4, 0.2],\n",
       "       [6.3, 3.3, 6. , 2.5],\n",
       "       [6.7, 2.5, 5.8, 1.8],\n",
       "       [7.4, 2.8, 6.1, 1.9],\n",
       "       [6.7, 3.1, 4.7, 1.5],\n",
       "       [5. , 2.3, 3.3, 1. ],\n",
       "       [6.6, 2.9, 4.6, 1.3],\n",
       "       [5. , 2. , 3.5, 1. ],\n",
       "       [7.3, 2.9, 6.3, 1.8],\n",
       "       [6.2, 3.4, 5.4, 2.3],\n",
       "       [4.9, 3.1, 1.5, 0.2],\n",
       "       [5.8, 4. , 1.2, 0.2],\n",
       "       [5.6, 3. , 4.5, 1.5],\n",
       "       [5.5, 2.3, 4. , 1.3],\n",
       "       [5.1, 2.5, 3. , 1.1],\n",
       "       [5.6, 2.7, 4.2, 1.3],\n",
       "       [6. , 3. , 4.8, 1.8],\n",
       "       [5.7, 2.6, 3.5, 1. ],\n",
       "       [6.3, 2.3, 4.4, 1.3],\n",
       "       [6.3, 2.9, 5.6, 1.8],\n",
       "       [5.8, 2.7, 4.1, 1. ],\n",
       "       [4.8, 3.4, 1.9, 0.2],\n",
       "       [4.4, 2.9, 1.4, 0.2]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "ce8f4d0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 1, 0, 2, 1, 0, 0, 0, 0, 2, 2,\n",
       "       0, 2, 1, 2, 0, 1, 1, 1, 0, 2, 2, 2, 1, 2, 2, 1, 1, 1, 1, 0, 1, 1,\n",
       "       1, 1, 1, 2, 1, 0, 2, 1, 0, 0, 0, 0, 0, 2, 2, 0, 2, 1, 0, 0, 1, 1,\n",
       "       1, 2, 2, 0, 0, 0, 2, 1, 2, 2, 2, 2, 1, 2, 0, 2, 2, 1, 0, 1, 1, 0,\n",
       "       0, 2, 1, 2, 1, 2, 0, 1, 1, 2, 2, 0, 0, 2, 0, 0, 2, 1, 1, 2, 2, 0,\n",
       "       0, 0, 2, 2, 1, 2, 2, 2, 1, 0])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "289a4d3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Random Forest classification\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bc2b28f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=30)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "classifier = RandomForestClassifier(n_estimators=30)\n",
    "classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "40ba2646",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.66666666666667\n",
      "Confusion Matrix:  [[ 9  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  0  9]]\n",
      "Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         9\n",
      "           1       1.00      0.92      0.96        12\n",
      "           2       0.90      1.00      0.95         9\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.97      0.97      0.97        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "y_pred=classifier.predict(x_test)\n",
    "accuracy=accuracy_score(y_test,y_pred)\n",
    "print(\"Accuracy:\",(accuracy)*100)\n",
    "print(\"Confusion Matrix: \",confusion_matrix(y_test,y_pred))\n",
    "print(\"Report :\",classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "57dad48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(n_estimators=20)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifier = RandomForestClassifier(n_estimators=20)\n",
    "classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c13e041",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 96.66666666666667\n",
      "Confusion Matrix:  [[ 9  0  0]\n",
      " [ 0 11  1]\n",
      " [ 0  0  9]]\n",
      "Report :               precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00         9\n",
      "           1       1.00      0.92      0.96        12\n",
      "           2       0.90      1.00      0.95         9\n",
      "\n",
      "    accuracy                           0.97        30\n",
      "   macro avg       0.97      0.97      0.97        30\n",
      "weighted avg       0.97      0.97      0.97        30\n",
      "\n"
     ]
    }
   ],
   "source": [
    "y_pred=classifier.predict(x_test)\n",
    "accuracy=accuracy_score(y_test,y_pred)\n",
    "print(\"Accuracy:\",(accuracy)*100)\n",
    "print(\"Confusion Matrix: \",confusion_matrix(y_test,y_pred))\n",
    "print(\"Report :\",classification_report(y_test,y_pred))"
   ]
  }
 ],
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  "language_info": {
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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