FSDS_GridSearchCrossValidation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RtGX3OkdD8g-"
   },
   "source": [
    "# **GridSearchCV** For Model optimization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "h0RjYOukD8hA"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split, cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "X3DdRw2bqU86",
    "outputId": "60566e03-c8d6-45dc-b9d4-37b152dcfbbd"
   },
   "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>Preg</th>\n",
       "      <th>Plas</th>\n",
       "      <th>Pres</th>\n",
       "      <th>skin</th>\n",
       "      <th>test</th>\n",
       "      <th>mass</th>\n",
       "      <th>pedi</th>\n",
       "      <th>age</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>148</td>\n",
       "      <td>72</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>33.6</td>\n",
       "      <td>0.627</td>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>85</td>\n",
       "      <td>66</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>26.6</td>\n",
       "      <td>0.351</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>183</td>\n",
       "      <td>64</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.3</td>\n",
       "      <td>0.672</td>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>66</td>\n",
       "      <td>23</td>\n",
       "      <td>94</td>\n",
       "      <td>28.1</td>\n",
       "      <td>0.167</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>137</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>168</td>\n",
       "      <td>43.1</td>\n",
       "      <td>2.288</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>763</th>\n",
       "      <td>10</td>\n",
       "      <td>101</td>\n",
       "      <td>76</td>\n",
       "      <td>48</td>\n",
       "      <td>180</td>\n",
       "      <td>32.9</td>\n",
       "      <td>0.171</td>\n",
       "      <td>63</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2</td>\n",
       "      <td>122</td>\n",
       "      <td>70</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>36.8</td>\n",
       "      <td>0.340</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>765</th>\n",
       "      <td>5</td>\n",
       "      <td>121</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "      <td>112</td>\n",
       "      <td>26.2</td>\n",
       "      <td>0.245</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>1</td>\n",
       "      <td>126</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.1</td>\n",
       "      <td>0.349</td>\n",
       "      <td>47</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>767</th>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>70</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>0.315</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>768 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Preg  Plas  Pres  skin  test  mass   pedi  age  class\n",
       "0       6   148    72    35     0  33.6  0.627   50      1\n",
       "1       1    85    66    29     0  26.6  0.351   31      0\n",
       "2       8   183    64     0     0  23.3  0.672   32      1\n",
       "3       1    89    66    23    94  28.1  0.167   21      0\n",
       "4       0   137    40    35   168  43.1  2.288   33      1\n",
       "..    ...   ...   ...   ...   ...   ...    ...  ...    ...\n",
       "763    10   101    76    48   180  32.9  0.171   63      0\n",
       "764     2   122    70    27     0  36.8  0.340   27      0\n",
       "765     5   121    72    23   112  26.2  0.245   30      0\n",
       "766     1   126    60     0     0  30.1  0.349   47      1\n",
       "767     1    93    70    31     0  30.4  0.315   23      0\n",
       "\n",
       "[768 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('pima-indians-diabetes+%281%29.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "kCWx588ase24"
   },
   "outputs": [],
   "source": [
    "X = df.drop('class',axis=1)\n",
    "y=df['class']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gy1vfVDvuPBf"
   },
   "source": [
    "# Divide your data into three(3) parts: Training, Validation and Testing data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "-12xq7m-qEJ6"
   },
   "outputs": [],
   "source": [
    "#save 20% of your dataset for final testing\n",
    "# use 80% for training and validation process\n",
    "\n",
    "# STEPS:\n",
    "  #1. first divide our dataset into 80:20 (i.e. training(80) and testing(20))\n",
    "  #2. next divide the 80 part to training and validation (i.e. whatever ratio you prefer)\n",
    "\n",
    "\n",
    "X_train1, X_test, y_train1,y_test = train_test_split(X,y, test_size=.20,random_state=1)\n",
    "\n",
    "\n",
    "X_train2, X_val, y_train2,y_val = train_test_split(X_train1,y_train1, test_size=.25,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "I8iNeft7ueiN",
    "outputId": "6118cfb3-be21-4848-f980-f1ea992aad6d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total dataset 768\n",
      "Total test data saved for future use:  154\n",
      "Total training data:  460\n",
      "Total validation data:  154\n"
     ]
    }
   ],
   "source": [
    "print('Total dataset',len(X.values))\n",
    "print('Total test data saved for future use: ',len(X_test+y_test))\n",
    "print('Total training data: ',len(X_train2+y_train2))\n",
    "print('Total validation data: ',len(X_val+y_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "OuddWKw_Yrwd",
    "outputId": "46e8139f-17fb-4a8e-c80e-ea17c1e815b6"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "768"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "154+460+154"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "JvbIZ6UDDHlM"
   },
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_clf = KNeighborsClassifier()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Ak0uBB1YD8hC",
    "outputId": "7ac167a4-815f-4fc6-b941-e380fc526eaf"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn_clf.fit(X_train2,y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "umttCWaoD8hE",
    "outputId": "f0874a72-de2d-4dd8-9281-d9613dc24b6d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto',\n",
       " 'leaf_size': 30,\n",
       " 'metric': 'minkowski',\n",
       " 'metric_params': None,\n",
       " 'n_jobs': None,\n",
       " 'n_neighbors': 5,\n",
       " 'p': 2,\n",
       " 'weights': 'uniform'}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNeighborsClassifier().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "Qv2epaH3D8hH"
   },
   "outputs": [],
   "source": [
    "param_grid = {'n_neighbors':list(range(1,9)),\n",
    "               'algorithm':('auto', 'ball_tree', 'kd_tree', 'brute')\n",
    "               \n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "x1ehIboiv2w7"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "gs=GridSearchCV(knn_clf,param_grid,cv=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XT-ufg0OD8hJ",
