KNN regression and classification with tuning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "77a6b1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a70b68f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F:\\\\Machine-learning\\\\Assignments\\\\data_set'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('F:\\\\Machine-learning\\\\Assignments\\\\data_set')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "55c956a8",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"weight-height.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f5284ade",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8555, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9de26f40",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6d7e89c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "label = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d32fa14",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Gender = label.fit_transform(df.Gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d62ae7b4",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender     Height      Weight\n",
       "0       1  73.847017  241.893563\n",
       "1       1  68.781904  162.310473\n",
       "2       1  74.110105  212.740856\n",
       "3       1  71.730978  220.042470\n",
       "4       1  69.881796  206.349801"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2648d78a",
   "metadata": {},
   "source": [
    "### split data into x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e15403d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.drop('Weight', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "d6b97260",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['Weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "54b35905",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    241.893563\n",
       "1    162.310473\n",
       "2    212.740856\n",
       "3    220.042470\n",
       "4    206.349801\n",
       "Name: Weight, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "7f3331bf",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>73.847017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>68.781904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>74.110105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>71.730978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>69.881796</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender     Height\n",
       "0       1  73.847017\n",
       "1       1  68.781904\n",
       "2       1  74.110105\n",
       "3       1  71.730978\n",
       "4       1  69.881796"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23827587",
   "metadata": {},
   "source": [
    "### split data into train and testing set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da0ce67d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.30, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "46c60a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5988, 2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c1b96bb",
   "metadata": {},
   "source": [
    "# KNN Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "67da33e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "\n",
    "KNN_reg = KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5aaad2df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor()"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_reg.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d8b91e51",
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = KNN_reg.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3cb6c999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([150.1761117 , 197.46276978, 179.03501546, ..., 160.26227546,\n",
       "       180.65461322, 200.56451578])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "933bbfa4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8838502073708883"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_reg.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93f8e489",
   "metadata": {},
   "source": [
    "#### Mean Square Error (MSE) & Mean Absolute Error(MAE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2aa04711",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123.15365863436983"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
    "\n",
    "mse = mean_squared_error(ytest, pred)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "41fde2a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.850747809837165"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae = mean_absolute_error(ytest, pred)\n",
    "mae"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9b79bff9",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6a58d7e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "linear_reg = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e2223f9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_reg.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8085546e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([143.83013436, 197.53417989, 184.63515112, ..., 160.7078525 ,\n",
       "       186.82852694, 201.4226542 ])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred2 = linear_reg.predict(xtest)\n",
    "pred2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "06815cb4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.905995960709116"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linear_reg.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "adb998dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.67250998070038"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse_linear_reg = mean_squared_error(ytest, pred2)\n",
    "mse_linear_reg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "977b5f93",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for this dataset linear regression is performing better than KNN regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1ca75544",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "37da6dcc",
   "metadata": {},
   "source": [
    "# Hyper-parameter Optimization Using RandomSearchCV \n",
    "## For KNN Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ddffd371",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e3ccf7f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "n_neighbors = [int(x) for x in np.linspace(start=20, stop=300, num=20)]\n",
    "weights = ['uniform', 'distance']\n",
    "algorithm = ['auto', 'ball_tree', 'kd_tree', 'brute']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ca7c3c19",
   "metadata": {},
   "outputs": [],
   "source": [
    "grids = {'n_neighbors': n_neighbors,\n",
    "         'weights': weights,\n",
    "         'algorithm': algorithm\n",
    "        }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f6074005",
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_reg2 = KNeighborsRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8209eb89",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmcv = RandomizedSearchCV(KNN_reg2, grids, n_iter=100, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "404ab515",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=KNeighborsRegressor(), n_iter=100,\n",
       "                   param_distributions={'algorithm': ['auto', 'ball_tree',\n",
       "                                                      'kd_tree', 'brute'],\n",
       "                                        'n_neighbors': [20, 34, 49, 64, 78, 93,\n",
       "                                                        108, 123, 137, 152, 167,\n",
       "                                                        182, 196, 211, 226, 241,\n",
       "                                                        255, 270, 285, 300],\n",
       "                                        'weights': ['uniform', 'distance']})"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bc5e39be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00119915, 0.00319738, 0.00784936, 0.00625024, 0.        ,\n",
       "        0.00625062, 0.00937433, 0.00624781, 0.01057558, 0.00625048,\n",
       "        0.00625086, 0.00312524, 0.01004734, 0.01250124, 0.0011992 ,\n",
       "        0.00312519, 0.        , 0.00745087, 0.00924854, 0.        ,\n",
       "        0.00119929, 0.00372419, 0.0031249 , 0.0102633 , 0.01353202,\n",
       "        0.00625033, 0.00312629, 0.00672483, 0.01562595, 0.00312653,\n",
       "        0.01069317, 0.00937395, 0.01097426, 0.00625105, 0.01046491,\n",
       "        0.0093749 , 0.00625024, 0.00652261, 0.00312486, 0.00612288,\n",
       "        0.00625148, 0.00625143, 0.00764909, 0.00079999, 0.00764923,\n",
       "        0.        , 0.00139875, 0.0093749 , 0.0014142 , 0.00472345,\n",
       "        0.00937643, 0.00312486, 0.0031261 , 0.00744958, 0.00059943,\n",
       "        0.00625238, 0.00059962, 0.00624995, 0.01409664, 0.00121469,\n",
       "        0.00694242, 0.01249962, 0.00764813, 0.00937648, 0.0011991 ,\n",
       "        0.0102643 , 0.00625186, 0.00415478, 0.01562691, 0.00764933,\n",
       "        0.00859122, 0.00659485, 0.00772071, 0.01317272, 0.00399671,\n",
       "        0.01151929, 0.0087935 , 0.00452485, 0.00692177, 0.01291609,\n",
       "        0.00099936, 0.00624871, 0.00937719, 0.00412383, 0.00299788,\n",
       "        0.00819397, 0.00412459, 0.00624957, 0.00395575, 0.00978746,\n",
       "        0.00779414, 0.00739136, 0.00772285, 0.00312467, 0.00060363,\n",
       "        0.00832043, 0.007723  , 0.00625048, 0.00937519, 0.00059967]),\n",
       " 'std_fit_time': array([2.39830017e-03, 3.96664412e-03, 6.98867415e-03, 7.65494749e-03,\n",
       "        0.00000000e+00, 7.65541465e-03, 7.65411055e-03, 7.65196924e-03,\n",
       "        6.47144940e-03, 7.65523974e-03, 7.65570731e-03, 6.25047684e-03,\n",
       "        5.73212080e-03, 6.25062011e-03, 2.39839554e-03, 6.25038147e-03,\n",
       "        0.00000000e+00, 7.02759197e-03, 5.89206566e-03, 0.00000000e+00,\n",
       "        1.46882812e-03, 6.06198404e-03, 6.24980927e-03, 8.53534519e-03,\n",
       "        4.19251971e-03, 7.65506436e-03, 6.25257492e-03, 8.27043688e-03,\n",
       "        1.78416128e-06, 6.25305176e-03, 9.05658683e-03, 7.65379906e-03,\n",
       "        6.23255007e-03, 7.65594025e-03, 8.77353343e-03, 7.65457763e-03,\n",
       "        7.65494751e-03, 5.44447294e-03, 6.24971390e-03, 5.82267532e-03,\n",
       "        7.65646592e-03, 7.65640747e-03, 6.99535127e-03, 1.59997940e-03,\n",
       "        6.99551437e-03, 0.00000000e+00, 2.79750824e-03, 7.65457766e-03,\n",
       "        2.32791671e-03, 6.26847956e-03, 7.65582347e-03, 6.24971390e-03,\n",
       "        6.25219345e-03, 7.02615904e-03, 1.19886398e-03, 7.65757548e-03,\n",
       "        1.19924545e-03, 7.65459715e-03, 3.05063992e-03, 1.48789198e-03,\n",
       "        8.57265025e-03, 6.24980945e-03, 6.99449926e-03, 7.65586277e-03,\n",
       "        1.46859452e-03, 7.20909943e-03, 7.65693308e-03, 6.07405539e-03,\n",
       "        1.87124413e-06, 6.99568194e-03, 5.38062713e-03, 4.89163333e-04,\n",
       "        4.95461220e-03, 3.15043000e-03, 5.24940458e-03, 7.50734533e-03,\n",
       "        1.83111391e-03, 6.17684443e-03, 5.00784975e-03, 5.41853934e-03,\n",
       "        1.99871063e-03, 7.65307877e-03, 7.65644649e-03, 6.06590941e-03,\n",
       "        3.68519722e-03, 9.79375873e-04, 5.86694803e-03, 7.65412985e-03,\n",
       "        6.51571011e-03, 5.09867403e-03, 7.48074514e-04, 1.01282234e-03,\n",
       "        9.74005812e-03, 6.24933243e-03, 1.20725632e-03, 5.05137243e-03,\n",
       "        4.95600834e-03, 7.65523945e-03, 7.65481121e-03, 1.19934082e-03]),\n",
       " 'mean_score_time': array([0.23032818, 0.0917625 , 0.04519601, 0.2045486 , 0.10968423,\n",
       "        0.05624719, 0.15109196, 0.18271213, 0.10289249, 0.20979724,\n",
       "        0.12940531, 0.0187501 , 0.0272727 , 0.03124862, 0.04596138,\n",
       "        0.07187319, 0.18288145, 0.12393064, 0.02544208, 0.22020569,\n",
       "        0.23701811, 0.21042695, 0.2293468 , 0.03571806, 0.01562247,\n",
       "        0.01249828, 0.04062204, 0.22635131, 0.04999681, 0.22612543,\n",
       "        0.05899963, 0.03352847, 0.06565123, 0.26152883, 0.20283694,\n",
       "        0.04062343, 0.01903186, 0.03447714, 0.2379725 , 0.04466834,\n",
       "        0.07499671, 0.09757066, 0.07126389, 0.21861997, 0.06207638,\n",
       "        0.2287972 , 0.06188483, 0.04999986, 0.12844648, 0.06625266,\n",
       "        0.018747  , 0.18841977, 0.19846101, 0.06792951, 0.19436173,\n",
       "        0.04374709, 0.20045404, 0.02499909, 0.06442184, 0.19992585,\n",
       "        0.07758474, 0.03437262, 0.04602175, 0.06249609, 0.21402106,\n",
       "        0.04789238, 0.05221806, 0.03437281, 0.04887786, 0.07621946,\n",
       "        0.07715983, 0.05197039, 0.05985565, 0.01864963, 0.02174239,\n",
       "        0.0755528 , 0.04896984, 0.1353622 , 0.15636659, 0.06518521,\n",
       "        0.09329147, 0.00937457, 0.03124733, 0.18832879, 0.07610407,\n",
       "        0.03398037, 0.20190225, 0.18749299, 0.21280513, 0.06078515,\n",
       "        0.0707572 , 0.12192531, 0.24284511, 0.05937262, 0.23787045,\n",
       "        0.05196915, 0.15904212, 0.01562352, 0.04062276, 0.24092283]),\n",
       " 'std_score_time': array([8.28912066e-03, 2.37797921e-02, 7.80277664e-03, 5.36451222e-03,\n",
       "        6.28250518e-04, 7.65634935e-03, 6.42628849e-03, 5.62615207e-03,\n",
       "        9.40402123e-03, 6.72890459e-03, 6.13344876e-03, 1.16916345e-02,\n",
       "        6.46993552e-03, 3.90424528e-06, 4.53858483e-03, 7.65862704e-03,\n",
       "        1.09158557e-02, 3.81577789e-03, 6.51859567e-03, 1.84651131e-03,\n",
       "        5.71446811e-03, 7.37841373e-03, 7.43947139e-03, 6.15717584e-03,\n",
       "        4.37184528e-06, 6.24914183e-03, 7.65740046e-03, 1.23286999e-02,\n",
       "        6.25112071e-03, 6.82983000e-03, 6.99739463e-03, 1.08806925e-02,\n",
       "        1.16463447e-02, 3.45417196e-02, 3.99023795e-02, 7.65512269e-03,\n",
       "        6.81502843e-03, 4.81577544e-03, 1.85708671e-02, 9.59931255e-03,\n",
       "        6.25371956e-03, 6.05583023e-03, 9.85844061e-03, 1.38901703e-02,\n",
