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21075A6603-random_Forest.ipynb

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+{
+ "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))"
+   ]
+  }
+ ],
+ "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": "3.9.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}