    "outputId": "458e6e7b-f4c2-4911-8b38-d9c945aa66ca",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=20, estimator=KNeighborsClassifier(),\n",
       "             param_grid={'algorithm': ('auto', 'ball_tree', 'kd_tree', 'brute'),\n",
       "                         'n_neighbors': [1, 2, 3, 4, 5, 6, 7, 8]})"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.fit(X_train2,y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=8)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_index_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7260869565217392"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "O0tFV4paD8hL",
    "outputId": "047d33ab-12e4-42a8-a862-30e6e23ef455"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'algorithm': 'auto', 'n_neighbors': 8}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "8s3tZueAD8hN",
    "outputId": "ef02f52b-881e-46a4-a69d-3b0f475e7e8b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'algorithm': 'auto', 'n_neighbors': 1},\n",
       " {'algorithm': 'auto', 'n_neighbors': 2},\n",
       " {'algorithm': 'auto', 'n_neighbors': 3},\n",
       " {'algorithm': 'auto', 'n_neighbors': 4},\n",
       " {'algorithm': 'auto', 'n_neighbors': 5},\n",
       " {'algorithm': 'auto', 'n_neighbors': 6},\n",
       " {'algorithm': 'auto', 'n_neighbors': 7},\n",
       " {'algorithm': 'auto', 'n_neighbors': 8},\n",
       " {'algorithm': 'ball_tree', 'n_neighbors': 1},\n",
       " {'algorithm': 'ball_tree', 'n_neighbors': 2},\n",
       " {'algorithm': 'ball_tree', 'n_neighbors': 3},\n",
       " {'algorithm': 'ball_tree', 'n_neighbors': 4},\n",
       " {'algorithm': 'ball_tree', 'n_neighbors': 5},\n",
       " {'algorithm': 'ball_tree', 'n_neighbors': 6},\n",
       " {'algorithm': 'ball_tree', 'n_neighbors': 7},\n",
       " {'algorithm': 'ball_tree', 'n_neighbors': 8},\n",
       " {'algorithm': 'kd_tree', 'n_neighbors': 1},\n",
       " {'algorithm': 'kd_tree', 'n_neighbors': 2},\n",
       " {'algorithm': 'kd_tree', 'n_neighbors': 3},\n",
       " {'algorithm': 'kd_tree', 'n_neighbors': 4},\n",
       " {'algorithm': 'kd_tree', 'n_neighbors': 5},\n",
       " {'algorithm': 'kd_tree', 'n_neighbors': 6},\n",
       " {'algorithm': 'kd_tree', 'n_neighbors': 7},\n",
       " {'algorithm': 'kd_tree', 'n_neighbors': 8},\n",
       " {'algorithm': 'brute', 'n_neighbors': 1},\n",
       " {'algorithm': 'brute', 'n_neighbors': 2},\n",
       " {'algorithm': 'brute', 'n_neighbors': 3},\n",
       " {'algorithm': 'brute', 'n_neighbors': 4},\n",
       " {'algorithm': 'brute', 'n_neighbors': 5},\n",
       " {'algorithm': 'brute', 'n_neighbors': 6},\n",
       " {'algorithm': 'brute', 'n_neighbors': 7},\n",
       " {'algorithm': 'brute', 'n_neighbors': 8}]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.cv_results_['params']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "opa0v3KsD8hP",
    "outputId": "f4733d29-7019-457e-b66b-b5e7ce2a6140",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.67391304, 0.7       , 0.7       , 0.70652174, 0.7       ,\n",
       "       0.71086957, 0.71521739, 0.72608696, 0.67391304, 0.7       ,\n",
       "       0.7       , 0.70652174, 0.7       , 0.71086957, 0.71521739,\n",
       "       0.72608696, 0.67391304, 0.7       , 0.7       , 0.70652174,\n",
       "       0.7       , 0.71086957, 0.71521739, 0.72608696, 0.67391304,\n",
       "       0.7       , 0.7       , 0.70652174, 0.7       , 0.71086957,\n",
       "       0.71521739, 0.72608696])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00495034, 0.00389986, 0.00425053, 0.00305015, 0.00389968,\n",
       "        0.004599  , 0.00395061, 0.00450046, 0.00300052, 0.00315025,\n",
       "        0.00285062, 0.00425012, 0.00335013, 0.00320042, 0.00220016,\n",
       "        0.00195098, 0.00199968, 0.00215095, 0.00205024, 0.00224929,\n",
       "        0.00215012, 0.00180027, 0.0020502 , 0.0018501 , 0.00150064,\n",
       "        0.00195007, 0.00165001, 0.00169984, 0.0022006 , 0.00455222,\n",
       "        0.00214994, 0.00185078]),\n",
       " 'std_fit_time': array([0.00220243, 0.00062511, 0.00163903, 0.00086505, 0.0016999 ,\n",
       "        0.00159355, 0.00139616, 0.00092206, 0.00070701, 0.0007923 ,\n",
       "        0.00072634, 0.00147918, 0.00115204, 0.00092704, 0.0008722 ,\n",
       "        0.00058997, 0.00063279, 0.00047716, 0.00021762, 0.00053506,\n",
       "        0.00047663, 0.00040006, 0.00049807, 0.00035705, 0.00067144,\n",
       "        0.00073993, 0.00057151, 0.00055691, 0.00067859, 0.00233628,\n",
       "        0.00079217, 0.00057379]),\n",
       " 'mean_score_time': array([0.00559996, 0.00464997, 0.00459974, 0.00395033, 0.00475028,\n",
       "        0.00525039, 0.00484973, 0.00489963, 0.00489978, 0.00440017,\n",
       "        0.00439982, 0.00499994, 0.0039501 , 0.00404984, 0.00275004,\n",
       "        0.00224923, 0.00200022, 0.00214924, 0.00214982, 0.00220013,\n",
       "        0.00204986, 0.00224985, 0.00220019, 0.00215042, 0.00274999,\n",
       "        0.00265045, 0.00250022, 0.00285048, 0.00319958, 0.00584936,\n",
       "        0.00295061, 0.00269955]),\n",
       " 'std_score_time': array([0.00270918, 0.00072606, 0.00111418, 0.00153247, 0.00108954,\n",
       "        0.00147881, 0.00138815, 0.00088872, 0.00117885, 0.00131975,\n",
       "        0.00128027, 0.00270232, 0.0015327 , 0.00092062, 0.00113465,\n",
       "        0.00053669, 0.00044863, 0.00035654, 0.00047699, 0.00060006,\n",
       "        0.00038406, 0.00043317, 0.00050901, 0.00057186, 0.00194668,\n",
       "        0.00079236, 0.00097516, 0.00115215, 0.00147023, 0.00312465,\n",
       "        0.00066986, 0.00055611]),\n",
       " 'param_algorithm': masked_array(data=['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute'],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_neighbors': masked_array(data=[1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8, 1, 2,\n",
       "                    3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8],\n",
       "              mask=[False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False,\n",
