Grid search CV.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "88ced14d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "iris=datasets.load_iris()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "12a1ae5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "eb29e946",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>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>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \\\n",
       "0                5.1               3.5                1.4               0.2   \n",
       "1                4.9               3.0                1.4               0.2   \n",
       "2                4.7               3.2                1.3               0.2   \n",
       "3                4.6               3.1                1.5               0.2   \n",
       "4                5.0               3.6                1.4               0.2   \n",
       "\n",
       "   flower  \n",
       "0  setosa  \n",
       "1  setosa  \n",
       "2  setosa  \n",
       "3  setosa  \n",
       "4  setosa  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "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.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a78acebf",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import numpy as np\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": 17,
   "id": "b2d67dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9111111111111111"
      ]
     },
     "execution_count": 17,
     "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": 18,
   "id": "d604e50a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7d0f6081",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.96666667, 1.        , 0.9       , 0.93333333, 1.        ])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_val_score(svm.SVC(kernel='rbf',C=30,gamma='auto'),iris.data,iris.target,cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a71b554a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.        , 0.9       , 0.96666667, 1.        ])"
      ]
     },
     "execution_count": 21,
     "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": 26,
   "id": "d5eb2e39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'rbf_10': 0.9800000000000001,\n",
       " 'rbf_20': 0.9666666666666668,\n",
       " 'rbf_30': 0.96,\n",
       " 'linear_10': 0.9733333333333334,\n",
       " 'linear_20': 0.9666666666666666,\n",
       " 'linear_30': 0.96}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kernel=['rbf','linear']\n",
    "c=[10,20,30]\n",
    "avg_score={}\n",
    "for kval in kernel:\n",
    "    for cval in c:\n",
    "        cv_score=cross_val_score(svm.SVC(kernel=kval,C=cval,gamma='auto'),iris.data,iris.target,cv=5)\n",
    "        avg_score[kval+'_'+str(cval)]=np.average(cv_score)\n",
    "avg_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "35af8a9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00293474, 0.00052571, 0.00192432, 0.001615  , 0.00159516,\n",
       "        0.00161567]),\n",
       " 'std_fit_time': array([0.00228015, 0.00066306, 0.00285226, 0.00323   , 0.00219788,\n",
       "        0.00323133]),\n",
       " 'mean_score_time': array([0.00113659, 0.00026121, 0.00094781, 0.        , 0.00010962,\n",
       "        0.        ]),\n",
       " 'std_score_time': array([0.00068573, 0.00032657, 0.00134955, 0.        , 0.00021925,\n",
       "        0.        ]),\n",
       " 'param_C': masked_array(data=[10, 10, 20, 20, 30, 30],\n",
       "              mask=[False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\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': 10, 'kernel': 'rbf'},\n",
       "  {'C': 10, 'kernel': 'linear'},\n",
       "  {'C': 20, 'kernel': 'rbf'},\n",
       "  {'C': 20, 'kernel': 'linear'},\n",
       "  {'C': 30, 'kernel': 'rbf'},\n",
       "  {'C': 30, 'kernel': 'linear'}],\n",
       " 'split0_test_score': array([0.96666667, 1.        , 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, 0.9       , 0.9       , 0.9       , 0.9       ,\n",
       "        0.9       ]),\n",
       " 'split3_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.93333333, 0.93333333,\n",
       "        0.9       ]),\n",
       " 'split4_test_score': array([1., 1., 1., 1., 1., 1.]),\n",
       " 'mean_test_score': array([0.98      , 0.97333333, 0.96666667, 0.96666667, 0.96      ,\n",
       "        0.96      ]),\n",
       " 'std_test_score': array([0.01632993, 0.03887301, 0.03651484, 0.0421637 , 0.03887301,\n",
       "        0.04898979]),\n",
       " 'rank_test_score': array([1, 2, 3, 4, 5, 5])}"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "clf=GridSearchCV(svm.SVC(gamma='auto'),{\n",
    "    'C':[10,20,30],\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": 30,
   "id": "bae169a4",
   "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>0.002935</td>\n",
       "      <td>0.002280</td>\n",
       "      <td>0.001137</td>\n",
       "      <td>0.000686</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>1</th>\n",
       "      <td>0.000526</td>\n",
       "      <td>0.000663</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000327</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>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001924</td>\n",
       "      <td>0.002852</td>\n",
       "      <td>0.000948</td>\n",
       "      <td>0.001350</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>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.001615</td>\n",
       "      <td>0.003230</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.001595</td>\n",
       "      <td>0.002198</td>\n",
       "      <td>0.000110</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>30</td>\n",
       "      <td>rbf</td>\n",
       "      <td>{'C': 30, 'kernel': 'rbf'}</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.038873</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.001616</td>\n",
       "      <td>0.003231</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>30</td>\n",
       "      <td>linear</td>\n",
       "      <td>{'C': 30, 'kernel': 'linear'}</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>0.048990</td>\n",