       "        6.44910726e-03, 1.49299281e-02, 3.89476150e-03, 6.24876083e-03,\n",
       "        3.18426894e-03, 6.05788123e-03, 6.24995290e-03, 9.96524669e-03,\n",
       "        2.11879091e-02, 1.01857857e-02, 8.94443984e-03, 6.25259890e-03,\n",
       "        1.14181136e-02, 7.65440255e-03, 4.27345936e-03, 1.30801211e-02,\n",
       "        1.07432594e-03, 6.25059642e-03, 2.92919061e-03, 6.50424062e-06,\n",
       "        2.09281810e-02, 2.03738409e-03, 6.70711183e-03, 6.25002615e-03,\n",
       "        4.01179833e-03, 5.26030263e-03, 1.17092746e-02, 1.09371261e-03,\n",
       "        5.25794613e-03, 2.71038284e-03, 6.51550728e-03, 3.46762416e-02,\n",
       "        2.09748870e-03, 1.10952040e-02, 5.30658084e-03, 3.63990814e-03,\n",
       "        5.23039403e-03, 7.65430505e-03, 1.72849359e-06, 3.35376057e-02,\n",
       "        5.98371584e-03, 3.62945233e-03, 1.07128181e-02, 9.88430502e-03,\n",
       "        9.67312409e-03, 4.93015864e-03, 1.93858722e-03, 1.17249484e-02,\n",
       "        1.44131805e-02, 1.16920445e-02, 2.27177552e-02, 9.41149790e-03,\n",
       "        1.49294102e-02, 3.21432105e-06, 7.65516279e-03, 2.27161748e-02]),\n",
       " 'param_weights': masked_array(data=['distance', 'uniform', 'uniform', 'uniform', 'uniform',\n",
       "                    'distance', 'uniform', 'uniform', 'distance',\n",
       "                    'uniform', 'uniform', 'distance', 'distance',\n",
       "                    'uniform', 'uniform', 'distance', 'uniform',\n",
       "                    'distance', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'distance', 'uniform',\n",
       "                    'distance', 'uniform', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'distance', 'uniform',\n",
       "                    'distance', 'uniform', 'uniform', 'distance',\n",
       "                    'distance', 'distance', 'distance', 'uniform',\n",
       "                    'distance', 'uniform', 'distance', 'distance',\n",
       "                    'uniform', 'uniform', 'uniform', 'distance', 'uniform',\n",
       "                    'uniform', 'distance', 'uniform', 'uniform', 'uniform',\n",
       "                    'uniform', 'distance', 'distance', 'uniform',\n",
       "                    'distance', 'distance', 'distance', 'distance',\n",
       "                    'uniform', 'uniform', 'uniform', 'distance',\n",
       "                    'distance', 'uniform', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'uniform',\n",
       "                    'distance', 'uniform', 'distance', 'uniform',\n",
       "                    'distance', 'distance', 'distance', 'uniform',\n",
       "                    'distance', 'distance', 'uniform', 'uniform',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'distance', 'uniform',\n",
       "                    'uniform', 'uniform', 'distance', 'distance',\n",
       "                    'distance'],\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",
       "                    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",
       "                    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",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_neighbors': masked_array(data=[226, 270, 167, 137, 152, 196, 285, 34, 152, 182, 211,\n",
       "                    34, 64, 123, 34, 78, 20, 196, 78, 211, 285, 152, 255,\n",
       "                    123, 78, 20, 137, 300, 196, 285, 211, 64, 64, 211, 255,\n",
       "                    137, 34, 108, 137, 123, 93, 137, 285, 182, 211, 241,\n",
       "                    211, 196, 182, 255, 64, 78, 93, 78, 64, 152, 123, 78,\n",
       "                    270, 108, 285, 137, 167, 241, 196, 182, 49, 108, 211,\n",
       "                    300, 270, 49, 226, 64, 49, 167, 167, 211, 300, 255,\n",
       "                    108, 20, 108, 34, 255, 108, 167, 78, 152, 241, 241,\n",
       "                    167, 300, 182, 226, 196, 270, 49, 152, 270],\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",
       "                    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",
       "                    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",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_algorithm': masked_array(data=['brute', 'kd_tree', 'kd_tree', 'brute', 'ball_tree',\n",
       "                    'kd_tree', 'ball_tree', 'brute', 'ball_tree', 'brute',\n",
       "                    'ball_tree', 'kd_tree', 'auto', 'auto', 'ball_tree',\n",
       "                    'ball_tree', 'brute', 'ball_tree', 'auto', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'auto', 'kd_tree', 'auto',\n",
       "                    'kd_tree', 'brute', 'auto', 'brute', 'auto', 'kd_tree',\n",
       "                    'ball_tree', 'brute', 'ball_tree', 'auto', 'auto',\n",
       "                    'kd_tree', 'brute', 'kd_tree', 'ball_tree',\n",
       "                    'ball_tree', 'auto', 'brute', 'kd_tree', 'brute',\n",
       "                    'auto', 'kd_tree', 'ball_tree', 'kd_tree', 'auto',\n",
       "                    'brute', 'brute', 'ball_tree', 'brute', 'auto',\n",
       "                    'brute', 'kd_tree', 'auto', 'brute', 'kd_tree', 'auto',\n",
       "                    'kd_tree', 'auto', 'brute', 'auto', 'ball_tree',\n",
       "                    'auto', 'kd_tree', 'kd_tree', 'auto', 'ball_tree',\n",
       "                    'kd_tree', 'kd_tree', 'auto', 'auto', 'auto',\n",
       "                    'ball_tree', 'ball_tree', 'kd_tree', 'ball_tree',\n",
       "                    'kd_tree', 'kd_tree', 'brute', 'auto', 'auto', 'brute',\n",
       "                    'brute', 'brute', 'kd_tree', 'kd_tree', 'ball_tree',\n",
       "                    'brute', 'auto', 'brute', 'auto', 'ball_tree', 'auto',\n",
       "                    'kd_tree', '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",
       "                    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",
       "                    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",
       "                    False, False, False, False],\n",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'weights': 'distance', 'n_neighbors': 226, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 270, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 167, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 137, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 152, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 196, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 285, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 34, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 152, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 182, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 211, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 34, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 64, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 123, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 34, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 78, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 20, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 196, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 78, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 211, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 285, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 152, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 255, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 123, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 78, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 20, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 137, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 300, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 196, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 285, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 211, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 64, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 64, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 211, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 255, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 137, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 34, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 108, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 137, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 123, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 93, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 137, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 285, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 182, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 211, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 241, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 211, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 196, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 182, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 255, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 64, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 78, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 93, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 78, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 64, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 152, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 123, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 78, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 270, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 108, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 285, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 137, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 167, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 241, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 196, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 182, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 49, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 108, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 211, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 300, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 270, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 49, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 226, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 64, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 49, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 167, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 167, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 211, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 300, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 255, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 108, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 20, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 108, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 34, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 255, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 108, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 167, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 78, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 152, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 241, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 241, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 167, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 300, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 182, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 226, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 196, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 270, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 49, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 152, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 270, 'algorithm': 'brute'}],\n",
       " 'split0_test_score': array([0.87667085, 0.88122498, 0.88702774, 0.88873881, 0.88798388,\n",
       "        0.87673289, 0.8804594 , 0.89098911, 0.87661085, 0.88616709,\n",
       "        0.88469493, 0.87146351, 0.87448028, 0.88903043, 0.89098911,\n",
       "        0.87519149, 0.88865098, 0.87673289, 0.87519149, 0.88469493,\n",
       "        0.87653883, 0.88798388, 0.87663803, 0.87625128, 0.89134711,\n",
       "        0.86804688, 0.88873881, 0.87968729, 0.87673289, 0.8804594 ,\n",
       "        0.87675579, 0.87448028, 0.8919632 , 0.87675528, 0.88202873,\n",
       "        0.88873881, 0.87146351, 0.87601683, 0.87644503, 0.87625128,\n",
       "        0.89088366, 0.87644552, 0.8804594 , 0.87669824, 0.87675579,\n",
       "        0.88271721, 0.88469493, 0.8853879 , 0.87669875, 0.88202873,\n",
       "        0.8919632 , 0.87519103, 0.89088366, 0.89134711, 0.8919632 ,\n",
       "        0.88798388, 0.87625079, 0.87519149, 0.88122498, 0.87601635,\n",
       "        0.87653935, 0.87644552, 0.87666972, 0.88271721, 0.8853879 ,\n",
       "        0.88616709, 0.87335656, 0.87601683, 0.88469493, 0.87968729,\n",
       "        0.87659233, 0.89197705, 0.87667137, 0.8919632 , 0.89197705,\n",
       "        0.87666972, 0.88702774, 0.87675579, 0.87968729, 0.87663855,\n",
       "        0.87601683, 0.86804688, 0.88986314, 0.87146309, 0.87663855,\n",
       "        0.88986314, 0.88702774, 0.89134711, 0.87661035, 0.88271721,\n",
       "        0.87664663, 0.88702774, 0.87650731, 0.87669875, 0.88349739,\n",
       "        0.8853879 , 0.88122498, 0.87335656, 0.87661085, 0.87659181]),\n",
       " 'split1_test_score': array([0.87870134, 0.88398732, 0.88919653, 0.89083696, 0.89023872,\n",
       "        0.87878244, 0.8832847 , 0.89446509, 0.87881503, 0.88833208,\n",
       "        0.88677334, 0.87501546, 0.87733606, 0.89147963, 0.89446509,\n",
       "        0.87783547, 0.89310324, 0.87878244, 0.87783547, 0.88677334,\n",
       "        0.87851529, 0.89023872, 0.87861317, 0.87859346, 0.8936209 ,\n",
       "        0.87204726, 0.89083696, 0.8824454 , 0.87878244, 0.8832847 ,\n",