       "                    False, False, False, False, False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'algorithm': 'auto', 'n_neighbors': 1},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 2},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 3},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 4},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 5},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 6},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 7},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 8},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 1},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 2},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 3},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 4},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 5},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 6},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 7},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 8},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 1},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 2},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 3},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 4},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 5},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 6},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 7},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 8},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 1},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 2},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 3},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 4},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 5},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 6},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 7},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 8}],\n",
       " 'split0_test_score': array([0.73913043, 0.69565217, 0.7826087 , 0.82608696, 0.86956522,\n",
       "        0.86956522, 0.82608696, 0.86956522, 0.73913043, 0.69565217,\n",
       "        0.7826087 , 0.82608696, 0.86956522, 0.86956522, 0.82608696,\n",
       "        0.86956522, 0.73913043, 0.69565217, 0.7826087 , 0.82608696,\n",
       "        0.86956522, 0.86956522, 0.82608696, 0.86956522, 0.73913043,\n",
       "        0.69565217, 0.7826087 , 0.82608696, 0.86956522, 0.86956522,\n",
       "        0.82608696, 0.86956522]),\n",
       " 'split1_test_score': array([0.73913043, 0.7826087 , 0.7826087 , 0.73913043, 0.69565217,\n",
       "        0.73913043, 0.7826087 , 0.73913043, 0.73913043, 0.7826087 ,\n",
       "        0.7826087 , 0.73913043, 0.69565217, 0.73913043, 0.7826087 ,\n",
       "        0.73913043, 0.73913043, 0.7826087 , 0.7826087 , 0.73913043,\n",
       "        0.69565217, 0.73913043, 0.7826087 , 0.73913043, 0.73913043,\n",
       "        0.7826087 , 0.7826087 , 0.73913043, 0.69565217, 0.73913043,\n",
       "        0.7826087 , 0.73913043]),\n",
       " 'split2_test_score': array([0.65217391, 0.7826087 , 0.69565217, 0.73913043, 0.69565217,\n",
       "        0.73913043, 0.7826087 , 0.73913043, 0.65217391, 0.7826087 ,\n",
       "        0.69565217, 0.73913043, 0.69565217, 0.73913043, 0.7826087 ,\n",
       "        0.73913043, 0.65217391, 0.7826087 , 0.69565217, 0.73913043,\n",
       "        0.69565217, 0.73913043, 0.7826087 , 0.73913043, 0.65217391,\n",
       "        0.7826087 , 0.69565217, 0.73913043, 0.69565217, 0.73913043,\n",
       "        0.7826087 , 0.73913043]),\n",
       " 'split3_test_score': array([0.60869565, 0.60869565, 0.65217391, 0.69565217, 0.60869565,\n",
       "        0.65217391, 0.65217391, 0.69565217, 0.60869565, 0.60869565,\n",
       "        0.65217391, 0.69565217, 0.60869565, 0.65217391, 0.65217391,\n",
       "        0.69565217, 0.60869565, 0.60869565, 0.65217391, 0.69565217,\n",
       "        0.60869565, 0.65217391, 0.65217391, 0.69565217, 0.60869565,\n",
       "        0.60869565, 0.65217391, 0.69565217, 0.60869565, 0.65217391,\n",
       "        0.65217391, 0.69565217]),\n",
       " 'split4_test_score': array([0.73913043, 0.65217391, 0.65217391, 0.65217391, 0.56521739,\n",
       "        0.65217391, 0.65217391, 0.73913043, 0.73913043, 0.65217391,\n",
       "        0.65217391, 0.65217391, 0.56521739, 0.65217391, 0.65217391,\n",
       "        0.73913043, 0.73913043, 0.65217391, 0.65217391, 0.65217391,\n",
       "        0.56521739, 0.65217391, 0.65217391, 0.73913043, 0.73913043,\n",
       "        0.65217391, 0.65217391, 0.65217391, 0.56521739, 0.65217391,\n",
       "        0.65217391, 0.73913043]),\n",
       " 'split5_test_score': array([0.69565217, 0.69565217, 0.73913043, 0.73913043, 0.73913043,\n",
       "        0.69565217, 0.69565217, 0.65217391, 0.69565217, 0.69565217,\n",
       "        0.73913043, 0.73913043, 0.73913043, 0.69565217, 0.69565217,\n",
       "        0.65217391, 0.69565217, 0.69565217, 0.73913043, 0.73913043,\n",
       "        0.73913043, 0.69565217, 0.69565217, 0.65217391, 0.69565217,\n",
       "        0.69565217, 0.73913043, 0.73913043, 0.73913043, 0.69565217,\n",
       "        0.69565217, 0.65217391]),\n",
       " 'split6_test_score': array([0.7826087 , 0.65217391, 0.69565217, 0.69565217, 0.65217391,\n",
       "        0.69565217, 0.73913043, 0.73913043, 0.7826087 , 0.65217391,\n",
       "        0.69565217, 0.69565217, 0.65217391, 0.69565217, 0.73913043,\n",
       "        0.73913043, 0.7826087 , 0.65217391, 0.69565217, 0.69565217,\n",
       "        0.65217391, 0.69565217, 0.73913043, 0.73913043, 0.7826087 ,\n",
       "        0.65217391, 0.69565217, 0.69565217, 0.65217391, 0.69565217,\n",
       "        0.73913043, 0.73913043]),\n",
       " 'split7_test_score': array([0.65217391, 0.65217391, 0.65217391, 0.60869565, 0.56521739,\n",
       "        0.56521739, 0.56521739, 0.56521739, 0.65217391, 0.65217391,\n",
       "        0.65217391, 0.60869565, 0.56521739, 0.56521739, 0.56521739,\n",
       "        0.56521739, 0.65217391, 0.65217391, 0.65217391, 0.60869565,\n",
       "        0.56521739, 0.56521739, 0.56521739, 0.56521739, 0.65217391,\n",
       "        0.65217391, 0.65217391, 0.60869565, 0.56521739, 0.56521739,\n",
       "        0.56521739, 0.56521739]),\n",
       " 'split8_test_score': array([0.69565217, 0.73913043, 0.69565217, 0.73913043, 0.86956522,\n",
       "        0.7826087 , 0.7826087 , 0.7826087 , 0.69565217, 0.73913043,\n",
       "        0.69565217, 0.73913043, 0.86956522, 0.7826087 , 0.7826087 ,\n",
       "        0.7826087 , 0.69565217, 0.73913043, 0.69565217, 0.73913043,\n",
       "        0.86956522, 0.7826087 , 0.7826087 , 0.7826087 , 0.69565217,\n",
       "        0.73913043, 0.69565217, 0.73913043, 0.86956522, 0.7826087 ,\n",
       "        0.7826087 , 0.7826087 ]),\n",
       " 'split9_test_score': array([0.52173913, 0.60869565, 0.65217391, 0.60869565, 0.73913043,\n",
       "        0.60869565, 0.60869565, 0.60869565, 0.52173913, 0.60869565,\n",
       "        0.65217391, 0.60869565, 0.73913043, 0.60869565, 0.60869565,\n",
       "        0.60869565, 0.52173913, 0.60869565, 0.65217391, 0.60869565,\n",
       "        0.73913043, 0.60869565, 0.60869565, 0.60869565, 0.52173913,\n",
       "        0.60869565, 0.65217391, 0.60869565, 0.73913043, 0.60869565,\n",
       "        0.60869565, 0.60869565]),\n",
       " 'split10_test_score': array([0.73913043, 0.69565217, 0.73913043, 0.7826087 , 0.73913043,\n",
       "        0.82608696, 0.7826087 , 0.86956522, 0.73913043, 0.69565217,\n",
       "        0.73913043, 0.7826087 , 0.73913043, 0.82608696, 0.7826087 ,\n",
       "        0.86956522, 0.73913043, 0.69565217, 0.73913043, 0.7826087 ,\n",
