GridSearchCV.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b6bf7d06-fdfb-46d1-9533-465e2e4a8b18",
   "metadata": {},
   "source": [
    "# Finding best model and hyper parameter tunning using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4b08e57c-2494-4161-a3be-549c61d920bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For iris flower dataset in sklearn library, we are going to find out best model and best hyper parameters using GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "52a7cbcb-1480-4b96-9a0b-706f6ae1a06d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm, datasets\n",
    "iris = datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff8b2797-a0a2-47c0-bc31-ec55301d4b5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal length (cm)</th>\n",
       "      <th>sepal width (cm)</th>\n",
       "      <th>petal length (cm)</th>\n",
       "      <th>petal width (cm)</th>\n",
       "      <th>flower</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>5.3</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "47                 4.6               3.2                1.4               0.2   \n",
       "48                 5.3               3.7                1.5               0.2   \n",
       "49                 5.0               3.3                1.4               0.2   \n",
       "50                 7.0               3.2                4.7               1.4   \n",
       "51                 6.4               3.2                4.5               1.5   \n",
       "..                 ...               ...                ...               ...   \n",
       "145                6.7               3.0                5.2               2.3   \n",
       "146                6.3               2.5                5.0               1.9   \n",
       "147                6.5               3.0                5.2               2.0   \n",
       "148                6.2               3.4                5.4               2.3   \n",
       "149                5.9               3.0                5.1               1.8   \n",
       "\n",
       "         flower  \n",
       "47       setosa  \n",
       "48       setosa  \n",
       "49       setosa  \n",
       "50   versicolor  \n",
       "51   versicolor  \n",
       "..          ...  \n",
       "145   virginica  \n",
       "146   virginica  \n",
       "147   virginica  \n",
       "148   virginica  \n",
       "149   virginica  \n",
       "\n",
       "[103 rows x 5 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame(iris.data,columns=iris.feature_names)\n",
    "df['flower'] = iris.target\n",
    "df['flower'] = df['flower'].apply(lambda x: iris.target_names[x])\n",
    "df[47:150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "89345e59-da3e-405e-bab3-f263b4e9841a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Approach 1: Use train_test_split and manually tune parameters by trial and error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fd73f4b5-a610-457e-8c8f-fd98fac74602",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b604441-6c93-48ca-b1cc-f0f363d3dce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9333333333333333"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = svm.SVC(kernel='rbf',C=30,gamma='auto')\n",
    "model.fit(X_train,y_train)\n",
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "58ea9352-863b-4562-8f7f-fd967e61a733",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Approach 2: Use K Fold Cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "821810cc-0be3-4b24-9e51-91dab80d4033",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Manually try suppling models with different parameters to cross_val_score function with 5 fold cross validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "721f6b3a-c91c-4ea5-a953-e089ca5aab6f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.        , 0.9       , 0.96666667, 1.        ])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(svm.SVC(kernel='linear',C=10,gamma='auto'),iris.data, iris.target, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99747732-95ba-4b79-addb-a054761738b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "633cc670-120d-4ce1-af99-de84137f399d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96666667, 1.        , 0.96666667, 0.96666667, 1.        ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(svm.SVC(kernel='rbf',C=10,gamma='auto'),iris.data, iris.target, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "022a92a7-4b1f-4b3c-9f90-d2b2ab146441",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96666667, 1.        , 0.9       , 0.96666667, 1.        ])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(svm.SVC(kernel='rbf',C=20,gamma='auto'),iris.data, iris.target, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6331b3be-22b9-40c8-986a-2955eada80fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Above approach is tiresome and very manual. We can use for loop as an alternative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f03d66c6-f158-4255-95a3-aa127c36aaed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rbf_1': 0.9800000000000001,\n",
       " 'rbf_10': 0.9800000000000001,\n",
       " 'rbf_20': 0.9666666666666668,\n",
       " 'linear_1': 0.9800000000000001,\n",
       " 'linear_10': 0.9733333333333334,\n",
       " 'linear_20': 0.9666666666666666}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernels = ['rbf', 'linear']\n",
    "C = [1,10,20]\n",
    "avg_scores = {}\n",
    "for kval in kernels:\n",
    "    for cval in C:\n",
    "        cv_scores = cross_val_score(svm.SVC(kernel=kval,C=cval,gamma='auto'),iris.data, iris.target, cv=5)\n",
    "        avg_scores[kval + '_' + str(cval)] = np.average(cv_scores)\n",
    "\n",