       "        0.87875681, 0.87733606, 0.89398544, 0.87875669, 0.88457881,\n",
       "        0.89083696, 0.87501546, 0.87838089, 0.87870717, 0.87859346,\n",
       "        0.89279047, 0.87870729, 0.8832847 , 0.87880552, 0.87875681,\n",
       "        0.88525789, 0.88677334, 0.88751791, 0.87880565, 0.88457881,\n",
       "        0.89398544, 0.87783536, 0.89279047, 0.8936209 , 0.89398544,\n",
       "        0.89023872, 0.87859334, 0.87783547, 0.88398732, 0.87838077,\n",
       "        0.87851542, 0.87870729, 0.87878155, 0.88525789, 0.88751791,\n",
       "        0.88833208, 0.87665237, 0.87838089, 0.88677334, 0.8824454 ,\n",
       "        0.87857385, 0.89501654, 0.87870147, 0.89398544, 0.89501654,\n",
       "        0.87878155, 0.88919653, 0.87875681, 0.8824454 , 0.8786133 ,\n",
       "        0.87838089, 0.87204726, 0.89190855, 0.87501536, 0.8786133 ,\n",
       "        0.89190855, 0.88919653, 0.8936209 , 0.87881491, 0.88525789,\n",
       "        0.87865194, 0.88919653, 0.87843552, 0.87880565, 0.88593344,\n",
       "        0.88751791, 0.88398732, 0.87665237, 0.87881503, 0.87857372]),\n",
       " 'split2_test_score': array([0.87872198, 0.88055534, 0.88506169, 0.88633167, 0.88567941,\n",
       "        0.87859727, 0.88009289, 0.88941757, 0.87838436, 0.88448586,\n",
       "        0.88310093, 0.87517186, 0.87707059, 0.88666973, 0.88941757,\n",
       "        0.87736821, 0.88825205, 0.87859727, 0.87736821, 0.88310093,\n",
       "        0.87883558, 0.88567941, 0.87877274, 0.87812253, 0.88820913,\n",
       "        0.87263034, 0.88633167, 0.87949267, 0.87859727, 0.88009289,\n",
       "        0.87862361, 0.87707059, 0.8888533 , 0.87862376, 0.88122356,\n",
       "        0.88633167, 0.87517186, 0.87788102, 0.87825605, 0.87812253,\n",
       "        0.88745938, 0.8782559 , 0.88009289, 0.87855285, 0.87862361,\n",
       "        0.8818761 , 0.88310093, 0.88385012, 0.8785527 , 0.88122356,\n",
       "        0.8888533 , 0.87736836, 0.88745938, 0.88820913, 0.8888533 ,\n",
       "        0.88567941, 0.87812269, 0.87736821, 0.88055534, 0.87788118,\n",
       "        0.87883543, 0.8782559 , 0.8784553 , 0.8818761 , 0.88385012,\n",
       "        0.88448586, 0.87641088, 0.87788102, 0.88310093, 0.87949267,\n",
       "        0.87878444, 0.8891956 , 0.87872183, 0.8888533 , 0.8891956 ,\n",
       "        0.8784553 , 0.88506169, 0.87862361, 0.87949267, 0.87877259,\n",
       "        0.87788102, 0.87263034, 0.88708862, 0.87517202, 0.87877259,\n",
       "        0.88708862, 0.88506169, 0.88820913, 0.87838451, 0.8818761 ,\n",
       "        0.8787463 , 0.88506169, 0.87886336, 0.8785527 , 0.88259938,\n",
       "        0.88385012, 0.88055534, 0.87641088, 0.87838436, 0.87878459]),\n",
       " 'split3_test_score': array([0.87930495, 0.88252295, 0.88713827, 0.88833568, 0.8877671 ,\n",
       "        0.87933628, 0.88172325, 0.89100631, 0.87924767, 0.88640816,\n",
       "        0.88542628, 0.87520893, 0.87731549, 0.88889779, 0.89100631,\n",
       "        0.87801272, 0.88915485, 0.87933628, 0.87801272, 0.88542628,\n",
       "        0.87906562, 0.8877671 , 0.87921659, 0.8790104 , 0.89102053,\n",
       "        0.87215237, 0.88833568, 0.88099013, 0.87933628, 0.88172325,\n",
       "        0.87935506, 0.87731549, 0.89079277, 0.87935461, 0.88333913,\n",
       "        0.88833568, 0.87520893, 0.87881381, 0.87915662, 0.8790104 ,\n",
       "        0.89064411, 0.87915707, 0.88172325, 0.87932164, 0.87935506,\n",
       "        0.88381609, 0.88542628, 0.88607704, 0.87932209, 0.88333913,\n",
       "        0.89079277, 0.87801228, 0.89064411, 0.89102053, 0.89079277,\n",
       "        0.8877671 , 0.87900995, 0.87801272, 0.88252295, 0.87881336,\n",
       "        0.87906609, 0.87915707, 0.87933069, 0.88381609, 0.88607704,\n",
       "        0.88640816, 0.87652412, 0.87881381, 0.88542628, 0.88099013,\n",
       "        0.87915408, 0.89108622, 0.8793054 , 0.89079277, 0.89108622,\n",
       "        0.87933069, 0.88713827, 0.87935506, 0.88099013, 0.87921705,\n",
       "        0.87881381, 0.87215237, 0.88984212, 0.8752085 , 0.87921705,\n",
       "        0.88984212, 0.88713827, 0.89102053, 0.87924722, 0.88381609,\n",
       "        0.87925258, 0.88713827, 0.87897592, 0.87932209, 0.88454577,\n",
       "        0.88607704, 0.88252295, 0.87652412, 0.87924767, 0.87915362]),\n",
       " 'split4_test_score': array([0.89067309, 0.89268798, 0.8979259 , 0.89889749, 0.89856607,\n",
       "        0.8905269 , 0.89190585, 0.89949919, 0.89019608, 0.89706412,\n",
       "        0.89566684, 0.88524587, 0.88773321, 0.89930174, 0.89949919,\n",
       "        0.88835626, 0.89728019, 0.8905269 , 0.88835626, 0.89566684,\n",
       "        0.89066129, 0.89856607, 0.89071368, 0.88976107, 0.90008314,\n",
       "        0.88211236, 0.89889749, 0.89113289, 0.8905269 , 0.89190585,\n",
       "        0.89061647, 0.88773321, 0.90045118, 0.89061561, 0.89354533,\n",
       "        0.89889749, 0.88524587, 0.88940097, 0.88998216, 0.88976107,\n",
       "        0.90000561, 0.88998298, 0.89190585, 0.89045997, 0.89061647,\n",
       "        0.89400359, 0.89566684, 0.89640869, 0.89046081, 0.89354533,\n",
       "        0.90045118, 0.88835547, 0.90000561, 0.90008314, 0.90045118,\n",
       "        0.89856607, 0.88976026, 0.88835626, 0.89268798, 0.88940016,\n",
       "        0.89066217, 0.88998298, 0.8903624 , 0.89400359, 0.89640869,\n",
       "        0.89706412, 0.88670243, 0.88940097, 0.89566684, 0.89113289,\n",
       "        0.8906968 , 0.89980635, 0.89067394, 0.90045118, 0.89980635,\n",
       "        0.8903624 , 0.8979259 , 0.89061647, 0.89113289, 0.89071455,\n",
       "        0.88940097, 0.88211236, 0.89968423, 0.88524515, 0.89071455,\n",
       "        0.89968423, 0.8979259 , 0.90008314, 0.89019525, 0.89400359,\n",
       "        0.89067896, 0.8979259 , 0.89066033, 0.89046081, 0.89477701,\n",
       "        0.89640869, 0.89268798, 0.88670243, 0.89019608, 0.89069593]),\n",
       " 'mean_test_score': array([0.88081444, 0.88419571, 0.88927003, 0.89062812, 0.89004704,\n",
       "        0.88079516, 0.88349322, 0.89307546, 0.8806508 , 0.88849146,\n",
       "        0.88713246, 0.87642113, 0.87878713, 0.89107586, 0.89307546,\n",
       "        0.87935283, 0.89128826, 0.88079516, 0.87935283, 0.88713246,\n",
       "        0.88072332, 0.89004704, 0.88079084, 0.88034775, 0.89285616,\n",
       "        0.87339784, 0.89062812, 0.88274967, 0.88079516, 0.88349322,\n",
       "        0.88082155, 0.87878713, 0.89320918, 0.88082119, 0.88494311,\n",
       "        0.89062812, 0.87642113, 0.8800987 , 0.88050941, 0.88034775,\n",
       "        0.89235665, 0.88050975, 0.88349322, 0.88076764, 0.88082155,\n",
       "        0.88553418, 0.88713246, 0.88784833, 0.880768  , 0.88494311,\n",
       "        0.89320918, 0.8793525 , 0.89235665, 0.89285616, 0.89320918,\n",
       "        0.89004704, 0.88034741, 0.87935283, 0.88419571, 0.88009836,\n",
       "        0.88072369, 0.88050975, 0.88071993, 0.88553418, 0.88784833,\n",
       "        0.88849146, 0.87792927, 0.8800987 , 0.88713246, 0.88274967,\n",
       "        0.8807603 , 0.89341635, 0.8808148 , 0.89320918, 0.89341635,\n",
       "        0.88071993, 0.88927003, 0.88082155, 0.88274967, 0.88079121,\n",
       "        0.8800987 , 0.87339784, 0.89167733, 0.87642082, 0.88079121,\n",
       "        0.89167733, 0.88927003, 0.89285616, 0.88065045, 0.88553418,\n",
       "        0.88079528, 0.88927003, 0.88068849, 0.880768  , 0.8862706 ,\n",
       "        0.88784833, 0.88419571, 0.87792927, 0.8806508 , 0.88075994]),\n",
       " 'std_test_score': array([0.00500968, 0.0044054 , 0.00452132, 0.00437517, 0.00449759,\n",
       "        0.00494406, 0.00435235, 0.00361136, 0.00485611, 0.00445654,\n",
       "        0.00442878, 0.00463595, 0.00459998, 0.00438566, 0.00361136,\n",
       "        0.00461336, 0.00346187, 0.00494406, 0.00461336, 0.00442878,\n",
       "        0.00504896, 0.00449759, 0.00503988, 0.00480019, 0.00400139,\n",
       "        0.00465918, 0.00437517, 0.00432327, 0.00494406, 0.00435235,\n",
       "        0.00497424, 0.00459998, 0.00398533, 0.00497401, 0.00445045,\n",
       "        0.00437517, 0.00463595, 0.00474792, 0.0048251 , 0.00480019,\n",
       "        0.00419   , 0.00482532, 0.00435235, 0.00492641, 0.00497424,\n",
       "        0.00438351, 0.00442878, 0.00443995, 0.00492663, 0.00445045,\n",
       "        0.00398533, 0.00461316, 0.00419   , 0.00400139, 0.00398533,\n",
       "        0.00449759, 0.00479998, 0.00461336, 0.0044054 , 0.00474771,\n",
       "        0.00504919, 0.00482532, 0.00490301, 0.00438351, 0.00443995,\n",
       "        0.00445654, 0.00455606, 0.00474792, 0.00442878, 0.00432327,\n",
       "        0.00504719, 0.00370698, 0.00500991, 0.00398533, 0.00370698,\n",
       "        0.00490301, 0.00452132, 0.00497424, 0.00432327, 0.00504011,\n",
       "        0.00474792, 0.00465918, 0.00428671, 0.00463578, 0.00504011,\n",
       "        0.00428671, 0.00452132, 0.00400139, 0.00485589, 0.00438351,\n",
       "        0.00502137, 0.00452132, 0.00506478, 0.00492663, 0.00439582,\n",
       "        0.00443995, 0.0044054 , 0.00455606, 0.00485611, 0.00504696]),\n",
       " 'rank_test_score': array([57, 43, 24, 18, 21, 59, 46,  7, 75, 28, 33, 96, 92, 17,  7, 88, 16,\n",
       "        59, 88, 33, 71, 21, 64, 81,  9, 99, 18, 49, 59, 46, 52, 92,  3, 55,\n",
       "        41, 18, 96, 84, 80, 81, 12, 78, 46, 67, 52, 38, 33, 30, 65, 41,  3,\n",
       "        91, 12,  9,  3, 21, 83, 88, 43, 87, 70, 78, 72, 38, 30, 28, 94, 84,\n",
       "        33, 49, 68,  1, 56,  3,  1, 72, 24, 52, 49, 62, 84, 99, 14, 98, 62,\n",
       "        14, 24,  9, 77, 38, 58, 24, 74, 65, 37, 30, 43, 94, 75, 69])}"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "26840d79",
   "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>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_weights</th>\n",
       "      <th>param_n_neighbors</th>\n",
       "      <th>param_algorithm</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>0.001199</td>\n",
       "      <td>0.002398</td>\n",
       "      <td>0.230328</td>\n",
       "      <td>0.008289</td>\n",
       "      <td>distance</td>\n",
       "      <td>226</td>\n",
       "      <td>brute</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 226, 'a...</td>\n",
       "      <td>0.876671</td>\n",
       "      <td>0.878701</td>\n",
       "      <td>0.878722</td>\n",
       "      <td>0.879305</td>\n",
       "      <td>0.890673</td>\n",
       "      <td>0.880814</td>\n",
       "      <td>0.005010</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003197</td>\n",
       "      <td>0.003967</td>\n",
       "      <td>0.091762</td>\n",
       "      <td>0.023780</td>\n",
       "      <td>uniform</td>\n",
       "      <td>270</td>\n",
       "      <td>kd_tree</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 270, 'al...</td>\n",
       "      <td>0.881225</td>\n",
       "      <td>0.883987</td>\n",
       "      <td>0.880555</td>\n",
       "      <td>0.882523</td>\n",
       "      <td>0.892688</td>\n",
       "      <td>0.884196</td>\n",
       "      <td>0.004405</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007849</td>\n",
       "      <td>0.006989</td>\n",
       "      <td>0.045196</td>\n",
       "      <td>0.007803</td>\n",
       "      <td>uniform</td>\n",
       "      <td>167</td>\n",
       "      <td>kd_tree</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 167, 'al...</td>\n",
       "      <td>0.887028</td>\n",
       "      <td>0.889197</td>\n",
       "      <td>0.885062</td>\n",
       "      <td>0.887138</td>\n",
       "      <td>0.897926</td>\n",
       "      <td>0.889270</td>\n",
       "      <td>0.004521</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.006250</td>\n",
       "      <td>0.007655</td>\n",
       "      <td>0.204549</td>\n",
       "      <td>0.005365</td>\n",
       "      <td>uniform</td>\n",
       "      <td>137</td>\n",
       "      <td>brute</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 137, 'al...</td>\n",
       "      <td>0.888739</td>\n",
       "      <td>0.890837</td>\n",
       "      <td>0.886332</td>\n",
       "      <td>0.888336</td>\n",
       "      <td>0.898897</td>\n",
       "      <td>0.890628</td>\n",
       "      <td>0.004375</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.109684</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>uniform</td>\n",
       "      <td>152</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 152, 'al...</td>\n",
       "      <td>0.887984</td>\n",
       "      <td>0.890239</td>\n",
       "      <td>0.885679</td>\n",
       "      <td>0.887767</td>\n",
       "      <td>0.898566</td>\n",
       "      <td>0.890047</td>\n",
       "      <td>0.004498</td>\n",
       "      <td>21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>0.008320</td>\n",
       "      <td>0.005051</td>\n",
       "      <td>0.051969</td>\n",
       "      <td>0.009411</td>\n",
       "      <td>uniform</td>\n",
       "      <td>196</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 196, 'al...</td>\n",
       "      <td>0.885388</td>\n",
       "      <td>0.887518</td>\n",
       "      <td>0.883850</td>\n",
       "      <td>0.886077</td>\n",
       "      <td>0.896409</td>\n",
       "      <td>0.887848</td>\n",
       "      <td>0.004440</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>0.007723</td>\n",
       "      <td>0.004956</td>\n",
       "      <td>0.159042</td>\n",
       "      <td>0.014929</td>\n",
       "      <td>uniform</td>\n",
       "      <td>270</td>\n",
       "      <td>ball_tree</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 270, 'al...</td>\n",
       "      <td>0.881225</td>\n",
       "      <td>0.883987</td>\n",
       "      <td>0.880555</td>\n",
       "      <td>0.882523</td>\n",
       "      <td>0.892688</td>\n",
       "      <td>0.884196</td>\n",
       "      <td>0.004405</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>0.006250</td>\n",