       "        0.73913043, 0.82608696, 0.7826087 , 0.86956522, 0.73913043,\n",
       "        0.69565217, 0.73913043, 0.7826087 , 0.73913043, 0.82608696,\n",
       "        0.7826087 , 0.86956522]),\n",
       " 'split11_test_score': array([0.65217391, 0.60869565, 0.69565217, 0.65217391, 0.65217391,\n",
       "        0.69565217, 0.69565217, 0.69565217, 0.65217391, 0.60869565,\n",
       "        0.69565217, 0.65217391, 0.65217391, 0.69565217, 0.69565217,\n",
       "        0.69565217, 0.65217391, 0.60869565, 0.69565217, 0.65217391,\n",
       "        0.65217391, 0.69565217, 0.69565217, 0.69565217, 0.65217391,\n",
       "        0.60869565, 0.69565217, 0.65217391, 0.65217391, 0.69565217,\n",
       "        0.69565217, 0.69565217]),\n",
       " 'split12_test_score': array([0.65217391, 0.7826087 , 0.73913043, 0.65217391, 0.69565217,\n",
       "        0.65217391, 0.65217391, 0.65217391, 0.65217391, 0.7826087 ,\n",
       "        0.73913043, 0.65217391, 0.69565217, 0.65217391, 0.65217391,\n",
       "        0.65217391, 0.65217391, 0.7826087 , 0.73913043, 0.65217391,\n",
       "        0.69565217, 0.65217391, 0.65217391, 0.65217391, 0.65217391,\n",
       "        0.7826087 , 0.73913043, 0.65217391, 0.69565217, 0.65217391,\n",
       "        0.65217391, 0.65217391]),\n",
       " 'split13_test_score': array([0.7826087 , 0.69565217, 0.7826087 , 0.73913043, 0.65217391,\n",
       "        0.65217391, 0.65217391, 0.56521739, 0.7826087 , 0.69565217,\n",
       "        0.7826087 , 0.73913043, 0.65217391, 0.65217391, 0.65217391,\n",
       "        0.56521739, 0.7826087 , 0.69565217, 0.7826087 , 0.73913043,\n",
       "        0.65217391, 0.65217391, 0.65217391, 0.56521739, 0.7826087 ,\n",
       "        0.69565217, 0.7826087 , 0.73913043, 0.65217391, 0.65217391,\n",
       "        0.65217391, 0.56521739]),\n",
       " 'split14_test_score': array([0.69565217, 0.69565217, 0.65217391, 0.65217391, 0.73913043,\n",
       "        0.73913043, 0.7826087 , 0.7826087 , 0.69565217, 0.69565217,\n",
       "        0.65217391, 0.65217391, 0.73913043, 0.73913043, 0.7826087 ,\n",
       "        0.7826087 , 0.69565217, 0.69565217, 0.65217391, 0.65217391,\n",
       "        0.73913043, 0.73913043, 0.7826087 , 0.7826087 , 0.69565217,\n",
       "        0.69565217, 0.65217391, 0.65217391, 0.73913043, 0.73913043,\n",
       "        0.7826087 , 0.7826087 ]),\n",
       " 'split15_test_score': array([0.65217391, 0.86956522, 0.73913043, 0.86956522, 0.7826087 ,\n",
       "        0.82608696, 0.7826087 , 0.86956522, 0.65217391, 0.86956522,\n",
       "        0.73913043, 0.86956522, 0.7826087 , 0.82608696, 0.7826087 ,\n",
       "        0.86956522, 0.65217391, 0.86956522, 0.73913043, 0.86956522,\n",
       "        0.7826087 , 0.82608696, 0.7826087 , 0.86956522, 0.65217391,\n",
       "        0.86956522, 0.73913043, 0.86956522, 0.7826087 , 0.82608696,\n",
       "        0.7826087 , 0.86956522]),\n",
       " 'split16_test_score': array([0.65217391, 0.69565217, 0.56521739, 0.65217391, 0.52173913,\n",
       "        0.56521739, 0.60869565, 0.65217391, 0.65217391, 0.69565217,\n",
       "        0.56521739, 0.65217391, 0.52173913, 0.56521739, 0.60869565,\n",
       "        0.65217391, 0.65217391, 0.69565217, 0.56521739, 0.65217391,\n",
       "        0.52173913, 0.56521739, 0.60869565, 0.65217391, 0.65217391,\n",
       "        0.69565217, 0.56521739, 0.65217391, 0.52173913, 0.56521739,\n",
       "        0.60869565, 0.65217391]),\n",
       " 'split17_test_score': array([0.69565217, 0.69565217, 0.65217391, 0.69565217, 0.73913043,\n",
       "        0.65217391, 0.65217391, 0.65217391, 0.69565217, 0.69565217,\n",
       "        0.65217391, 0.69565217, 0.73913043, 0.65217391, 0.65217391,\n",
       "        0.65217391, 0.69565217, 0.69565217, 0.65217391, 0.69565217,\n",
       "        0.73913043, 0.65217391, 0.65217391, 0.65217391, 0.69565217,\n",
       "        0.69565217, 0.65217391, 0.69565217, 0.73913043, 0.65217391,\n",
       "        0.65217391, 0.65217391]),\n",
       " 'split18_test_score': array([0.43478261, 0.65217391, 0.60869565, 0.56521739, 0.60869565,\n",
       "        0.69565217, 0.65217391, 0.69565217, 0.43478261, 0.65217391,\n",
       "        0.60869565, 0.56521739, 0.60869565, 0.69565217, 0.65217391,\n",
       "        0.69565217, 0.43478261, 0.65217391, 0.60869565, 0.56521739,\n",
       "        0.60869565, 0.69565217, 0.65217391, 0.69565217, 0.43478261,\n",
       "        0.65217391, 0.60869565, 0.56521739, 0.60869565, 0.69565217,\n",
       "        0.65217391, 0.69565217]),\n",
       " 'split19_test_score': array([0.69565217, 0.73913043, 0.82608696, 0.82608696, 0.86956522,\n",
       "        0.91304348, 0.95652174, 0.95652174, 0.69565217, 0.73913043,\n",
       "        0.82608696, 0.82608696, 0.86956522, 0.91304348, 0.95652174,\n",
       "        0.95652174, 0.69565217, 0.73913043, 0.82608696, 0.82608696,\n",
       "        0.86956522, 0.91304348, 0.95652174, 0.95652174, 0.69565217,\n",
       "        0.73913043, 0.82608696, 0.82608696, 0.86956522, 0.91304348,\n",
       "        0.95652174, 0.95652174]),\n",
       " 'mean_test_score': array([0.67391304, 0.7       , 0.7       , 0.70652174, 0.7       ,\n",
       "        0.71086957, 0.71521739, 0.72608696, 0.67391304, 0.7       ,\n",
       "        0.7       , 0.70652174, 0.7       , 0.71086957, 0.71521739,\n",
       "        0.72608696, 0.67391304, 0.7       , 0.7       , 0.70652174,\n",
       "        0.7       , 0.71086957, 0.71521739, 0.72608696, 0.67391304,\n",
       "        0.7       , 0.7       , 0.70652174, 0.7       , 0.71086957,\n",
       "        0.71521739, 0.72608696]),\n",
       " 'std_test_score': array([0.08075729, 0.06579455, 0.06434195, 0.07762422, 0.09809143,\n",
       "        0.09251271, 0.09169172, 0.10298017, 0.08075729, 0.06579455,\n",
       "        0.06434195, 0.07762422, 0.09809143, 0.09251271, 0.09169172,\n",
       "        0.10298017, 0.08075729, 0.06579455, 0.06434195, 0.07762422,\n",
       "        0.09809143, 0.09251271, 0.09169172, 0.10298017, 0.08075729,\n",
       "        0.06579455, 0.06434195, 0.07762422, 0.09809143, 0.09251271,\n",
       "        0.09169172, 0.10298017]),\n",
       " 'rank_test_score': array([29, 17, 21, 13, 21,  9,  5,  1, 29, 17, 21, 13, 21,  9,  5,  1, 29,\n",
       "        17, 21, 13, 21,  9,  5,  1, 29, 17, 21, 13, 21,  9,  5,  1])}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f10rnJuPD8hR",
    "outputId": "fc4ae7f6-35f4-4922-e74c-d55e1cbc6540"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=8)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "knn_clf = KNeighborsClassifier(n_neighbors=8, algorithm='auto')\n",
    "\n",
    "knn_clf.fit(X_train2,y_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZyREPimND8hT",
    "outputId": "2505efcb-8eb5-4d0d-e978-8016189ac485"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy:  0.7467532467532467\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "\n",
