hyperparameter(flow).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8cd017eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8260a33d",
   "metadata": {},
   "source": [
    "#(get data ready)\n",
    "heart_disease_data = pd.read_csv(\"../Data/heart-disease.csv\")\n",
    "heart_disease_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "728b33f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: target, dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#(split into X , Y)\n",
    "# X = data \n",
    "# Y = result\n",
    "X = heart_disease_data.drop(\"target\",axis=1)\n",
    "Y = heart_disease_data[\"target\"]\n",
    "Y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7541fa8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# (now split the data into train and test)\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train , X_test , Y_train , Y_test = train_test_split(X,Y,train_size=0.8)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc9b3dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8688524590163934"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (choosing the model)\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "clf = RandomForestClassifier()\n",
    "clf.fit(X_train , Y_train)\n",
    "clf.score(X_test,Y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc95cf3",
   "metadata": {},
   "source": [
    "## Improving a model\n",
    "\n",
    "From a data perspective\n",
    "* could we collect more data? (generally , the more data , the better)\n",
    "* could we improve our data?\n",
    "\n",
    "From a model perspective\n",
    "* Is there a better model we could use?\n",
    "* could we imporove the current model? (yes by hyperparameters)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "780d263d",
   "metadata": {},
   "source": [
    "now it's time for hyperparameter(improve through experimentation) .\n",
    "consider hyperparameter as tunning for better result\n",
    "\n",
    "### their are three ways of doing hyperparameter in machine learning\n",
    "\n",
    "* by hand\n",
    "* randomly with RandomSearchCV(prefered)\n",
    "* excaustly with GridSearchCV\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d1ffded4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': True,\n",
       " 'ccp_alpha': 0.0,\n",
       " 'class_weight': None,\n",
       " 'criterion': 'gini',\n",
       " 'max_depth': None,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_leaf_nodes': None,\n",
       " 'max_samples': None,\n",
       " 'min_impurity_decrease': 0.0,\n",
       " 'min_samples_leaf': 1,\n",
       " 'min_samples_split': 2,\n",
       " 'min_weight_fraction_leaf': 0.0,\n",
       " 'n_estimators': 100,\n",
       " 'n_jobs': None,\n",
       " 'oob_score': False,\n",
       " 'random_state': None,\n",
       " 'verbose': 0,\n",
       " 'warm_start': False}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "clf = RandomForestClassifier()\n",
    "\n",
    "# these are the hyperparams\n",
    "clf.get_params()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb14317b",
   "metadata": {},
   "source": [
    "we're going to try and adjust\n",
    "* max_depth\n",
    "* max_features\n",
    "* min_samples_leaf\n",
    "* min_samples_split\n",
    "* n_estimators"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e8e994a",
   "metadata": {},
   "source": [
    "### Hyperparameter tuning  with RandomSearchCV(prefered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "51112b36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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",
       "    </tr>\n",
       "    <tr>\n",
       "      <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": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "heart_disease_data = pd.read_csv(\"../Data/heart-disease.csv\")\n",
    "heart_disease_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "73929515",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 10 candidates, totalling 50 fits\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10; total time=   4.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10; total time=   0.1s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=4, n_estimators=10; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=500; total time=   0.7s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=500; total time=   0.7s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=500; total time=   0.8s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=500; total time=   0.7s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=2, min_samples_split=4, n_estimators=500; total time=   1.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1200; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1200; total time=   2.5s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1200; total time=   2.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1200; total time=   2.7s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=1200; total time=   2.4s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   1.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   1.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   0.9s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   1.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=500; total time=   1.0s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=1, min_samples_split=4, n_estimators=200; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   2.1s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   2.0s\n",
      "[CV] END max_depth=30, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=1000; total time=   2.2s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   0.0s\n",