       "      <td>0.007655</td>\n",
       "      <td>0.015624</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>distance</td>\n",
       "      <td>49</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 49, 'al...</td>\n",
       "      <td>0.873357</td>\n",
       "      <td>0.876652</td>\n",
       "      <td>0.876411</td>\n",
       "      <td>0.876524</td>\n",
       "      <td>0.886702</td>\n",
       "      <td>0.877929</td>\n",
       "      <td>0.004556</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>0.009375</td>\n",
       "      <td>0.007655</td>\n",
       "      <td>0.040623</td>\n",
       "      <td>0.007655</td>\n",
       "      <td>distance</td>\n",
       "      <td>152</td>\n",
       "      <td>kd_tree</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 152, 'a...</td>\n",
       "      <td>0.876611</td>\n",
       "      <td>0.878815</td>\n",
       "      <td>0.878384</td>\n",
       "      <td>0.879248</td>\n",
       "      <td>0.890196</td>\n",
       "      <td>0.880651</td>\n",
       "      <td>0.004856</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0.000600</td>\n",
       "      <td>0.001199</td>\n",
       "      <td>0.240923</td>\n",
       "      <td>0.022716</td>\n",
       "      <td>distance</td>\n",
       "      <td>270</td>\n",
       "      <td>brute</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 270, 'a...</td>\n",
       "      <td>0.876592</td>\n",
       "      <td>0.878574</td>\n",
       "      <td>0.878785</td>\n",
       "      <td>0.879154</td>\n",
       "      <td>0.890696</td>\n",
       "      <td>0.880760</td>\n",
       "      <td>0.005047</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0        0.001199      0.002398         0.230328        0.008289   \n",
       "1        0.003197      0.003967         0.091762        0.023780   \n",
       "2        0.007849      0.006989         0.045196        0.007803   \n",
       "3        0.006250      0.007655         0.204549        0.005365   \n",
       "4        0.000000      0.000000         0.109684        0.000628   \n",
       "..            ...           ...              ...             ...   \n",
       "95       0.008320      0.005051         0.051969        0.009411   \n",
       "96       0.007723      0.004956         0.159042        0.014929   \n",
       "97       0.006250      0.007655         0.015624        0.000003   \n",
       "98       0.009375      0.007655         0.040623        0.007655   \n",
       "99       0.000600      0.001199         0.240923        0.022716   \n",
       "\n",
       "   param_weights param_n_neighbors param_algorithm  \\\n",
       "0       distance               226           brute   \n",
       "1        uniform               270         kd_tree   \n",
       "2        uniform               167         kd_tree   \n",
       "3        uniform               137           brute   \n",
       "4        uniform               152       ball_tree   \n",
       "..           ...               ...             ...   \n",
       "95       uniform               196            auto   \n",
       "96       uniform               270       ball_tree   \n",
       "97      distance                49            auto   \n",
       "98      distance               152         kd_tree   \n",
       "99      distance               270           brute   \n",
       "\n",
       "                                               params  split0_test_score  \\\n",
       "0   {'weights': 'distance', 'n_neighbors': 226, 'a...           0.876671   \n",
       "1   {'weights': 'uniform', 'n_neighbors': 270, 'al...           0.881225   \n",
       "2   {'weights': 'uniform', 'n_neighbors': 167, 'al...           0.887028   \n",
       "3   {'weights': 'uniform', 'n_neighbors': 137, 'al...           0.888739   \n",
       "4   {'weights': 'uniform', 'n_neighbors': 152, 'al...           0.887984   \n",
       "..                                                ...                ...   \n",
       "95  {'weights': 'uniform', 'n_neighbors': 196, 'al...           0.885388   \n",
       "96  {'weights': 'uniform', 'n_neighbors': 270, 'al...           0.881225   \n",
       "97  {'weights': 'distance', 'n_neighbors': 49, 'al...           0.873357   \n",
       "98  {'weights': 'distance', 'n_neighbors': 152, 'a...           0.876611   \n",
       "99  {'weights': 'distance', 'n_neighbors': 270, 'a...           0.876592   \n",
       "\n",
       "    split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0            0.878701           0.878722           0.879305   \n",
       "1            0.883987           0.880555           0.882523   \n",
       "2            0.889197           0.885062           0.887138   \n",
       "3            0.890837           0.886332           0.888336   \n",
       "4            0.890239           0.885679           0.887767   \n",
       "..                ...                ...                ...   \n",
       "95           0.887518           0.883850           0.886077   \n",
       "96           0.883987           0.880555           0.882523   \n",
       "97           0.876652           0.876411           0.876524   \n",
       "98           0.878815           0.878384           0.879248   \n",
       "99           0.878574           0.878785           0.879154   \n",
       "\n",
       "    split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0            0.890673         0.880814        0.005010               57  \n",
       "1            0.892688         0.884196        0.004405               43  \n",
       "2            0.897926         0.889270        0.004521               24  \n",
       "3            0.898897         0.890628        0.004375               18  \n",
       "4            0.898566         0.890047        0.004498               21  \n",
       "..                ...              ...             ...              ...  \n",
       "95           0.896409         0.887848        0.004440               30  \n",
       "96           0.892688         0.884196        0.004405               43  \n",
       "97           0.886702         0.877929        0.004556               94  \n",
       "98           0.890196         0.880651        0.004856               75  \n",
       "99           0.890696         0.880760        0.005047               69  \n",
       "\n",
       "[100 rows x 16 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame(rmcv.cv_results_)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "06d5f2f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'weights': 'uniform', 'n_neighbors': 49, 'algorithm': 'ball_tree'}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b6e37c45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='ball_tree', n_neighbors=49)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "188e27b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8934163528428757"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "bc804027",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='ball_tree', n_neighbors=49)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_reg3 = rmcv.best_estimator_     # model assign with best parameters\n",
    "\n",
    "KNN_reg3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "056ed259",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9035720360482231"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_reg3.score(xtest, ytest)  # KNN best score after tuning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9d43551",
   "metadata": {},
   "source": [
    "# Hyper-parameter Optimization Using GrideSearchCV\n",
    "## For KNN Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3648b44e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "6a47f5d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "gscv = GridSearchCV(KNN_reg2, grids, cv=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "fbb73670",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=3, estimator=KNeighborsRegressor(),\n",
       "             param_grid={'algorithm': ['auto', 'ball_tree', 'kd_tree', 'brute'],\n",
       "                         'n_neighbors': [20, 34, 49, 64, 78, 93, 108, 123, 137,\n",
       "                                         152, 167, 182, 196, 211, 226, 241, 255,\n",
       "                                         270, 285, 300],\n",
       "                         'weights': ['uniform', 'distance']})"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.fit(xtrain ,ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8e36ca3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00799282, 0.00899426, 0.00753927, 0.00520865, 0.        ,\n",
       "        0.00518203, 0.00520929, 0.00754086, 0.        , 0.01562595,\n",
       "        0.00987196, 0.01087077, 0.00520905, 0.00787369, 0.00199819,\n",
       "        0.00520945, 0.0056119 , 0.00520809, 0.        , 0.00787608,\n",
       "        0.        , 0.00753903, 0.00520881, 0.00520889, 0.01041476,\n",
       "        0.01675224, 0.015625  , 0.01274896, 0.0052096 , 0.00199819,\n",
       "        0.        , 0.01041404, 0.01041309, 0.01241167, 0.00520587,\n",
       "        0.0059065 , 0.01041794, 0.00520945, 0.00520658, 0.0052077 ,\n",
       "        0.00520921, 0.0072089 , 0.        , 0.01042   , 0.00720779,\n",
       "        0.01041786, 0.        , 0.00520984, 0.01241708, 0.0019985 ,\n",
       "        0.        , 0.00520849, 0.01142685, 0.        , 0.00199842,\n",
       "        0.00520881, 0.00720731, 0.00521008, 0.00520849, 0.00687504,\n",
       "        0.01208282, 0.00199811, 0.00720827, 0.01041857, 0.01562365,\n",
       "        0.00520817, 0.00720604, 0.        , 0.00520913, 0.0052069 ,\n",
       "        0.00520817, 0.0104154 , 0.00520881, 0.00720493, 0.01041484,\n",
       "        0.        , 0.        , 0.        , 0.00720382, 0.00199858,\n",
       "        0.00954143, 0.00520825, 0.00520706, 0.00520841, 0.        ,\n",
       "        0.        , 0.0072066 , 0.01041786, 0.00520762, 0.01041691,\n",
       "        0.        , 0.01041897, 0.00520937, 0.01257785, 0.        ,\n",
       "        0.        , 0.00233142, 0.00520945, 0.01041667, 0.00520873,\n",
       "        0.00520698, 0.        , 0.01041937, 0.        , 0.00520992,\n",
       "        0.        , 0.        , 0.00233062, 0.00520762, 0.01241318,\n",
       "        0.01241287, 0.0052073 , 0.00520778, 0.01041794, 0.01041023,\n",
       "        0.00520905, 0.00520746, 0.00520603, 0.00753729, 0.01040959,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.01041865,\n",
       "        0.        , 0.        , 0.00520603, 0.        , 0.0062077 ,\n",
       "        0.00520778, 0.        , 0.00520817, 0.00620723, 0.        ,\n",
       "        0.00521032, 0.00099834, 0.        , 0.0052069 , 0.00654093,\n",
       "        0.00520674, 0.00099937, 0.        , 0.00520913, 0.        ,\n",
       "        0.0052069 , 0.        , 0.00620437, 0.00520778, 0.        ,\n",
       "        0.        , 0.0006663 , 0.005874  , 0.        , 0.        ,\n",
       "        0.00620723, 0.01041643, 0.00521016, 0.00620818, 0.00653704]),\n",
       " 'std_fit_time': array([1.41574395e-03, 1.63345890e-03, 6.39089410e-03, 7.36614520e-03,\n",
       "        0.00000000e+00, 3.71894584e-03, 7.36704434e-03, 6.39193901e-03,\n",
       "        0.00000000e+00, 5.15042996e-07, 7.01235427e-03, 3.38630731e-03,\n",
       "        7.36670716e-03, 6.37965665e-03, 2.82586190e-03, 7.36726912e-03,\n",
       "        7.09691847e-03, 7.36535846e-03, 0.00000000e+00, 6.38169820e-03,\n",
       "        0.00000000e+00, 6.38908982e-03, 7.36636999e-03, 7.36648238e-03,\n",
       "        7.36434777e-03, 1.58826199e-03, 8.92080638e-07, 4.06835122e-03,\n",
       "        7.36749390e-03, 2.82586190e-03, 0.00000000e+00, 7.36384136e-03,\n",
       "        7.36316683e-03, 4.53742319e-03, 7.36221150e-03, 6.92438849e-03,\n",
       "        7.36659570e-03, 7.36726912e-03, 7.36322302e-03, 7.36479650e-03,\n",
       "        7.36693194e-03, 6.43930764e-03, 0.00000000e+00, 7.36805586e-03,\n",
       "        6.43797118e-03, 7.36653876e-03, 0.00000000e+00, 7.36783108e-03,\n",
       "        4.54090201e-03, 2.82631147e-03, 0.00000000e+00, 7.36592042e-03,\n",
       "        5.94500976e-03, 0.00000000e+00, 2.82619907e-03, 7.36636999e-03,\n",
       "        6.43639614e-03, 7.36816825e-03, 7.36592042e-03, 6.51739932e-03,\n",
       "        5.01075453e-03, 2.82574951e-03, 6.43740569e-03, 7.36704492e-03,\n",
       "        1.98841031e-06, 7.36547085e-03, 6.43532751e-03, 0.00000000e+00,\n",
       "        7.36681955e-03, 7.36367259e-03, 7.36547085e-03, 7.36479667e-03,\n",
       "        7.36636999e-03, 6.43280246e-03, 7.36440337e-03, 0.00000000e+00,\n",
       "        0.00000000e+00, 0.00000000e+00, 6.43241713e-03, 2.82642386e-03,\n",
       "        4.32558319e-03, 7.36558325e-03, 7.36389737e-03, 7.36580803e-03,\n",
       "        0.00000000e+00, 0.00000000e+00, 6.43605514e-03, 7.36653886e-03,\n",
       "        7.36468411e-03, 7.36586446e-03, 0.00000000e+00, 7.36732542e-03,\n",
       "        7.36715673e-03, 9.27873349e-03, 0.00000000e+00, 0.00000000e+00,\n",
       "        3.29711986e-03, 7.36726912e-03, 7.36569565e-03, 7.36625760e-03,\n",
       "        7.36378498e-03, 0.00000000e+00, 7.36760639e-03, 0.00000000e+00,\n",
       "        7.36794347e-03, 0.00000000e+00, 0.00000000e+00, 3.29599595e-03,\n",
       "        7.36468411e-03, 4.53915939e-03, 4.53910375e-03, 7.36423455e-03,\n",
       "        7.36490890e-03, 7.36659493e-03, 7.36114448e-03, 7.36670716e-03,\n",
       "        7.36445933e-03, 7.36243628e-03, 6.38698534e-03, 7.36069426e-03,\n",
       "        0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,\n",
       "        7.36710055e-03, 0.00000000e+00, 0.00000000e+00, 7.36243628e-03,\n",
       "        0.00000000e+00, 6.77117934e-03, 7.36490890e-03, 0.00000000e+00,\n",
       "        7.36547085e-03, 6.77007144e-03, 0.00000000e+00, 7.36850543e-03,\n",
       "        1.41186323e-03, 0.00000000e+00, 7.36367259e-03, 6.62695038e-03,\n",
       "        7.36344781e-03, 1.41332432e-03, 0.00000000e+00, 7.36681955e-03,\n",