    "y_pred = knn_clf.predict(X_test)\n",
    "\n",
    "print('Accuracy: ', metrics.accuracy_score(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 416
    },
    "id": "0kmK-FBZ0Tvu",
    "outputId": "eb6b07a9-f64a-42d0-c0f1-a659b28d8b3d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "conf_matrix = confusion_matrix(y_test,y_pred)\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "import seaborn as sns\n",
    "sns.heatmap(conf_matrix, annot=True)\n",
    " \n",
    "# fig, ax = plot_confusion_matrix(conf_mat=conf_matrix, figsize=(6, 6))\n",
    "plt.xlabel('Predictions', fontsize=18)\n",
    "plt.ylabel('Actuals', fontsize=18)\n",
    "plt.title('Confusion Matrix', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kOb1C-gX2xYR",
    "outputId": "3b3b5d6b-e20b-4da1-858f-0370d196e069"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.76      0.89      0.82        99\n",
      "           1       0.71      0.49      0.58        55\n",
      "\n",
      "    accuracy                           0.75       154\n",
      "   macro avg       0.73      0.69      0.70       154\n",
      "weighted avg       0.74      0.75      0.73       154\n",
      "\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "fFG9lskc4Pt8",
    "outputId": "0e3af13e-5241-4a2b-e46a-7d148afe2f0b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y_scores = knn_clf.predict_proba(X_test)\n",
    "fpr, tpr, threshold = roc_curve(y_test, y_scores[:, 1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of kNN')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Another example of Gridsearch CV with Pipeline and ROC/AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>id</th>\n",
       "      <th>diagnosis</th>\n",
       "      <th>radius_mean</th>\n",
       "      <th>texture_mean</th>\n",
       "      <th>perimeter_mean</th>\n",
       "      <th>area_mean</th>\n",
       "      <th>smoothness_mean</th>\n",
       "      <th>compactness_mean</th>\n",
       "      <th>concavity_mean</th>\n",
       "      <th>points_mean</th>\n",
       "      <th>...</th>\n",
       "      <th>radius_worst</th>\n",
       "      <th>texture_worst</th>\n",
       "      <th>perimeter_worst</th>\n",
       "      <th>area_worst</th>\n",
       "      <th>smoothness_worst</th>\n",
       "      <th>compactness_worst</th>\n",
       "      <th>concavity_worst</th>\n",
       "      <th>points_worst</th>\n",
       "      <th>symmetry_worst</th>\n",
       "      <th>dimension_worst</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>87139402</td>\n",
       "      <td>B</td>\n",
       "      <td>12.32</td>\n",
       "      <td>12.39</td>\n",
       "      <td>78.85</td>\n",
       "      <td>464.1</td>\n",
       "      <td>0.10280</td>\n",
       "      <td>0.06981</td>\n",
       "      <td>0.03987</td>\n",
       "      <td>0.03700</td>\n",
       "      <td>...</td>\n",
       "      <td>13.50</td>\n",
       "      <td>15.64</td>\n",
       "      <td>86.97</td>\n",
       "      <td>549.1</td>\n",
       "      <td>0.1385</td>\n",
       "      <td>0.1266</td>\n",
       "      <td>0.12420</td>\n",
       "      <td>0.09391</td>\n",
       "      <td>0.2827</td>\n",
       "      <td>0.06771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8910251</td>\n",
       "      <td>B</td>\n",
       "      <td>10.60</td>\n",
       "      <td>18.95</td>\n",
       "      <td>69.28</td>\n",
       "      <td>346.4</td>\n",
       "      <td>0.09688</td>\n",
       "      <td>0.11470</td>\n",
       "      <td>0.06387</td>\n",
       "      <td>0.02642</td>\n",
       "      <td>...</td>\n",
       "      <td>11.88</td>\n",
       "      <td>22.94</td>\n",
       "      <td>78.28</td>\n",
       "      <td>424.8</td>\n",
       "      <td>0.1213</td>\n",
       "      <td>0.2515</td>\n",
       "      <td>0.19160</td>\n",
       "      <td>0.07926</td>\n",
       "      <td>0.2940</td>\n",
       "      <td>0.07587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>905520</td>\n",
       "      <td>B</td>\n",
       "      <td>11.04</td>\n",
       "      <td>16.83</td>\n",
       "      <td>70.92</td>\n",
       "      <td>373.2</td>\n",
       "      <td>0.10770</td>\n",
       "      <td>0.07804</td>\n",
       "      <td>0.03046</td>\n",
       "      <td>0.02480</td>\n",
       "      <td>...</td>\n",
       "      <td>12.41</td>\n",
       "      <td>26.44</td>\n",
       "      <td>79.93</td>\n",
       "      <td>471.4</td>\n",
       "      <td>0.1369</td>\n",
       "      <td>0.1482</td>\n",
       "      <td>0.10670</td>\n",
       "      <td>0.07431</td>\n",
       "      <td>0.2998</td>\n",
       "      <td>0.07881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>868871</td>\n",
       "      <td>B</td>\n",
       "      <td>11.28</td>\n",
       "      <td>13.39</td>\n",
       "      <td>73.00</td>\n",
       "      <td>384.8</td>\n",
       "      <td>0.11640</td>\n",
       "      <td>0.11360</td>\n",
       "      <td>0.04635</td>\n",
       "      <td>0.04796</td>\n",
       "      <td>...</td>\n",
       "      <td>11.92</td>\n",
       "      <td>15.77</td>\n",
       "      <td>76.53</td>\n",
       "      <td>434.0</td>\n",
       "      <td>0.1367</td>\n",
       "      <td>0.1822</td>\n",
       "      <td>0.08669</td>\n",
       "      <td>0.08611</td>\n",
       "      <td>0.2102</td>\n",
       "      <td>0.06784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9012568</td>\n",
       "      <td>B</td>\n",
       "      <td>15.19</td>\n",
       "      <td>13.21</td>\n",
       "      <td>97.65</td>\n",
       "      <td>711.8</td>\n",
       "      <td>0.07963</td>\n",
       "      <td>0.06934</td>\n",
       "      <td>0.03393</td>\n",
       "      <td>0.02657</td>\n",
       "      <td>...</td>\n",
       "      <td>16.20</td>\n",
       "      <td>15.73</td>\n",
       "      <td>104.50</td>\n",
       "      <td>819.1</td>\n",
       "      <td>0.1126</td>\n",
       "      <td>0.1737</td>\n",
       "      <td>0.13620</td>\n",
       "      <td>0.08178</td>\n",
       "      <td>0.2487</td>\n",
       "      <td>0.06766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>911320502</td>\n",
       "      <td>B</td>\n",
       "      <td>13.17</td>\n",
       "      <td>18.22</td>\n",
       "      <td>84.28</td>\n",
       "      <td>537.3</td>\n",
       "      <td>0.07466</td>\n",
       "      <td>0.05994</td>\n",
       "      <td>0.04859</td>\n",
       "      <td>0.02870</td>\n",
       "      <td>...</td>\n",
       "      <td>14.90</td>\n",
       "      <td>23.89</td>\n",
       "      <td>95.10</td>\n",
       "      <td>687.6</td>\n",
       "      <td>0.1282</td>\n",
       "      <td>0.1965</td>\n",
       "      <td>0.18760</td>\n",
       "      <td>0.10450</td>\n",
       "      <td>0.2235</td>\n",