    "avg_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7d30f687-26d0-450f-83b2-5730ec0b616a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#From above results we can say that rbf with C=1 or 10 or linear with C=1 will give best performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e2c131e7-1334-4ef3-b2e3-4ecb1449ed6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Approach 3: Use GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "427f391e-9d96-442c-979a-51e076cef76d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#GridSearchCV does exactly same thing as for loop above but in a single line of code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c328963d-e488-4019-b6fe-488ed9046606",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00161686, 0.00237255, 0.00160079, 0.        , 0.00201368,\n",
       "        0.00118909]),\n",
       " 'std_fit_time': array([0.00323372, 0.00300396, 0.00320158, 0.        , 0.00310725,\n",
       "        0.0014552 ]),\n",
       " 'mean_score_time': array([0.        , 0.        , 0.00123558, 0.00078063, 0.00099554,\n",
       "        0.00044708]),\n",
       " 'std_score_time': array([0.        , 0.        , 0.00247116, 0.00095676, 0.00122142,\n",
       "        0.00089417]),\n",
       " 'param_C': masked_array(data=[1, 1, 10, 10, 20, 20],\n",
       "              mask=[False, False, False, False, False, False],\n",
       "        fill_value=999999),\n",
       " 'param_kernel': masked_array(data=['rbf', 'linear', 'rbf', 'linear', 'rbf', 'linear'],\n",
       "              mask=[False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'C': 1, 'kernel': 'rbf'},\n",
       "  {'C': 1, 'kernel': 'linear'},\n",
       "  {'C': 10, 'kernel': 'rbf'},\n",
       "  {'C': 10, 'kernel': 'linear'},\n",
       "  {'C': 20, 'kernel': 'rbf'},\n",
       "  {'C': 20, 'kernel': 'linear'}],\n",
       " 'split0_test_score': array([0.96666667, 0.96666667, 0.96666667, 1.        , 0.96666667,\n",
       "        1.        ]),\n",
       " 'split1_test_score': array([1., 1., 1., 1., 1., 1.]),\n",
       " 'split2_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.9       , 0.9       ,\n",
       "        0.9       ]),\n",
       " 'split3_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.96666667, 0.96666667,\n",
       "        0.93333333]),\n",
       " 'split4_test_score': array([1., 1., 1., 1., 1., 1.]),\n",
       " 'mean_test_score': array([0.98      , 0.98      , 0.98      , 0.97333333, 0.96666667,\n",
       "        0.96666667]),\n",
       " 'std_test_score': array([0.01632993, 0.01632993, 0.01632993, 0.03887301, 0.03651484,\n",
       "        0.0421637 ]),\n",
       " 'rank_test_score': array([1, 1, 1, 4, 5, 6])}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "clf = GridSearchCV(svm.SVC(gamma='auto'), {\n",
    "    'C': [1,10,20],\n",
    "    'kernel': ['rbf','linear']\n",
    "}, cv=5, return_train_score=False)\n",
    "clf.fit(iris.data, iris.target)\n",
    "clf.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6913d76b-ed3c-4484-930a-27c65648b4f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>split4_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.001617</td>\n",
       "      <td>0.003234</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.016330</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.002373</td>\n",
       "      <td>0.003004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.016330</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001601</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>0.001236</td>\n",
       "      <td>0.002471</td>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>0.016330</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000781</td>\n",
       "      <td>0.000957</td>\n",
       "      <td>10</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.973333</td>\n",
       "      <td>0.038873</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.002014</td>\n",
       "      <td>0.003107</td>\n",
       "      <td>0.000996</td>\n",
       "      <td>0.001221</td>\n",
       "      <td>20</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 20, 'kernel': 'rbf'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.036515</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.001189</td>\n",
       "      <td>0.001455</td>\n",
       "      <td>0.000447</td>\n",
       "      <td>0.000894</td>\n",
       "      <td>20</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 20, 'kernel': 'linear'}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.042164</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  param_C  \\\n",
       "0       0.001617      0.003234         0.000000        0.000000        1   \n",
       "1       0.002373      0.003004         0.000000        0.000000        1   \n",
       "2       0.001601      0.003202         0.001236        0.002471       10   \n",
       "3       0.000000      0.000000         0.000781        0.000957       10   \n",
       "4       0.002014      0.003107         0.000996        0.001221       20   \n",
       "5       0.001189      0.001455         0.000447        0.000894       20   \n",
       "\n",
       "  param_kernel                         params  split0_test_score  \\\n",
       "0          rbf      {'C': 1, 'kernel': 'rbf'}           0.966667   \n",
       "1       linear   {'C': 1, 'kernel': 'linear'}           0.966667   \n",
       "2          rbf     {'C': 10, 'kernel': 'rbf'}           0.966667   \n",
       "3       linear  {'C': 10, 'kernel': 'linear'}           1.000000   \n",
       "4          rbf     {'C': 20, 'kernel': 'rbf'}           0.966667   \n",
       "5       linear  {'C': 20, 'kernel': 'linear'}           1.000000   \n",
       "\n",
       "   split1_test_score  split2_test_score  split3_test_score  split4_test_score  \\\n",