       "        0.00000000e+00, 7.36367259e-03, 0.00000000e+00, 6.76609205e-03,\n",
       "        7.36490890e-03, 0.00000000e+00, 0.00000000e+00, 9.42291141e-04,\n",
       "        6.94209542e-03, 0.00000000e+00, 0.00000000e+00, 6.77066431e-03,\n",
       "        7.36552759e-03, 7.36828064e-03, 6.77036045e-03, 6.62468023e-03]),\n",
       " 'mean_score_time': array([0.01732548, 0.01732334, 0.01831563, 0.02083214, 0.03369188,\n",
       "        0.02640462, 0.03611469, 0.03122425, 0.04166508, 0.03124785,\n",
       "        0.05271037, 0.05574497, 0.04687405, 0.04823867, 0.05310814,\n",
       "        0.05728968, 0.06234304, 0.0617764 , 0.06784995, 0.06721878,\n",
       "        0.07812158, 0.07146064, 0.07812166, 0.07915862, 0.07812111,\n",
       "        0.078173  , 0.08332928, 0.09271661, 0.09601259, 0.10551922,\n",
       "        0.11021972, 0.10115131, 0.10379847, 0.11320972, 0.11543361,\n",
       "        0.11862699, 0.10995984, 0.12622857, 0.13020317, 0.13703497,\n",
       "        0.04845134, 0.0431579 , 0.06249833, 0.06100941, 0.06851562,\n",
       "        0.07291309, 0.09427611, 0.0907491 , 0.0910004 , 0.10821199,\n",
       "        0.1093661 , 0.10952942, 0.12029672, 0.13424293, 0.14108896,\n",
       "        0.14175185, 0.13923232, 0.15176161, 0.1574041 , 0.16062339,\n",
       "        0.16696564, 0.17466974, 0.17638342, 0.17240357, 0.17870355,\n",
       "        0.18246102, 0.19726594, 0.19323301, 0.20033518, 0.20439982,\n",
       "        0.21435404, 0.21470785, 0.22606723, 0.23291278, 0.23533098,\n",
       "        0.2412618 , 0.24112296, 0.24615852, 0.25000024, 0.26491904,\n",
       "        0.00601466, 0.01562381, 0.01562595, 0.0208327 , 0.03124706,\n",
       "        0.03124865, 0.03007396, 0.03124921, 0.03645651, 0.03645976,\n",
       "        0.04759574, 0.04166174, 0.0468727 , 0.04592609, 0.05729079,\n",
       "        0.06249714, 0.06070813, 0.06249539, 0.0624996 , 0.06895192,\n",
       "        0.07291802, 0.0845081 , 0.07291492, 0.08969633, 0.08332729,\n",
       "        0.09501775, 0.09374547, 0.102705  , 0.09922902, 0.09201606,\n",
       "        0.09794609, 0.10416412, 0.11021852, 0.11055319, 0.10937444,\n",
       "        0.12600764, 0.12065697, 0.13268304, 0.12147737, 0.13576706,\n",
       "        0.2628545 , 0.26179481, 0.26729369, 0.26633231, 0.26766109,\n",
       "        0.26724736, 0.27285957, 0.28156352, 0.28355233, 0.27159007,\n",
       "        0.28290677, 0.28313406, 0.28258808, 0.28191717, 0.28874199,\n",
       "        0.287033  , 0.29261812, 0.29912591, 0.29835812, 0.29581316,\n",
       "        0.2999537 , 0.31264011, 0.30908577, 0.30949386, 0.30890497,\n",
       "        0.31484063, 0.31464394, 0.31677063, 0.32012542, 0.32535521,\n",
       "        0.32484786, 0.32859039, 0.32415469, 0.34013764, 0.33673755,\n",
       "        0.32715185, 0.32549175, 0.3453211 , 0.32891901, 0.33846434]),\n",
       " 'std_score_time': array([1.69794559e-03, 2.05174300e-03, 9.65159134e-03, 7.36535877e-03,\n",
       "        3.45519895e-03, 3.73465138e-03, 6.88325471e-03, 3.46459742e-05,\n",
       "        7.36541469e-03, 8.48537942e-07, 1.44716950e-02, 1.07074606e-02,\n",
       "        3.39415177e-06, 1.93089587e-03, 6.75602478e-03, 7.36901130e-03,\n",
       "        2.21195256e-04, 1.01590950e-03, 7.56665193e-03, 7.73083990e-03,\n",
       "        3.93370586e-06, 1.01324968e-02, 3.78978062e-06, 1.47171214e-03,\n",
       "        3.08002192e-06, 7.66928332e-05, 7.36721294e-03, 6.58570238e-03,\n",
       "        3.20720791e-03, 5.44936298e-03, 1.19782293e-03, 6.40604509e-03,\n",
       "        7.12468357e-03, 3.64316980e-03, 8.56463319e-03, 6.69685114e-03,\n",
       "        8.39228409e-04, 1.74387013e-03, 7.36277371e-03, 5.07352785e-03,\n",
       "        2.23889755e-03, 5.24902542e-03, 1.16800773e-06, 1.09923360e-02,\n",
       "        6.86613931e-03, 7.36603579e-03, 7.49427162e-04, 9.33445587e-03,\n",
       "        3.88093807e-03, 4.85514691e-03, 1.89071930e-06, 1.39191473e-03,\n",
       "        7.75320354e-03, 6.78297803e-03, 6.61901619e-04, 8.20633017e-04,\n",
       "        5.89783408e-03, 7.92841372e-03, 1.63720848e-03, 1.27397595e-02,\n",
       "        1.07470627e-02, 1.23540497e-02, 6.09044210e-03, 7.59149042e-04,\n",
       "        6.52499603e-03, 7.49434504e-03, 4.20313288e-03, 7.01928867e-03,\n",
       "        5.74884579e-03, 1.81433763e-03, 1.58888561e-02, 8.31787386e-03,\n",
       "        7.78329134e-03, 1.42276536e-02, 1.28314535e-02, 6.17719722e-03,\n",
       "        9.55958150e-03, 6.60448199e-03, 5.08696801e-03, 4.90899768e-03,\n",
       "        5.87801947e-03, 1.69707588e-06, 3.46049326e-06, 7.36850559e-03,\n",
       "        2.14429499e-06, 7.86741172e-07, 2.18621087e-03, 6.13229806e-06,\n",
       "        7.36715776e-03, 7.37092225e-03, 1.02350807e-03, 7.36592073e-03,\n",
       "        2.09964829e-06, 1.33605638e-03, 7.36878795e-03, 4.49004074e-06,\n",
       "        7.76437679e-03, 4.44053492e-06, 2.78268518e-06, 9.12237645e-03,\n",
       "        7.36985456e-03, 6.69208674e-03, 7.36867402e-03, 5.72820826e-03,\n",
       "        7.36794349e-03, 1.78444223e-03, 4.84328238e-06, 1.45382076e-02,\n",
       "        7.75372865e-03, 2.45216115e-03, 4.74143671e-03, 7.36490902e-03,\n",
       "        1.19363090e-03, 1.67412984e-03, 5.53806667e-06, 1.42973689e-03,\n",
       "        6.13995300e-03, 6.37691605e-03, 4.98294655e-03, 6.56170478e-03,\n",
       "        6.17865809e-03, 4.23039727e-03, 1.28119677e-03, 1.02508475e-03,\n",
       "        1.08880307e-02, 2.30178002e-03, 6.35343298e-03, 1.27604107e-02,\n",
       "        2.40003090e-02, 1.01098763e-02, 1.29683287e-02, 1.35925503e-03,\n",
       "        1.90262185e-03, 9.37704392e-03, 7.99344675e-03, 1.51614643e-02,\n",
       "        7.08279371e-03, 1.24760686e-02, 1.33839409e-02, 1.41963167e-02,\n",
       "        1.06266366e-02, 1.12952811e-02, 8.91843536e-03, 1.32335028e-02,\n",
       "        6.57799739e-03, 1.36910581e-02, 1.45627334e-02, 8.20916915e-03,\n",
       "        5.65441762e-03, 1.89291417e-02, 7.56152089e-03, 2.65780800e-03,\n",
       "        7.56993008e-03, 1.36399655e-02, 8.97269737e-03, 3.06380269e-03,\n",
       "        8.23926956e-03, 1.14983866e-02, 2.07059932e-02, 5.29718970e-03]),\n",
       " 'param_algorithm': masked_array(data=['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', '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",
       "                    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",
       "                    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",
       "                    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",
       "                    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=[20, 20, 34, 34, 49, 49, 64, 64, 78, 78, 93, 93, 108,\n",
       "                    108, 123, 123, 137, 137, 152, 152, 167, 167, 182, 182,\n",
       "                    196, 196, 211, 211, 226, 226, 241, 241, 255, 255, 270,\n",
       "                    270, 285, 285, 300, 300, 20, 20, 34, 34, 49, 49, 64,\n",
       "                    64, 78, 78, 93, 93, 108, 108, 123, 123, 137, 137, 152,\n",
       "                    152, 167, 167, 182, 182, 196, 196, 211, 211, 226, 226,\n",
       "                    241, 241, 255, 255, 270, 270, 285, 285, 300, 300, 20,\n",
       "                    20, 34, 34, 49, 49, 64, 64, 78, 78, 93, 93, 108, 108,\n",
       "                    123, 123, 137, 137, 152, 152, 167, 167, 182, 182, 196,\n",
       "                    196, 211, 211, 226, 226, 241, 241, 255, 255, 270, 270,\n",
       "                    285, 285, 300, 300, 20, 20, 34, 34, 49, 49, 64, 64, 78,\n",
       "                    78, 93, 93, 108, 108, 123, 123, 137, 137, 152, 152,\n",
       "                    167, 167, 182, 182, 196, 196, 211, 211, 226, 226, 241,\n",
       "                    241, 255, 255, 270, 270, 285, 285, 300, 300],\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",
       "                    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",
       "                    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",
       "                    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",
       "                    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_weights': masked_array(data=['uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance'],\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",
       "                    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",
       "                    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",
       "                    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",
       "                    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': 20, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 20, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 34, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 34, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 49, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 49, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 64, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 64, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 78, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 78, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 93, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 93, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 108, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 108, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 123, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 123, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 137, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 137, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 152, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 152, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 167, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 167, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 182, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 182, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 196, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 196, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 211, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 211, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 226, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 226, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 241, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 241, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 255, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 255, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 270, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 270, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 285, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 285, 'weights': 'distance'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 300, 'weights': 'uniform'},\n",
       "  {'algorithm': 'auto', 'n_neighbors': 300, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 20, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 20, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 34, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 34, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 49, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 49, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 64, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 64, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 78, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 78, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 93, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 93, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 108, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 108, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 123, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 123, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 137, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 137, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 152, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 152, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 167, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 167, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 182, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 182, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 196, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 196, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 211, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 211, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 226, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 226, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 241, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 241, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 255, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 255, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 270, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 270, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 285, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 285, 'weights': 'distance'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 300, 'weights': 'uniform'},\n",
