Hyperparameter Tuning Random Forest using GridSearchCV and RandomizedSearchCV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a6b5e04d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier,GradientBoostingClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7c2f054",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day65-random-forest/heart.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ecd9930b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4409de1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "925135ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bde66754",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "39527cce",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier()\n",
    "gd=GradientBoostingClassifier()\n",
    "svc=SVC()\n",
    "lr=LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8e2773ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf.fit(X_train,y_train)\n",
    "y_pred=rf.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "49a23f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7704918032786885"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gd.fit(X_train,y_train)\n",
    "y_pred=gd.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07f8c421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7049180327868853"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train,y_train)\n",
    "y_pred=svc.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "147601f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8852459016393442"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr.fit(X_train,y_train)\n",
    "y_pred=lr.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4e308e76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8248387096774193"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "np.mean(cross_val_score(rf,X,y,cv=10,scoring=\"accuracy\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d5d6fb32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "C:\\Users\\Abdur rahim nishad\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8316129032258065"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "np.mean(cross_val_score(lr,X,y,cv=10,scoring=\"accuracy\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bea57137",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6604301075268817"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "np.mean(cross_val_score(svc,X,y,cv=10,scoring=\"accuracy\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2039fc93",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9016393442622951"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf=RandomForestClassifier(max_samples=.75,random_state=42)\n",
    "rf.fit(X_train,y_train)\n",
    "y_pred=rf.predict(X_test)\n",
    "accuracy_score(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ccb69a32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8349462365591398"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "np.mean(cross_val_score(RandomForestClassifier(max_samples=.75,random_state=42),X,y,cv=10,scoring=\"accuracy\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b578dfb3",
   "metadata": {},
   "source": [
    "# GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "4dcf67f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators=[20,60,100,120]\n",
    "max_features=[.2,.6,1.0]\n",
    "max_features=[.2,.6,1]\n",
    "max_depth=[2,8,None]\n",
    "max_sample=[.5,.75,.1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2ebc53a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [20, 60, 100, 120], 'max_features': [0.2, 0.6, 1], 'max_depth': [2, 8, None], 'max_samples': [0.5, 0.75, 0.1]}\n"
     ]
    }
   ],
   "source": [
    "param_grid={\"n_estimators\":n_estimators,\n",
    "            \"max_features\":max_features,\n",
    "            \"max_depth\": max_depth,\n",
    "            \"max_samples\":max_sample\n",
    "    \n",
    "}\n",
    "print(param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "c8699d83",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf=RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "91a35f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import GridSearchCV'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rf_grid=GridSearchCV(estimator=rf,\n",
    "                     param_grid=param_grid,\n",
    "                     cv=5,\n",
    "                     verbose=2,\n",
    "                     n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "57feefc0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 108 candidates, totalling 540 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "             param_grid={'max_depth': [2, 8, None],\n",
       "                         'max_features': [0.2, 0.6, 1],\n",
       "                         'max_samples': [0.5, 0.75, 0.1],\n",
       "                         'n_estimators': [20, 60, 100, 120]},\n",
       "             verbose=2)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f3397e2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 8, 'max_features': 1, 'max_samples': 0.75, 'n_estimators': 100}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "99145352",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8510204081632654"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18e9ba5a",
   "metadata": {},
   "source": [
    "# RandomSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "25fe9cbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators=[20,60,100,120]\n",
    "max_features=[.2,.6,1.0]\n",
    "max_features=[.2,.6,1]\n",
    "max_depth=[2,8,None]\n",
    "max_sample=[.5,.75,.1]\n",
    "bootstrap=[True,False]\n",
    "min_samples_split=[2,5]\n",
    "min_samples_leaf=[1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5b039c9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'n_estimators': [20, 60, 100, 120], 'max_features': [0.2, 0.6, 1], 'max_depth': [2, 8, None], 'max_samples': [0.5, 0.75, 0.1], 'min_samples_split': [2, 5], 'min_samples_leaf': [2, 5], 'bootstrap': [True, False]}\n"
     ]
    }
   ],
   "source": [
    "param_grid={\"n_estimators\":n_estimators,\n",
    "            \"max_features\":max_features,\n",
    "            \"max_depth\": max_depth,\n",
    "            \"max_samples\":max_sample,\n",
    "            \"min_samples_split\":min_samples_split,\n",
    "            \"min_samples_leaf\":min_samples_split,\n",
    "            \"bootstrap\":bootstrap\n",
    "    \n",
    "}\n",
    "print(param_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "63ad9db1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rf_grid=RandomizedSearchCV(\n",
    "    estimator=rf,\n",
    "    param_distributions=param_grid,\n",
    "    cv=5,\n",
    "    verbose=0,\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "6e7c6af6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(), n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [2, 8, None],\n",
       "                                        'max_features': [0.2, 0.6, 1],\n",
       "                                        'max_samples': [0.5, 0.75, 0.1],\n",
       "                                        'min_samples_leaf': [2, 5],\n",
       "                                        'min_samples_split': [2, 5],\n",
       "                                        'n_estimators': [20, 60, 100, 120]})"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "b2dc0b4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 100,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 5,\n",
       " 'max_samples': 0.1,\n",
       " 'max_features': 0.6,\n",
       " 'max_depth': 2,\n",
       " 'bootstrap': True}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "92c05b35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8181972789115648"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_grid.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb524b4c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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