       "      <td>0.06925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>898677</td>\n",
       "      <td>B</td>\n",
       "      <td>10.26</td>\n",
       "      <td>14.71</td>\n",
       "      <td>66.20</td>\n",
       "      <td>321.6</td>\n",
       "      <td>0.09882</td>\n",
       "      <td>0.09159</td>\n",
       "      <td>0.03581</td>\n",
       "      <td>0.02037</td>\n",
       "      <td>...</td>\n",
       "      <td>10.88</td>\n",
       "      <td>19.48</td>\n",
       "      <td>70.89</td>\n",
       "      <td>357.1</td>\n",
       "      <td>0.1360</td>\n",
       "      <td>0.1636</td>\n",
       "      <td>0.07162</td>\n",
       "      <td>0.04074</td>\n",
       "      <td>0.2434</td>\n",
       "      <td>0.08488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>873885</td>\n",
       "      <td>M</td>\n",
       "      <td>15.28</td>\n",
       "      <td>22.41</td>\n",
       "      <td>98.92</td>\n",
       "      <td>710.6</td>\n",
       "      <td>0.09057</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.05375</td>\n",
       "      <td>0.03263</td>\n",
       "      <td>...</td>\n",
       "      <td>17.80</td>\n",
       "      <td>28.03</td>\n",
       "      <td>113.80</td>\n",
       "      <td>973.1</td>\n",
       "      <td>0.1301</td>\n",
       "      <td>0.3299</td>\n",
       "      <td>0.36300</td>\n",
       "      <td>0.12260</td>\n",
       "      <td>0.3175</td>\n",
       "      <td>0.09772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>911201</td>\n",
       "      <td>B</td>\n",
       "      <td>14.53</td>\n",
       "      <td>13.98</td>\n",
       "      <td>93.86</td>\n",
       "      <td>644.2</td>\n",
       "      <td>0.10990</td>\n",
       "      <td>0.09242</td>\n",
       "      <td>0.06895</td>\n",
       "      <td>0.06495</td>\n",
       "      <td>...</td>\n",
       "      <td>15.80</td>\n",
       "      <td>16.93</td>\n",
       "      <td>103.10</td>\n",
       "      <td>749.9</td>\n",
       "      <td>0.1347</td>\n",
       "      <td>0.1478</td>\n",
       "      <td>0.13730</td>\n",
       "      <td>0.10690</td>\n",
       "      <td>0.2606</td>\n",
       "      <td>0.07810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>9012795</td>\n",
       "      <td>M</td>\n",
       "      <td>21.37</td>\n",
       "      <td>15.10</td>\n",
       "      <td>141.30</td>\n",
       "      <td>1386.0</td>\n",
       "      <td>0.10010</td>\n",
       "      <td>0.15150</td>\n",
       "      <td>0.19320</td>\n",
       "      <td>0.12550</td>\n",
       "      <td>...</td>\n",
       "      <td>22.69</td>\n",
       "      <td>21.84</td>\n",
       "      <td>152.10</td>\n",
       "      <td>1535.0</td>\n",
       "      <td>0.1192</td>\n",
       "      <td>0.2840</td>\n",
       "      <td>0.40240</td>\n",
       "      <td>0.19660</td>\n",
       "      <td>0.2730</td>\n",
       "      <td>0.08666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            id diagnosis  radius_mean  texture_mean  perimeter_mean  \\\n",
       "0     87139402         B        12.32         12.39           78.85   \n",
       "1      8910251         B        10.60         18.95           69.28   \n",
       "2       905520         B        11.04         16.83           70.92   \n",
       "3       868871         B        11.28         13.39           73.00   \n",
       "4      9012568         B        15.19         13.21           97.65   \n",
       "..         ...       ...          ...           ...             ...   \n",
       "564  911320502         B        13.17         18.22           84.28   \n",
       "565     898677         B        10.26         14.71           66.20   \n",
       "566     873885         M        15.28         22.41           98.92   \n",
       "567     911201         B        14.53         13.98           93.86   \n",
       "568    9012795         M        21.37         15.10          141.30   \n",
       "\n",
       "     area_mean  smoothness_mean  compactness_mean  concavity_mean  \\\n",
       "0        464.1          0.10280           0.06981         0.03987   \n",
       "1        346.4          0.09688           0.11470         0.06387   \n",
       "2        373.2          0.10770           0.07804         0.03046   \n",
       "3        384.8          0.11640           0.11360         0.04635   \n",
       "4        711.8          0.07963           0.06934         0.03393   \n",
       "..         ...              ...               ...             ...   \n",
       "564      537.3          0.07466           0.05994         0.04859   \n",
       "565      321.6          0.09882           0.09159         0.03581   \n",
       "566      710.6          0.09057           0.10520         0.05375   \n",
       "567      644.2          0.10990           0.09242         0.06895   \n",
       "568     1386.0          0.10010           0.15150         0.19320   \n",
       "\n",
       "     points_mean  ...  radius_worst  texture_worst  perimeter_worst  \\\n",
       "0        0.03700  ...         13.50          15.64            86.97   \n",
       "1        0.02642  ...         11.88          22.94            78.28   \n",
       "2        0.02480  ...         12.41          26.44            79.93   \n",
       "3        0.04796  ...         11.92          15.77            76.53   \n",
       "4        0.02657  ...         16.20          15.73           104.50   \n",
       "..           ...  ...           ...            ...              ...   \n",
       "564      0.02870  ...         14.90          23.89            95.10   \n",
       "565      0.02037  ...         10.88          19.48            70.89   \n",
       "566      0.03263  ...         17.80          28.03           113.80   \n",
       "567      0.06495  ...         15.80          16.93           103.10   \n",
       "568      0.12550  ...         22.69          21.84           152.10   \n",
       "\n",
       "     area_worst  smoothness_worst  compactness_worst  concavity_worst  \\\n",
       "0         549.1            0.1385             0.1266          0.12420   \n",
       "1         424.8            0.1213             0.2515          0.19160   \n",
       "2         471.4            0.1369             0.1482          0.10670   \n",
       "3         434.0            0.1367             0.1822          0.08669   \n",
       "4         819.1            0.1126             0.1737          0.13620   \n",
       "..          ...               ...                ...              ...   \n",
       "564       687.6            0.1282             0.1965          0.18760   \n",
       "565       357.1            0.1360             0.1636          0.07162   \n",
       "566       973.1            0.1301             0.3299          0.36300   \n",
       "567       749.9            0.1347             0.1478          0.13730   \n",
       "568      1535.0            0.1192             0.2840          0.40240   \n",