       "  {'algorithm': 'ball_tree', 'n_neighbors': 300, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 20, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 20, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 34, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 34, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 49, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 49, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 64, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 64, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 78, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 78, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 93, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 93, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 108, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 108, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 123, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 123, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 137, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 137, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 152, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 152, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 167, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 167, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 182, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 182, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 196, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 196, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 211, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 211, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 226, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 226, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 241, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 241, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 255, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 255, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 270, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 270, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 285, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 285, 'weights': 'distance'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 300, 'weights': 'uniform'},\n",
       "  {'algorithm': 'kd_tree', 'n_neighbors': 300, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 20, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 20, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 34, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 34, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 49, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 49, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 64, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 64, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 78, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 78, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 93, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 93, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 108, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 108, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 123, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 123, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 137, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 137, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 152, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 152, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 167, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 167, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 182, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 182, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 196, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 196, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 211, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 211, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 226, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 226, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 241, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 241, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 255, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 255, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 270, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 270, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 285, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 285, 'weights': 'distance'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 300, 'weights': 'uniform'},\n",
       "  {'algorithm': 'brute', 'n_neighbors': 300, 'weights': 'distance'}],\n",
       " 'split0_test_score': array([0.89185253, 0.87197781, 0.89331552, 0.87495198, 0.89354738,\n",
       "        0.87654064, 0.89261517, 0.87730709, 0.89186515, 0.877786  ,\n",
       "        0.89065411, 0.87800422, 0.88976483, 0.87807133, 0.88904568,\n",
       "        0.87815573, 0.88803401, 0.8781247 , 0.88688233, 0.8780255 ,\n",
       "        0.88575469, 0.8779705 , 0.88463488, 0.87784642, 0.88367349,\n",
       "        0.87772392, 0.88270896, 0.87758542, 0.88179111, 0.87745789,\n",
       "        0.88103826, 0.87734474, 0.87991749, 0.87717102, 0.87880453,\n",
       "        0.87700171, 0.87771109, 0.87682186, 0.87644229, 0.8766098 ,\n",
       "        0.89185253, 0.87197781, 0.89331552, 0.87495198, 0.89354738,\n",
       "        0.87654064, 0.89261517, 0.87730709, 0.89186515, 0.877786  ,\n",
       "        0.89065411, 0.87800422, 0.88976483, 0.87807133, 0.88904568,\n",
       "        0.87815573, 0.88803401, 0.8781247 , 0.88688233, 0.8780255 ,\n",
       "        0.88575469, 0.8779705 , 0.88463488, 0.87784642, 0.88367349,\n",
       "        0.87772392, 0.88270896, 0.87758542, 0.88179111, 0.87745789,\n",
       "        0.88103826, 0.87734474, 0.87991749, 0.87717102, 0.87880453,\n",
       "        0.87700171, 0.87771109, 0.87682186, 0.87644229, 0.8766098 ,\n",
       "        0.89185253, 0.87197781, 0.89331552, 0.87495198, 0.89354738,\n",
       "        0.87654064, 0.89261517, 0.87730709, 0.89186515, 0.877786  ,\n",
       "        0.89065411, 0.87800422, 0.88976483, 0.87807133, 0.88904568,\n",
       "        0.87815573, 0.88803401, 0.8781247 , 0.88688233, 0.8780255 ,\n",
       "        0.88575469, 0.8779705 , 0.88463488, 0.87784642, 0.88367349,\n",
       "        0.87772392, 0.88270896, 0.87758542, 0.88179111, 0.87745789,\n",
       "        0.88103826, 0.87734474, 0.87991749, 0.87717102, 0.87880453,\n",
       "        0.87700171, 0.87771109, 0.87682186, 0.87644229, 0.8766098 ,\n",
       "        0.89185253, 0.87197756, 0.89331552, 0.87495171, 0.89354738,\n",
       "        0.87654036, 0.89261517, 0.87730681, 0.89186515, 0.8777857 ,\n",
       "        0.89065411, 0.87800391, 0.88976483, 0.87807102, 0.88904568,\n",
       "        0.87815541, 0.88803401, 0.87812438, 0.88688233, 0.87802518,\n",
       "        0.88575469, 0.87797018, 0.88463488, 0.8778461 , 0.88367349,\n",
       "        0.8777236 , 0.88270896, 0.8775851 , 0.88179111, 0.87745757,\n",
       "        0.88103826, 0.87734441, 0.87991749, 0.8771707 , 0.87880453,\n",
       "        0.87700139, 0.87771109, 0.87682153, 0.87644229, 0.87660947]),\n",
       " 'split1_test_score': array([0.88905945, 0.87267115, 0.89037982, 0.87510859, 0.89028703,\n",
       "        0.87637693, 0.88986887, 0.87705757, 0.88896188, 0.87752227,\n",
       "        0.88820404, 0.87777456, 0.88751786, 0.87805387, 0.88670079,\n",
       "        0.87819458, 0.88584346, 0.87826594, 0.88482401, 0.87829815,\n",
       "        0.88391574, 0.87834986, 0.88331936, 0.87843793, 0.88241621,\n",
       "        0.87843826, 0.88150253, 0.87843095, 0.88062738, 0.87840357,\n",
       "        0.87994278, 0.87840105, 0.87917199, 0.87840358, 0.87821244,\n",
       "        0.87835952, 0.87723617, 0.87827134, 0.87634607, 0.87820258,\n",
       "        0.88905945, 0.87267115, 0.89037982, 0.87510859, 0.89028703,\n",
       "        0.87637693, 0.88986887, 0.87705757, 0.88896188, 0.87752227,\n",
       "        0.88820404, 0.87777456, 0.88751786, 0.87805387, 0.88670079,\n",
       "        0.87819458, 0.88584346, 0.87826594, 0.88482401, 0.87829815,\n",
       "        0.88391574, 0.87834986, 0.88331936, 0.87843793, 0.88241621,\n",
       "        0.87843826, 0.88150253, 0.87843095, 0.88062738, 0.87840357,\n",
       "        0.87994278, 0.87840105, 0.87917199, 0.87840358, 0.87821244,\n",
       "        0.87835952, 0.87723617, 0.87827134, 0.87634607, 0.87820258,\n",
       "        0.88905945, 0.87267115, 0.89037982, 0.87510859, 0.89028703,\n",
       "        0.87637693, 0.88986887, 0.87705757, 0.88896188, 0.87752227,\n",
       "        0.88820404, 0.87777456, 0.88751786, 0.87805387, 0.88670079,\n",
       "        0.87819458, 0.88584346, 0.87826594, 0.88482401, 0.87829815,\n",
       "        0.88391574, 0.87834986, 0.88331936, 0.87843793, 0.88241621,\n",
       "        0.87843826, 0.88150253, 0.87843095, 0.88062738, 0.87840357,\n",
       "        0.87994278, 0.87840105, 0.87917199, 0.87840358, 0.87821244,\n",
       "        0.87835952, 0.87723617, 0.87827134, 0.87634607, 0.87820258,\n",
       "        0.88905945, 0.87267129, 0.89037982, 0.87510874, 0.89028703,\n",
       "        0.87637708, 0.88986887, 0.87705773, 0.88896188, 0.87752244,\n",
       "        0.88820404, 0.87777473, 0.88751786, 0.87805405, 0.88670079,\n",
       "        0.87819476, 0.88584346, 0.87826612, 0.88482401, 0.87829833,\n",
       "        0.88391574, 0.87835004, 0.88331936, 0.87843811, 0.88241621,\n",
       "        0.87843844, 0.88150253, 0.87843113, 0.88062738, 0.87840375,\n",
       "        0.87994278, 0.87840123, 0.87917199, 0.87840377, 0.87821244,\n",
       "        0.87835971, 0.87723617, 0.87827153, 0.87634607, 0.87820278]),\n",
       " 'split2_test_score': array([0.89495107, 0.87908998, 0.89679685, 0.88193446, 0.89738154,\n",
       "        0.88338623, 0.89694972, 0.88421218, 0.89700792, 0.88484794,\n",
       "        0.89677789, 0.88540226, 0.89595143, 0.8857204 , 0.89560794,\n",
       "        0.88596673, 0.89506653, 0.88612123, 0.89425594, 0.88624124,\n",
       "        0.89342298, 0.88631035, 0.89270805, 0.88638479, 0.89164135,\n",
       "        0.88637272, 0.89116275, 0.88644014, 0.89028604, 0.88642098,\n",
       "        0.88943293, 0.88639055, 0.88853134, 0.88636139, 0.8875846 ,\n",
       "        0.88631193, 0.88660033, 0.88625917, 0.88546272, 0.88618699,\n",
       "        0.89495107, 0.87908998, 0.89679685, 0.88193446, 0.89738154,\n",
       "        0.88338623, 0.89694972, 0.88421218, 0.89700792, 0.88484794,\n",
       "        0.89677789, 0.88540226, 0.89595143, 0.8857204 , 0.89560794,\n",
       "        0.88596673, 0.89506653, 0.88612123, 0.89425594, 0.88624124,\n",
       "        0.89342298, 0.88631035, 0.89270805, 0.88638479, 0.89164135,\n",
       "        0.88637272, 0.89116275, 0.88644014, 0.89028604, 0.88642098,\n",
       "        0.88943293, 0.88639055, 0.88853134, 0.88636139, 0.8875846 ,\n",
       "        0.88631193, 0.88660033, 0.88625917, 0.88546272, 0.88618699,\n",
       "        0.89495107, 0.87908998, 0.89679685, 0.88193446, 0.89738154,\n",
       "        0.88338623, 0.89694972, 0.88421218, 0.89700792, 0.88484794,\n",
       "        0.89677789, 0.88540226, 0.89595143, 0.8857204 , 0.89560794,\n",
       "        0.88596673, 0.89506653, 0.88612123, 0.89425594, 0.88624124,\n",
       "        0.89342298, 0.88631035, 0.89270805, 0.88638479, 0.89164135,\n",
       "        0.88637272, 0.89116275, 0.88644014, 0.89028604, 0.88642098,\n",
       "        0.88943293, 0.88639055, 0.88853134, 0.88636139, 0.8875846 ,\n",
       "        0.88631193, 0.88660033, 0.88625917, 0.88546272, 0.88618699,\n",
       "        0.89495107, 0.8790894 , 0.89679685, 0.88193384, 0.89738154,\n",
       "        0.88338559, 0.89694972, 0.88421153, 0.89700792, 0.88484728,\n",
       "        0.89677789, 0.88540159, 0.89595143, 0.88571973, 0.89560794,\n",
       "        0.88596605, 0.89506653, 0.88612054, 0.89425594, 0.88624055,\n",
       "        0.89342298, 0.88630965, 0.89270805, 0.88638409, 0.89164135,\n",
       "        0.88637201, 0.89116275, 0.88643943, 0.89028604, 0.88642026,\n",
       "        0.88943293, 0.88638982, 0.88853134, 0.88636066, 0.8875846 ,\n",
       "        0.8863112 , 0.88660033, 0.88625844, 0.88546272, 0.88618626]),\n",
       " 'mean_test_score': array([0.89195435, 0.87457964, 0.8934974 , 0.87733168, 0.89373865,\n",
       "        0.87876793, 0.89314459, 0.87952561, 0.89261165, 0.88005207,\n",
       "        0.89187868, 0.88039368, 0.89107804, 0.8806152 , 0.89045147,\n",
       "        0.88077235, 0.889648  , 0.88083729, 0.88865409, 0.88085496,\n",
       "        0.88769781, 0.88087691, 0.88688743, 0.88088972, 0.88591035,\n",
       "        0.88084497, 0.88512475, 0.88081884, 0.88423484, 0.88076081,\n",
       "        0.88347132, 0.88071211, 0.88254027, 0.88064533, 0.88153386,\n",
       "        0.88055772, 0.88051586, 0.88045079, 0.87941703, 0.88033312,\n",
       "        0.89195435, 0.87457964, 0.8934974 , 0.87733168, 0.89373865,\n",
       "        0.87876793, 0.89314459, 0.87952561, 0.89261165, 0.88005207,\n",
       "        0.89187868, 0.88039368, 0.89107804, 0.8806152 , 0.89045147,\n",
       "        0.88077235, 0.889648  , 0.88083729, 0.88865409, 0.88085496,\n",
       "        0.88769781, 0.88087691, 0.88688743, 0.88088972, 0.88591035,\n",
       "        0.88084497, 0.88512475, 0.88081884, 0.88423484, 0.88076081,\n",
       "        0.88347132, 0.88071211, 0.88254027, 0.88064533, 0.88153386,\n",
       "        0.88055772, 0.88051586, 0.88045079, 0.87941703, 0.88033312,\n",
       "        0.89195435, 0.87457964, 0.8934974 , 0.87733168, 0.89373865,\n",
       "        0.87876793, 0.89314459, 0.87952561, 0.89261165, 0.88005207,\n",
       "        0.89187868, 0.88039368, 0.89107804, 0.8806152 , 0.89045147,\n",
       "        0.88077235, 0.889648  , 0.88083729, 0.88865409, 0.88085496,\n",
       "        0.88769781, 0.88087691, 0.88688743, 0.88088972, 0.88591035,\n",
       "        0.88084497, 0.88512475, 0.88081884, 0.88423484, 0.88076081,\n",
       "        0.88347132, 0.88071211, 0.88254027, 0.88064533, 0.88153386,\n",
       "        0.88055772, 0.88051586, 0.88045079, 0.87941703, 0.88033312,\n",
       "        0.89195435, 0.87457942, 0.8934974 , 0.87733143, 0.89373865,\n",
       "        0.87876768, 0.89314459, 0.87952535, 0.89261165, 0.88005181,\n",
       "        0.89187868, 0.88039341, 0.89107804, 0.88061493, 0.89045147,\n",
       "        0.88077208, 0.889648  , 0.88083701, 0.88865409, 0.88085468,\n",