       "0                1.0           0.966667           0.966667                1.0   \n",
       "1                1.0           0.966667           0.966667                1.0   \n",
       "2                1.0           0.966667           0.966667                1.0   \n",
       "3                1.0           0.900000           0.966667                1.0   \n",
       "4                1.0           0.900000           0.966667                1.0   \n",
       "5                1.0           0.900000           0.933333                1.0   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.980000        0.016330                1  \n",
       "1         0.980000        0.016330                1  \n",
       "2         0.980000        0.016330                1  \n",
       "3         0.973333        0.038873                4  \n",
       "4         0.966667        0.036515                5  \n",
       "5         0.966667        0.042164                6  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(clf.cv_results_)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "409d58a1-d154-4732-9344-c78c07fe2e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.973333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>20</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_C param_kernel  mean_test_score\n",
       "0        1          rbf         0.980000\n",
       "1        1       linear         0.980000\n",
       "2       10          rbf         0.980000\n",
       "3       10       linear         0.973333\n",
       "4       20          rbf         0.966667\n",
       "5       20       linear         0.966667"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['param_C','param_kernel','mean_test_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "647ffb98-2b25-4f34-a02c-138462a57977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b240bedd-0e9b-41aa-a86d-c22b87316f1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9800000000000001"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f8a7123d-8bfe-4769-b405-1efbb9e2addb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__abstractmethods__',\n",
       " '__annotations__',\n",
       " '__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getstate__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__sklearn_clone__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_abc_impl',\n",
       " '_build_request_for_signature',\n",
       " '_check_feature_names',\n",
       " '_check_n_features',\n",
       " '_check_refit_for_multimetric',\n",
       " '_doc_link_module',\n",
       " '_doc_link_template',\n",
       " '_doc_link_url_param_generator',\n",
       " '_estimator_type',\n",
       " '_format_results',\n",
       " '_get_default_requests',\n",
       " '_get_doc_link',\n",
       " '_get_metadata_request',\n",
       " '_get_param_names',\n",
       " '_get_routed_params_for_fit',\n",
       " '_get_scorers',\n",
       " '_get_tags',\n",
       " '_more_tags',\n",
       " '_parameter_constraints',\n",
       " '_repr_html_',\n",
       " '_repr_html_inner',\n",
       " '_repr_mimebundle_',\n",
       " '_required_parameters',\n",
       " '_run_search',\n",
       " '_select_best_index',\n",
       " '_sk_visual_block_',\n",
       " '_validate_data',\n",
       " '_validate_params',\n",
       " 'best_estimator_',\n",
       " 'best_index_',\n",
       " 'best_params_',\n",
       " 'best_score_',\n",
       " 'classes_',\n",
       " 'cv',\n",
       " 'cv_results_',\n",
       " 'decision_function',\n",
       " 'error_score',\n",
       " 'estimator',\n",
       " 'fit',\n",
       " 'get_metadata_routing',\n",
       " 'get_params',\n",
       " 'inverse_transform',\n",
       " 'multimetric_',\n",
       " 'n_features_in_',\n",
       " 'n_jobs',\n",
       " 'n_splits_',\n",
       " 'param_grid',\n",
       " 'pre_dispatch',\n",
       " 'predict',\n",
       " 'predict_log_proba',\n",
       " 'predict_proba',\n",
       " 'refit',\n",
       " 'refit_time_',\n",
       " 'return_train_score',\n",
       " 'score',\n",
       " 'score_samples',\n",
       " 'scorer_',\n",
       " 'scoring',\n",
       " 'set_params',\n",
       " 'transform',\n",
       " 'verbose']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(clf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fca9f709-d0d5-4bef-8116-c19e2e143eaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use RandomizedSearchCV to reduce number of iterations and with random combination of parameters. This is useful when you have too many parameters to try and your training time is longer. It helps reduce the cost of computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "7b61d574-660b-47da-ab60-d82d621211d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .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>param_C</th>\n",
       "      <th>param_kernel</th>\n",
       "      <th>mean_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.980000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.973333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   param_C param_kernel  mean_test_score\n",
       "0        1          rbf         0.980000\n",
       "1       10       linear         0.973333"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rs = RandomizedSearchCV(svm.SVC(gamma='auto'), {\n",
    "        'C': [1,10,20],\n",
    "        'kernel': ['rbf','linear']\n",
    "    }, \n",
    "    cv=5, \n",
    "    return_train_score=False, \n",
    "    n_iter=2\n",
    ")\n",
    "rs.fit(iris.data, iris.target)\n",
    "pd.DataFrame(rs.cv_results_)[['param_C','param_kernel','mean_test_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f2136bc-cc31-47e7-9ec7-0a0a65bde907",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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