      "[CV] END max_depth=5, max_features=auto, min_samples_leaf=1, min_samples_split=6, n_estimators=1200; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=30, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   0.0s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.2s\n",
      "[CV] END max_depth=20, max_features=sqrt, min_samples_leaf=4, min_samples_split=6, n_estimators=100; total time=   0.2s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Riya\\Desktop\\machine-learning\\env\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:425: FitFailedWarning: \n",
      "15 fits failed out of a total of 50.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "15 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\Riya\\Desktop\\machine-learning\\env\\lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 732, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"C:\\Users\\Riya\\Desktop\\machine-learning\\env\\lib\\site-packages\\sklearn\\base.py\", line 1144, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"C:\\Users\\Riya\\Desktop\\machine-learning\\env\\lib\\site-packages\\sklearn\\base.py\", line 637, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"C:\\Users\\Riya\\Desktop\\machine-learning\\env\\lib\\site-packages\\sklearn\\utils\\_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "C:\\Users\\Riya\\Desktop\\machine-learning\\env\\lib\\site-packages\\sklearn\\model_selection\\_search.py:976: UserWarning: One or more of the test scores are non-finite: [0.83044218 0.84685374 0.83443878 0.84268707 0.85518707        nan\n",
      " 0.84268707        nan        nan 0.83852041]\n",
      "  warnings.warn(\n"
     ]
    },
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       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 5, 10, 20, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 4, 6],\n",
       "                                        &#x27;n_estimators&#x27;: [10, 100, 200, 500,\n",
       "                                                         1000, 1200]},\n",
       "                   verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomizedSearchCV</label><div class=\"sk-toggleable__content\"><pre>RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
       "                   param_distributions={&#x27;max_depth&#x27;: [None, 5, 10, 20, 30],\n",
       "                                        &#x27;max_features&#x27;: [&#x27;auto&#x27;, &#x27;sqrt&#x27;],\n",
       "                                        &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                                        &#x27;min_samples_split&#x27;: [2, 4, 6],\n",
       "                                        &#x27;n_estimators&#x27;: [10, 100, 200, 500,\n",
       "                                                         1000, 1200]},\n",
       "                   verbose=2)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_jobs=-1)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=RandomForestClassifier(n_jobs=-1),\n",
       "                   param_distributions={'max_depth': [None, 5, 10, 20, 30],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 4, 6],\n",
       "                                        'n_estimators': [10, 100, 200, 500,\n",
       "                                                         1000, 1200]},\n",
       "                   verbose=2)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# importing the RandomizedSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# heart_disease_data = pd.read_csv(\"../Data/heart-disease.csv\")\n",
    "\n",
    "# now will gonna make the dic , with 5 above mentioned params\n",
    "# you can search about these params and it's working \n",
    "# the value which are given to theses params are already searched\n",
    "grid = {\"n_estimators\":[10,100,200,500,1000,1200],\n",
    "       \"max_depth\":[None,5,10,20,30],\n",
    "       \"max_features\":[\"auto\",\"sqrt\"],\n",
    "       \"min_samples_split\":[2,4,6],\n",
    "       \"min_samples_leaf\":[1,2,4]}\n",
    "\n",
    "# data being split into x and y\n",
    "x = heart_disease_data.drop(\"target\",axis=1)\n",
    "y = heart_disease_data[\"target\"]\n",
    "\n",
    "# now split the x and y in train and test \n",
    "x_train,x_test,y_train,y_test = train_test_split(x,y,train_size=0.8)\n",
    "\n",
    "\n",
    "# initialize randomforestclassifier(-1 means use all the machine processor)\n",
    "clf = RandomForestClassifier(n_jobs=-1)\n",
    "\n",
    "# Setup RandomizedSearchCV\n",
    "# where , estimator = model\n",
    "#         param_distribution = dic of params\n",
    "#         n_iter = 10 means (each params , will make 10 times combination with other params,which mean in total 5*10 = 50 combination will be checked)\n",
    "#         cv = no of params ,(which is here is 5)              \n",
    "\n",
    "rs_clf = RandomizedSearchCV(estimator=clf,\n",
    "                           param_distributions=grid,\n",
    "                           n_iter=10,\n",
    "                           cv=5,\n",
    "                           verbose=2)\n",
    "\n",
    "# train the data\n",
    "rs_clf.fit(x_train,y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a0b53613",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 100,\n",
       " 'min_samples_split': 6,\n",
       " 'min_samples_leaf': 2,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': None}"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now let's check the best combination made by RandomizedSearchCV\n",
    "rs_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "5893b202",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8032786885245902"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Scoring automatically uses the best hyperparameters\n",
    "rs_clf.score(x_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a076b25c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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