       "        0.88769781, 0.88087663, 0.88688743, 0.88088944, 0.88591035,\n",
       "        0.88084468, 0.88512475, 0.88081855, 0.88423484, 0.88076053,\n",
       "        0.88347132, 0.88071182, 0.88254027, 0.88064504, 0.88153386,\n",
       "        0.88055743, 0.88051586, 0.8804505 , 0.87941703, 0.88033283]),\n",
       " 'std_test_score': array([0.00240632, 0.00320182, 0.0026229 , 0.00325529, 0.00289948,\n",
       "        0.00326631, 0.00291488, 0.00331547, 0.00332692, 0.0033929 ,\n",
       "        0.00360577, 0.00354284, 0.00356601, 0.00360993, 0.00376975,\n",
       "        0.00367302, 0.00393446, 0.00373675, 0.00404926, 0.0038103 ,\n",
       "        0.00411734, 0.00384515, 0.00415069, 0.0038931 , 0.0040848 ,\n",
       "        0.00391958, 0.00429783, 0.00398982, 0.00430514, 0.00402092,\n",
       "        0.00423915, 0.00403835, 0.00424725, 0.00407306, 0.00428534,\n",
       "        0.00410642, 0.00430673, 0.00414955, 0.00427513, 0.00419007,\n",
       "        0.00240632, 0.00320182, 0.0026229 , 0.00325529, 0.00289948,\n",
       "        0.00326631, 0.00291488, 0.00331547, 0.00332692, 0.0033929 ,\n",
       "        0.00360577, 0.00354284, 0.00356601, 0.00360993, 0.00376975,\n",
       "        0.00367302, 0.00393446, 0.00373675, 0.00404926, 0.0038103 ,\n",
       "        0.00411734, 0.00384515, 0.00415069, 0.0038931 , 0.0040848 ,\n",
       "        0.00391958, 0.00429783, 0.00398982, 0.00430514, 0.00402092,\n",
       "        0.00423915, 0.00403835, 0.00424725, 0.00407306, 0.00428534,\n",
       "        0.00410642, 0.00430673, 0.00414955, 0.00427513, 0.00419007,\n",
       "        0.00240632, 0.00320182, 0.0026229 , 0.00325529, 0.00289948,\n",
       "        0.00326631, 0.00291488, 0.00331547, 0.00332692, 0.0033929 ,\n",
       "        0.00360577, 0.00354284, 0.00356601, 0.00360993, 0.00376975,\n",
       "        0.00367302, 0.00393446, 0.00373675, 0.00404926, 0.0038103 ,\n",
       "        0.00411734, 0.00384515, 0.00415069, 0.0038931 , 0.0040848 ,\n",
       "        0.00391958, 0.00429783, 0.00398982, 0.00430514, 0.00402092,\n",
       "        0.00423915, 0.00403835, 0.00424725, 0.00407306, 0.00428534,\n",
       "        0.00410642, 0.00430673, 0.00414955, 0.00427513, 0.00419007,\n",
       "        0.00240632, 0.00320159, 0.0026229 , 0.00325503, 0.00289948,\n",
       "        0.00326604, 0.00291488, 0.00331518, 0.00332692, 0.00339262,\n",
       "        0.00360577, 0.00354255, 0.00356601, 0.00360964, 0.00376975,\n",
       "        0.00367273, 0.00393446, 0.00373647, 0.00404926, 0.00381001,\n",
       "        0.00411734, 0.00384486, 0.00415069, 0.00389282, 0.0040848 ,\n",
       "        0.00391929, 0.00429783, 0.00398954, 0.00430514, 0.00402064,\n",
       "        0.00423915, 0.00403807, 0.00424725, 0.00407278, 0.00428534,\n",
       "        0.00410614, 0.00430673, 0.00414927, 0.00427513, 0.0041898 ]),\n",
       " 'rank_test_score': array([ 17, 157,   5, 153,   1, 149,   9, 141,  13, 137,  21, 129,  25,\n",
       "        113,  29,  97,  33,  89,  37,  81,  41,  77,  45,  73,  49,  85,\n",
       "         53,  93,  57, 101,  61, 105,  65, 109,  69, 117, 121, 125, 145,\n",
       "        133,  17, 157,   5, 153,   1, 149,   9, 141,  13, 137,  21, 129,\n",
       "         25, 113,  29,  97,  33,  89,  37,  81,  41,  77,  45,  73,  49,\n",
       "         85,  53,  93,  57, 101,  61, 105,  65, 109,  69, 117, 121, 125,\n",
       "        145, 133,  17, 157,   5, 153,   1, 149,   9, 141,  13, 137,  21,\n",
       "        129,  25, 113,  29,  97,  33,  89,  37,  81,  41,  77,  45,  73,\n",
       "         49,  85,  53,  93,  57, 101,  61, 105,  65, 109,  69, 117, 121,\n",
       "        125, 145, 133,  17, 160,   5, 156,   1, 152,   9, 144,  13, 140,\n",
       "         21, 132,  25, 116,  29, 100,  33,  92,  37,  84,  41,  80,  45,\n",
       "         76,  49,  88,  53,  96,  57, 104,  61, 108,  65, 112,  69, 120,\n",
       "        121, 128, 145, 136])}"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "3709657d",
   "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>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_algorithm</th>\n",
       "      <th>param_n_neighbors</th>\n",
       "      <th>param_weights</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>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>0.007993</td>\n",
       "      <td>0.001416</td>\n",
       "      <td>0.017325</td>\n",
       "      <td>0.001698</td>\n",
       "      <td>auto</td>\n",
       "      <td>20</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 20, 'weig...</td>\n",
       "      <td>0.891853</td>\n",
       "      <td>0.889059</td>\n",
       "      <td>0.894951</td>\n",
       "      <td>0.891954</td>\n",
       "      <td>0.002406</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.008994</td>\n",
       "      <td>0.001633</td>\n",
       "      <td>0.017323</td>\n",
       "      <td>0.002052</td>\n",
       "      <td>auto</td>\n",
       "      <td>20</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 20, 'weig...</td>\n",
       "      <td>0.871978</td>\n",
       "      <td>0.872671</td>\n",
       "      <td>0.879090</td>\n",
       "      <td>0.874580</td>\n",
       "      <td>0.003202</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007539</td>\n",
       "      <td>0.006391</td>\n",
       "      <td>0.018316</td>\n",
       "      <td>0.009652</td>\n",
       "      <td>auto</td>\n",
       "      <td>34</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 34, 'weig...</td>\n",
       "      <td>0.893316</td>\n",
       "      <td>0.890380</td>\n",
       "      <td>0.896797</td>\n",
       "      <td>0.893497</td>\n",
       "      <td>0.002623</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.005209</td>\n",
       "      <td>0.007366</td>\n",
       "      <td>0.020832</td>\n",
       "      <td>0.007365</td>\n",
       "      <td>auto</td>\n",
       "      <td>34</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 34, 'weig...</td>\n",
       "      <td>0.874952</td>\n",
       "      <td>0.875109</td>\n",
       "      <td>0.881934</td>\n",
       "      <td>0.877332</td>\n",
       "      <td>0.003255</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.033692</td>\n",
       "      <td>0.003455</td>\n",
       "      <td>auto</td>\n",
       "      <td>49</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'algorithm': 'auto', 'n_neighbors': 49, 'weig...</td>\n",
       "      <td>0.893547</td>\n",
       "      <td>0.890287</td>\n",
       "      <td>0.897382</td>\n",
       "      <td>0.893739</td>\n",
       "      <td>0.002899</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0.006207</td>\n",
       "      <td>0.006771</td>\n",
       "      <td>0.327152</td>\n",
       "      <td>0.003064</td>\n",
       "      <td>brute</td>\n",
       "      <td>270</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'algorithm': 'brute', 'n_neighbors': 270, 'we...</td>\n",
       "      <td>0.877001</td>\n",
       "      <td>0.878360</td>\n",
       "      <td>0.886311</td>\n",
       "      <td>0.880557</td>\n",
       "      <td>0.004106</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0.010416</td>\n",
       "      <td>0.007366</td>\n",
       "      <td>0.325492</td>\n",
       "      <td>0.008239</td>\n",
       "      <td>brute</td>\n",
       "      <td>285</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'algorithm': 'brute', 'n_neighbors': 285, 'we...</td>\n",
       "      <td>0.877711</td>\n",
       "      <td>0.877236</td>\n",
       "      <td>0.886600</td>\n",
       "      <td>0.880516</td>\n",
       "      <td>0.004307</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.005210</td>\n",
       "      <td>0.007368</td>\n",
       "      <td>0.345321</td>\n",
       "      <td>0.011498</td>\n",
       "      <td>brute</td>\n",
       "      <td>285</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'algorithm': 'brute', 'n_neighbors': 285, 'we...</td>\n",
       "      <td>0.876822</td>\n",
       "      <td>0.878272</td>\n",
       "      <td>0.886258</td>\n",
       "      <td>0.880451</td>\n",
       "      <td>0.004149</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.006208</td>\n",
       "      <td>0.006770</td>\n",
       "      <td>0.328919</td>\n",
       "      <td>0.020706</td>\n",
       "      <td>brute</td>\n",
       "      <td>300</td>\n",
       "      <td>uniform</td>\n",
       "      <td>{'algorithm': 'brute', 'n_neighbors': 300, 'we...</td>\n",
       "      <td>0.876442</td>\n",
       "      <td>0.876346</td>\n",
       "      <td>0.885463</td>\n",
       "      <td>0.879417</td>\n",
       "      <td>0.004275</td>\n",
       "      <td>145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.006537</td>\n",
       "      <td>0.006625</td>\n",
       "      <td>0.338464</td>\n",
       "      <td>0.005297</td>\n",
       "      <td>brute</td>\n",
       "      <td>300</td>\n",
       "      <td>distance</td>\n",
       "      <td>{'algorithm': 'brute', 'n_neighbors': 300, 'we...</td>\n",
       "      <td>0.876609</td>\n",
       "      <td>0.878203</td>\n",
       "      <td>0.886186</td>\n",
       "      <td>0.880333</td>\n",
       "      <td>0.004190</td>\n",
       "      <td>136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0         0.007993      0.001416         0.017325        0.001698   \n",
       "1         0.008994      0.001633         0.017323        0.002052   \n",
       "2         0.007539      0.006391         0.018316        0.009652   \n",
       "3         0.005209      0.007366         0.020832        0.007365   \n",
       "4         0.000000      0.000000         0.033692        0.003455   \n",
       "..             ...           ...              ...             ...   \n",
       "155       0.006207      0.006771         0.327152        0.003064   \n",
       "156       0.010416      0.007366         0.325492        0.008239   \n",
       "157       0.005210      0.007368         0.345321        0.011498   \n",
       "158       0.006208      0.006770         0.328919        0.020706   \n",
       "159       0.006537      0.006625         0.338464        0.005297   \n",
       "\n",
       "    param_algorithm param_n_neighbors param_weights  \\\n",
       "0              auto                20       uniform   \n",
       "1              auto                20      distance   \n",
       "2              auto                34       uniform   \n",
       "3              auto                34      distance   \n",
       "4              auto                49       uniform   \n",
       "..              ...               ...           ...   \n",
       "155           brute               270      distance   \n",
       "156           brute               285       uniform   \n",
       "157           brute               285      distance   \n",
       "158           brute               300       uniform   \n",
       "159           brute               300      distance   \n",
       "\n",
       "                                                params  split0_test_score  \\\n",
       "0    {'algorithm': 'auto', 'n_neighbors': 20, 'weig...           0.891853   \n",
       "1    {'algorithm': 'auto', 'n_neighbors': 20, 'weig...           0.871978   \n",
       "2    {'algorithm': 'auto', 'n_neighbors': 34, 'weig...           0.893316   \n",
       "3    {'algorithm': 'auto', 'n_neighbors': 34, 'weig...           0.874952   \n",
       "4    {'algorithm': 'auto', 'n_neighbors': 49, 'weig...           0.893547   \n",
       "..                                                 ...                ...   \n",
       "155  {'algorithm': 'brute', 'n_neighbors': 270, 'we...           0.877001   \n",
       "156  {'algorithm': 'brute', 'n_neighbors': 285, 'we...           0.877711   \n",
       "157  {'algorithm': 'brute', 'n_neighbors': 285, 'we...           0.876822   \n",
       "158  {'algorithm': 'brute', 'n_neighbors': 300, 'we...           0.876442   \n",
       "159  {'algorithm': 'brute', 'n_neighbors': 300, 'we...           0.876609   \n",
       "\n",
       "     split1_test_score  split2_test_score  mean_test_score  std_test_score  \\\n",
       "0             0.889059           0.894951         0.891954        0.002406   \n",
       "1             0.872671           0.879090         0.874580        0.003202   \n",
       "2             0.890380           0.896797         0.893497        0.002623   \n",
       "3             0.875109           0.881934         0.877332        0.003255   \n",
       "4             0.890287           0.897382         0.893739        0.002899   \n",
       "..                 ...                ...              ...             ...   \n",
       "155           0.878360           0.886311         0.880557        0.004106   \n",
       "156           0.877236           0.886600         0.880516        0.004307   \n",
       "157           0.878272           0.886258         0.880451        0.004149   \n",
       "158           0.876346           0.885463         0.879417        0.004275   \n",
       "159           0.878203           0.886186         0.880333        0.004190   \n",
       "\n",
       "     rank_test_score  \n",
       "0                 17  \n",
       "1                157  \n",
       "2                  5  \n",
       "3                153  \n",
       "4                  1  \n",
       "..               ...  \n",
       "155              120  \n",
       "156              121  \n",
       "157              128  \n",
       "158              145  \n",
       "159              136  \n",
       "\n",
       "[160 rows x 14 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results2 = pd.DataFrame(gscv.cv_results_)\n",
    "results2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "282bb97c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=49)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "9adf9d70",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.893738653645841"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gscv.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "211a97a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(n_neighbors=49)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_reg4 = gscv.best_estimator_  # model assign with best parameters\n",