       "\n",
       "     points_worst  symmetry_worst  dimension_worst  \n",
       "0         0.09391          0.2827          0.06771  \n",
       "1         0.07926          0.2940          0.07587  \n",
       "2         0.07431          0.2998          0.07881  \n",
       "3         0.08611          0.2102          0.06784  \n",
       "4         0.08178          0.2487          0.06766  \n",
       "..            ...             ...              ...  \n",
       "564       0.10450          0.2235          0.06925  \n",
       "565       0.04074          0.2434          0.08488  \n",
       "566       0.12260          0.3175          0.09772  \n",
       "567       0.10690          0.2606          0.07810  \n",
       "568       0.19660          0.2730          0.08666  \n",
       "\n",
       "[569 rows x 32 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_wisc=pd.read_csv('wisc_bc_data.csv')\n",
    "df_wisc\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_wisc=df_wisc.iloc[:,2:].values\n",
    "y_wisc=df_wisc.iloc[:,1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.232e+01, 1.239e+01, 7.885e+01, ..., 9.391e-02, 2.827e-01,\n",
       "        6.771e-02],\n",
       "       [1.060e+01, 1.895e+01, 6.928e+01, ..., 7.926e-02, 2.940e-01,\n",
       "        7.587e-02],\n",
       "       [1.104e+01, 1.683e+01, 7.092e+01, ..., 7.431e-02, 2.998e-01,\n",
       "        7.881e-02],\n",
       "       ...,\n",
       "       [1.528e+01, 2.241e+01, 9.892e+01, ..., 1.226e-01, 3.175e-01,\n",
       "        9.772e-02],\n",
       "       [1.453e+01, 1.398e+01, 9.386e+01, ..., 1.069e-01, 2.606e-01,\n",
       "        7.810e-02],\n",
       "       [2.137e+01, 1.510e+01, 1.413e+02, ..., 1.966e-01, 2.730e-01,\n",
       "        8.666e-02]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_wisc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B',\n",
       "       'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M',\n",
       "       'B', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'B', 'M',\n",
       "       'M', 'M', 'M', 'M', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'B',\n",
       "       'B', 'B', 'M', 'B', 'B', 'B', 'M', 'M', 'M', 'M', 'M', 'M', 'M',\n",
       "       'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B',\n",
       "       'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B',\n",
       "       'B', 'M', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'M',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'M',\n",
       "       'M', 'M', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'M', 'M', 'M',\n",
       "       'M', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'M', 'B', 'M', 'M', 'M',\n",
       "       'M', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'M', 'B',\n",
       "       'B', 'M', 'M', 'M', 'B', 'M', 'B', 'M', 'B', 'M', 'B', 'B', 'M',\n",
       "       'B', 'M', 'B', 'B', 'M', 'M', 'M', 'B', 'M', 'B', 'B', 'M', 'M',\n",
       "       'M', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M',\n",
       "       'B', 'M', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B',\n",
       "       'B', 'M', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'B', 'M',\n",
       "       'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'M', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'B', 'M', 'B',\n",
       "       'B', 'M', 'M', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B',\n",
       "       'M', 'M', 'M', 'B', 'B', 'B', 'M', 'B', 'M', 'M', 'M', 'M', 'B',\n",
       "       'M', 'M', 'B', 'B', 'M', 'M', 'M', 'M', 'B', 'B', 'B', 'M', 'M',\n",
       "       'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'B',\n",
       "       'M', 'B', 'M', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B',\n",
       "       'M', 'B', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B',\n",
       "       'M', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B',\n",
       "       'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'M', 'B', 'M', 'B',\n",
       "       'M', 'B', 'B', 'M', 'B', 'B', 'B', 'M', 'M', 'B', 'B', 'B', 'M',\n",
       "       'M', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B', 'B', 'M',\n",
       "       'B', 'B', 'M', 'M', 'B', 'M', 'B', 'B', 'B', 'M', 'B', 'B', 'B',\n",
       "       'M', 'M', 'M', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B',\n",
       "       'M', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'M', 'B', 'B',\n",
       "       'B', 'B', 'M', 'B', 'B', 'M', 'M', 'B', 'B', 'M', 'B', 'B', 'M',\n",
       "       'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M', 'B', 'M', 'B', 'M', 'B',\n",
       "       'M', 'M', 'B', 'B', 'M', 'B', 'M', 'B', 'M', 'M', 'M', 'M', 'B',\n",
       "       'B', 'B', 'B', 'M', 'M', 'M', 'M', 'M', 'M', 'B', 'B', 'B', 'B',\n",
       "       'B', 'M', 'M', 'B', 'M', 'M', 'B', 'M', 'M', 'M', 'M', 'M', 'B',\n",
       "       'B', 'B', 'M', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'B', 'B', 'B',\n",
       "       'M', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'M', 'B',\n",
       "       'B', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'B', 'M', 'B', 'B', 'B',\n",
       "       'B', 'M', 'B', 'B', 'B', 'B', 'B', 'M', 'B', 'M'], dtype=object)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_wisc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()\n",
    "y_wisc=le.fit_transform(y_wisc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0,\n",
       "       0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0,\n",
       "       1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       0, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
       "       1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,\n",
       "       1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0,\n",
       "       0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0,\n",
       "       1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 0,\n",
       "       0, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1, 1,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0,\n",
       "       0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,\n",
       "       0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_wisc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "le.transform(['M','B'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 'M' transformed to 1 and 'B' transformed to 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save 20% of your dataset for final testing\n",
    "# use 80% for training and validation process\n",
    "\n",
    "# STEPS:\n",
    "  #1. first divide our dataset into 80:20 (i.e. training(80) and testing(20))\n",
    "  #2. next divide the 80 part to training and validation (i.e. whatever ratio you prefer)\n",
    "\n",
    "\n",