       "      <td>5</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       0.002935      0.002280         0.001137        0.000686      10   \n",
       "1       0.000526      0.000663         0.000261        0.000327      10   \n",
       "2       0.001924      0.002852         0.000948        0.001350      20   \n",
       "3       0.001615      0.003230         0.000000        0.000000      20   \n",
       "4       0.001595      0.002198         0.000110        0.000219      30   \n",
       "5       0.001616      0.003231         0.000000        0.000000      30   \n",
       "\n",
       "  param_kernel                         params  split0_test_score  \\\n",
       "0          rbf     {'C': 10, 'kernel': 'rbf'}           0.966667   \n",
       "1       linear  {'C': 10, 'kernel': 'linear'}           1.000000   \n",
       "2          rbf     {'C': 20, 'kernel': 'rbf'}           0.966667   \n",
       "3       linear  {'C': 20, 'kernel': 'linear'}           1.000000   \n",
       "4          rbf     {'C': 30, 'kernel': 'rbf'}           0.966667   \n",
       "5       linear  {'C': 30, '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           0.900000           0.966667                1.0   \n",
       "2                1.0           0.900000           0.966667                1.0   \n",
       "3                1.0           0.900000           0.933333                1.0   \n",
       "4                1.0           0.900000           0.933333                1.0   \n",
       "5                1.0           0.900000           0.900000                1.0   \n",
       "\n",
       "   mean_test_score  std_test_score  rank_test_score  \n",
       "0         0.980000        0.016330                1  \n",
       "1         0.973333        0.038873                2  \n",
       "2         0.966667        0.036515                3  \n",
       "3         0.966667        0.042164                4  \n",
       "4         0.960000        0.038873                5  \n",
       "5         0.960000        0.048990                5  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(clf.cv_results_)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "73094b95",
   "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>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>10</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",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30</td>\n",
       "      <td>linear</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  param_C param_kernel  mean_test_score\n",
       "0      10          rbf         0.980000\n",
       "1      10       linear         0.973333\n",
       "2      20          rbf         0.966667\n",
       "3      20       linear         0.966667\n",
       "4      30          rbf         0.960000\n",
       "5      30       linear         0.960000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['param_C','param_kernel','mean_test_score']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2e5ee227",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 10, 'kernel': 'rbf'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "12eb13ff",
   "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>30</td>\n",
       "      <td>rbf</td>\n",
       "      <td>0.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</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      30          rbf         0.960000\n",
       "1      20       linear         0.966667"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "rs=RandomizedSearchCV(svm.SVC(gamma='auto'),\n",
    "                     {\n",
    "                         'C':[10,20,30],\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": 35,
   "id": "026d2bfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "  #choosing best model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f6a2340c",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "12dd1fdd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_params={\n",
    "    'svm':{\n",
    "        'model':svm.SVC(gamma='auto'),\n",
    "        'params':{\n",
    "            'C':[10,20,30],\n",
    "            'kernel':['rbf','linear']\n",
    "        }\n",
    "    },\n",
    "    'random_forest':{ \n",
    "        'model':RandomForestClassifier(),\n",
    "        'params':{\n",
    "            'n_estimators':[1,5,10]\n",
    "        }\n",
    "    },\n",
    "    'logistic_regression':\n",
    "    {\n",
    "        'model':LogisticRegression(solver='liblinear',multi_class='auto'),\n",
    "        'params':{\n",
    "            'C':[1,5,10]\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "7c883309",
   "metadata": {},
   "outputs": [],
   "source": [
    "scores=[]\n",
    "for model_name ,mp in model_params.items():\n",
    "    clf=GridSearchCV(mp['model'],mp['params'],cv=5)\n",
    "    clf.fit(iris.data,iris.target)\n",
    "    scores.append({\n",
    "        'model':model_name,\n",
    "        'best_score':clf.best_score_,\n",
    "        'best_params':clf.best_params_\n",
    "    })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5baa91d0",
   "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>model</th>\n",
       "      <th>best_score</th>\n",
       "      <th>best_params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>svm</td>\n",
       "      <td>0.980000</td>\n",
       "      <td>{'C': 10, 'kernel': 'rbf'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>random_forest</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>{'n_estimators': 5}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>logistic_regression</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>{'C': 5}</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model  best_score                 best_params\n",
       "0                  svm    0.980000  {'C': 10, 'kernel': 'rbf'}\n",
       "1        random_forest    0.960000         {'n_estimators': 5}\n",
       "2  logistic_regression    0.966667                    {'C': 5}"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(scores,columns=['model','best_score','best_params'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea3b58ee",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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