    "X_wisc_train1, X_wisc_test, y_wisc_train1,y_wisc_test = train_test_split(X_wisc,y_wisc, test_size=.20,random_state=1)\n",
    "\n",
    "\n",
    "X_wisc_train2, X_wisc_val, y_wisc_train2,y_wisc_val = train_test_split(X_wisc_train1,y_wisc_train1, test_size=.25,random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total dataset 569\n",
      "Total test data saved for future use:  114\n",
      "Total training data:  341\n",
      "Total validation data:  114\n"
     ]
    }
   ],
   "source": [
    "print('Total dataset',len(X_wisc))\n",
    "print('Total test data saved for future use: ',len(X_wisc_test))\n",
    "print('Total training data: ',len(X_wisc_train2))\n",
    "print('Total validation data: ',len(X_wisc_val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### implement Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "#from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.pipeline import Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy:96.49%\n"
     ]
    }
   ],
   "source": [
    "pipe_svc=Pipeline([('scl',StandardScaler()),('pca',PCA()),('svc',SVC())])\n",
    "pipe_svc.fit(X_wisc_train2,y_wisc_train2)\n",
    "print('Test accuracy:{:.2f}%'.format(pipe_svc.score(X_wisc_test,y_wisc_test)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perofrm Hyperparameter tuning using Gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'copy': True, 'with_mean': True, 'with_std': True}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StandardScaler().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'copy': True,\n",
       " 'iterated_power': 'auto',\n",
       " 'n_components': None,\n",
       " 'random_state': None,\n",
       " 'svd_solver': 'auto',\n",
       " 'tol': 0.0,\n",
       " 'whiten': False}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PCA().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'break_ties': False,\n",
       " 'cache_size': 200,\n",
       " 'class_weight': None,\n",
       " 'coef0': 0.0,\n",
       " 'decision_function_shape': 'ovr',\n",
       " 'degree': 3,\n",
       " 'gamma': 'scale',\n",
       " 'kernel': 'rbf',\n",
       " 'max_iter': -1,\n",
       " 'probability': False,\n",
       " 'random_state': None,\n",
       " 'shrinking': True,\n",
       " 'tol': 0.001,\n",
       " 'verbose': False}"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVC().get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_svc=Pipeline([('scl',StandardScaler()),('pca',PCA()),('svc',SVC())])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid2 = {'pca__n_components':[10,11,12,13,14,15,16,17,18],\n",
    "               'svc__C':[0.001,0.01,0.1,1,10,100],\n",
    "               'svc__gamma':[0.001,0.01,0.1,1,10,100],\n",
    "               'svc__kernel':['rbf','poly']\n",
    "               }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "gs_2=GridSearchCV(pipe_svc, param_grid=param_grid2, cv=5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5,\n",
       "             estimator=Pipeline(steps=[('scl', StandardScaler()),\n",
       "                                       ('pca', PCA()), ('svc', SVC())]),\n",
       "             param_grid={'pca__n_components': [10, 11, 12, 13, 14, 15, 16, 17,\n",
       "                                               18],\n",
       "                         'svc__C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
       "                         'svc__gamma': [0.001, 0.01, 0.1, 1, 10, 100],\n",
       "                         'svc__kernel': ['rbf', 'poly']})"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs_2.fit(X_wisc_train2,y_wisc_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best cross validation accuracy: 98.24%\n",
      "Best parameter: {'pca__n_components': 11, 'svc__C': 10, 'svc__gamma': 0.01, 'svc__kernel': 'rbf'}\n",
      "validation score: 96.49%\n"
     ]
    }
   ],
   "source": [
    "print('Best cross validation accuracy: {:.2f}%'.format(gs_2.best_score_*100))\n",
    "print('Best parameter: {}'.format(gs_2.best_params_))\n",
    "print('validation score: {:.2f}%'.format(gs_2.score(X_wisc_val,y_wisc_val)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### implement best parameter to the pipeline again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe_svc=Pipeline([('scl',StandardScaler()),('pca',PCA(n_components=11)),('svc',SVC(C=10, gamma=0.01,kernel='rbf',probability=True))])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scl', StandardScaler()), ('pca', PCA(n_components=11)),\n",
       "                ('svc', SVC(C=10, gamma=0.01, probability=True))])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipe_svc.fit(X_wisc_train2,y_wisc_train2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 95.61%\n"
     ]
    }
   ],
   "source": [
    "print('Test score: {:.2f}%'.format(pipe_svc.score(X_wisc_test,y_wisc_test)*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1,\n",
       "       1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 1,\n",
       "       0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,\n",
       "       1, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1,\n",
       "       0, 0, 0, 1])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_wisc_pred=pipe_svc.predict(X_wisc_test)\n",
    "y_wisc_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "conf_matrix_wisc = confusion_matrix(y_wisc_test,y_wisc_pred)\n",
    "\n",
    "plt.figure(figsize=(6, 6))\n",
    "import seaborn as sns\n",
    "sns.heatmap(conf_matrix_wisc, annot=True)\n",
    " \n",
    "# fig, ax = plot_confusion_matrix(conf_mat=conf_matrix, figsize=(6, 6))\n",
    "plt.xlabel('Predictions', fontsize=18)\n",
    "plt.ylabel('Actuals', fontsize=18)\n",
    "plt.title('Confusion Matrix', fontsize=18)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.97      0.97        77\n",
      "           1       0.94      0.92      0.93        37\n",
      "\n",
      "    accuracy                           0.96       114\n",
      "   macro avg       0.95      0.95      0.95       114\n",
      "weighted avg       0.96      0.96      0.96       114\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Classification Report\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_wisc_test, y_wisc_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# plot ROC/AUC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import auc\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "y_wisc_scores = pipe_svc.predict_proba(X_wisc_test)\n",
    "fpr, tpr, threshold = roc_curve(y_wisc_test, y_wisc_scores[:, 1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "\n",
    "plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)\n",
    "plt.legend(loc = 'lower right')\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0, 1])\n",
    "plt.ylim([0, 1])\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.title('ROC Curve of kNN')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}