    "KNN_reg4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "07bd03e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9035720360482231"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_reg4.score(xtest,ytest)       # best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c82a478",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5f0dd830",
   "metadata": {},
   "source": [
    "# KNN classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "12bf2615",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Male</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Male</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Male</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Male</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Male</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Gender     Height      Weight\n",
       "0   Male  73.847017  241.893563\n",
       "1   Male  68.781904  162.310473\n",
       "2   Male  74.110105  212.740856\n",
       "3   Male  71.730978  220.042470\n",
       "4   Male  69.881796  206.349801"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "df = pd.read_csv('weight-height.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "241f7641",
   "metadata": {},
   "outputs": [],
   "source": [
    "# preprocessing\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "label = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "06d75b93",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.Gender = label.fit_transform(df.Gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "62152750",
   "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>Gender</th>\n",
       "      <th>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Gender     Height      Weight\n",
       "0       1  73.847017  241.893563\n",
       "1       1  68.781904  162.310473\n",
       "2       1  74.110105  212.740856\n",
       "3       1  71.730978  220.042470\n",
       "4       1  69.881796  206.349801"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b0bf2c73",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into x, y\n",
    "\n",
    "x = df.drop('Gender', axis=1)\n",
    "y = df['Gender']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "5cef1f07",
   "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>Height</th>\n",
       "      <th>Weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>73.847017</td>\n",
       "      <td>241.893563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>68.781904</td>\n",
       "      <td>162.310473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>74.110105</td>\n",
       "      <td>212.740856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>71.730978</td>\n",
       "      <td>220.042470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>69.881796</td>\n",
       "      <td>206.349801</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Height      Weight\n",
       "0  73.847017  241.893563\n",
       "1  68.781904  162.310473\n",
       "2  74.110105  212.740856\n",
       "3  71.730978  220.042470\n",
       "4  69.881796  206.349801"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "f6b7b1d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    1\n",
       "2    1\n",
       "3    1\n",
       "4    1\n",
       "Name: Gender, dtype: int32"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "0101ae85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8555, 2)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "94299994",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data into testing and training set\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "xtrain, xtest, ytrain, ytest = train_test_split(x, y, test_size=0.30, random_state=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d17bd324",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5988, 2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xtrain.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8ad9cfbe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# KNN classification\n",
    "\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "KNN_clf = KNeighborsClassifier()\n",
    "\n",
    "KNN_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "b7d92179",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier()"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_clf.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "84ff9258",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 0, 1, 1])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy\n",
    "\n",
    "pred_clf = KNN_clf.predict(xtest)\n",
    "pred_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "eaa4506d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9146864043630697"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_clf.score(xtest, ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4032273",
   "metadata": {},
   "source": [
    "## Hyper-parameter Optimization Using RandomSearchCV\n",
    "### For KNN classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "7387a76c",
   "metadata": {},
   "outputs": [],
   "source": [
    "KNN_clf2 = KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "e0c273e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "rmcv_clf = RandomizedSearchCV(KNN_clf2, grids, n_iter=200, cv=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "c1e60ba6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\sabbi\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\model_selection\\_search.py:292: UserWarning: The total space of parameters 160 is smaller than n_iter=200. Running 160 iterations. For exhaustive searches, use GridSearchCV.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=5, estimator=KNeighborsClassifier(), n_iter=200,\n",
       "                   param_distributions={'algorithm': ['auto', 'ball_tree',\n",
       "                                                      'kd_tree', 'brute'],\n",
       "                                        'n_neighbors': [20, 34, 49, 64, 78, 93,\n",
       "                                                        108, 123, 137, 152, 167,\n",
       "                                                        182, 196, 211, 226, 241,\n",
       "                                                        255, 270, 285, 300],\n",
       "                                        'weights': ['uniform', 'distance']})"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv_clf.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "5385ad9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([0.00672317, 0.00312595, 0.00764804, 0.00312653, 0.00460558,\n",
       "        0.01217318, 0.        , 0.00139899, 0.00765052, 0.00625196,\n",
       "        0.00312586, 0.00312543, 0.0107759 , 0.00506139, 0.01250072,\n",
       "        0.00625076, 0.00764909, 0.00924973, 0.00745072, 0.00625043,\n",
       "        0.00764971, 0.00937529, 0.01417785, 0.01097484, 0.00119877,\n",
       "        0.0076488 , 0.00452466, 0.00452337, 0.0093781 , 0.00764871,\n",
       "        0.01552272, 0.00784683, 0.01390171, 0.01097445, 0.00625114,\n",
       "        0.00312386, 0.00432343, 0.00452414, 0.00764937, 0.00452948,\n",
       "        0.00432463, 0.00625162, 0.00625033, 0.01057587, 0.01571603,\n",
       "        0.00552459, 0.00432448, 0.00765028, 0.00432458, 0.00119901,\n",
       "        0.00745106, 0.00119834, 0.01562614, 0.00432506, 0.00380154,\n",
       "        0.00625076, 0.00984368, 0.01197481, 0.00119901, 0.00432401,\n",
       "        0.00432377, 0.00312548, 0.01461229, 0.00624719, 0.008847  ,\n",
       "        0.00937486, 0.00686517, 0.00312514, 0.01250296, 0.00744877,\n",
       "        0.00312638, 0.015624  , 0.00744977, 0.00744834, 0.00625029,\n",
       "        0.00060949, 0.0006443 , 0.00937452, 0.00937543, 0.00432386,\n",
       "        0.00353169, 0.00937581, 0.00744982, 0.00624743, 0.00432487,\n",
       "        0.0062511 , 0.01097341, 0.01250257, 0.00452571, 0.00620198,\n",
       "        0.00452514, 0.00937352, 0.00625167, 0.01250114, 0.00452442,\n",
       "        0.00319757, 0.00625114, 0.00937653, 0.00765057, 0.00624919,\n",
       "        0.00312495, 0.00904794, 0.0107739 , 0.00624857, 0.00625191,\n",
       "        0.00624886, 0.00888772, 0.00624914, 0.00904865, 0.0125    ,\n",
       "        0.00592346, 0.00312424, 0.01249924, 0.00765076, 0.00312529,\n",
       "        0.0076499 , 0.00686364, 0.00937338, 0.01077461, 0.0107739 ,\n",
       "        0.        , 0.00432496, 0.        , 0.00080862, 0.        ,\n",
       "        0.        , 0.00099936, 0.00039973, 0.        , 0.00684915,\n",
       "        0.00059972, 0.00372534, 0.00625134, 0.00372438, 0.        ,\n",
       "        0.0068615 , 0.        , 0.00080972, 0.00745082, 0.00312533,\n",
       "        0.00080886, 0.00059972, 0.        , 0.00039968, 0.00059967,\n",
       "        0.00684767, 0.00625129, 0.        , 0.00685053, 0.00312462,\n",
       "        0.00059958, 0.00039983, 0.00059962, 0.00099974, 0.00459909,\n",
       "        0.00554333, 0.00492411, 0.00452294, 0.00179811, 0.00319695]),\n",
       " 'std_fit_time': array([6.03275443e-03, 6.25190735e-03, 6.99415537e-03, 6.25305176e-03,\n",
       "        3.82921571e-03, 4.23013592e-03, 0.00000000e+00, 2.79798508e-03,\n",
       "        6.99645288e-03, 7.65704992e-03, 6.25171661e-03, 6.25085831e-03,\n",
       "        6.34119020e-03, 1.01227760e-02, 6.25035810e-03, 7.65559019e-03,\n",
       "        6.99611623e-03, 5.89302328e-03, 7.02716399e-03, 7.65518118e-03,\n",
       "        6.99646988e-03, 7.65488951e-03, 7.79605965e-03, 6.23303602e-03,\n",
       "        2.39753723e-03, 6.99526087e-03, 6.17736761e-03, 6.17518689e-03,\n",
       "        7.65718613e-03, 6.99497513e-03, 2.08980814e-04, 6.98622660e-03,\n",
       "        3.45230116e-03, 6.23403602e-03, 7.65605731e-03, 6.24771118e-03,\n",
       "        6.10795907e-03, 6.17662474e-03, 6.99603047e-03, 6.17843803e-03,\n",
       "        6.10971333e-03, 7.65664115e-03, 7.65506433e-03, 6.47131806e-03,\n",
       "        1.75869374e-04, 5.71994057e-03, 6.10967419e-03, 6.99688755e-03,\n",
       "        6.11022651e-03, 2.39801407e-03, 7.02742179e-03, 2.39667892e-03,\n",
       "        1.36878015e-06, 6.11095829e-03, 4.11667215e-03, 7.65558989e-03,\n",
       "        8.08243874e-03, 4.48498806e-03, 2.39801407e-03, 6.10901759e-03,\n",
       "        6.10820074e-03, 6.25095367e-03, 4.65597580e-03, 7.65121039e-03,\n",
       "        6.02905637e-03, 7.65453958e-03, 7.24200348e-03, 6.25028610e-03,\n",
       "        6.25147838e-03, 7.02515019e-03, 6.25276566e-03, 3.10274095e-06,\n",
       "        7.02631573e-03, 7.02438969e-03, 7.65500590e-03, 1.21898651e-03,\n",
       "        1.28860474e-03, 7.65426611e-03, 7.65500596e-03, 6.10867779e-03,\n",
       "        6.09698197e-03, 7.65531752e-03, 7.02624030e-03, 7.65150316e-03,\n",
       "        6.11030472e-03, 7.65599882e-03, 6.23255192e-03, 6.25128750e-03,\n",
       "        6.17858682e-03, 5.95308064e-03, 6.17842460e-03, 7.65344956e-03,\n",
       "        7.65669949e-03, 6.25057338e-03, 6.17740554e-03, 4.11511912e-03,\n",
       "        7.65605706e-03, 7.65590147e-03, 6.99680136e-03, 7.65366266e-03,\n",
       "        6.24990463e-03, 5.94689059e-03, 6.33978769e-03, 7.65290361e-03,\n",
       "        7.65699152e-03, 7.65325385e-03, 6.01230229e-03, 7.65360425e-03,\n",
       "        5.94761232e-03, 6.25000020e-03, 5.77284868e-03, 6.24847412e-03,\n",
       "        6.24961908e-03, 6.99672475e-03, 6.25057220e-03, 6.99639335e-03,\n",
       "        7.24114939e-03, 7.65333196e-03, 6.34020501e-03, 6.33952789e-03,\n",
       "        0.00000000e+00, 5.80784240e-03, 0.00000000e+00, 1.61724091e-03,\n",
       "        0.00000000e+00, 0.00000000e+00, 1.26410314e-03, 7.99465179e-04,\n",
       "        0.00000000e+00, 7.24780634e-03, 1.19943619e-03, 6.06383322e-03,\n",
       "        7.65629075e-03, 6.06196118e-03, 0.00000000e+00, 7.24426223e-03,\n",
       "        0.00000000e+00, 1.16706939e-03, 6.76557110e-03, 6.25066757e-03,\n",
       "        1.16729148e-03, 1.19943619e-03, 0.00000000e+00, 7.99369812e-04,\n",
       "        1.19934082e-03, 7.24609977e-03, 7.65623227e-03, 0.00000000e+00,\n",
       "        7.24989587e-03, 6.24923706e-03, 1.19915009e-03, 7.99655914e-04,\n",
       "        1.19924545e-03, 1.99947357e-03, 6.34265902e-03, 5.15320797e-03,\n",
       "        5.47524883e-03, 5.77584703e-03, 1.46814744e-03, 3.99590250e-04]),\n",
       " 'mean_score_time': array([0.0882514 , 0.01250005, 0.08000979, 0.01874743, 0.098982  ,\n",
       "        0.01591396, 0.09487891, 0.02315731, 0.09319353, 0.02812262,\n",
       "        0.09432549, 0.03437243, 0.09599376, 0.03869939, 0.09758968,\n",
       "        0.04062243, 0.1027585 , 0.04442015, 0.1086555 , 0.05214014,\n",
       "        0.1099761 , 0.05312304, 0.10779843, 0.05238581, 0.12688794,\n",
       "        0.05883031, 0.1242981 , 0.07064753, 0.12537923, 0.07120318,\n",
       "        0.12564836, 0.0740067 , 0.1274282 , 0.07701211, 0.14083595,\n",
       "        0.08859472, 0.14331727, 0.08980999, 0.14602814, 0.09577518,\n",
       "        0.09983096, 0.03437147, 0.10836477, 0.03512421, 0.10959616,\n",
       "        0.05468302, 0.12450333, 0.06484532, 0.12983246, 0.07455864,\n",
       "        0.13973231, 0.08401217, 0.14090743, 0.09306045, 0.16948667,\n",
       "        0.09368339, 0.15987377, 0.10130372, 0.16815591, 0.11221809,\n",
       "        0.17801108, 0.11626134, 0.17416477, 0.12204309, 0.18652296,\n",
       "        0.12517095, 0.1938036 , 0.14188638, 0.19169464, 0.14369202,\n",
       "        0.20750775, 0.14054422, 0.21016626, 0.15632076, 0.21627555,\n",
       "        0.16808114, 0.22319317, 0.16337652, 0.22654057, 0.17464852,\n",
       "        0.07802339, 0.00937433, 0.07755585, 0.01562772, 0.08678942,\n",
       "        0.01874847, 0.08012233, 0.02081175, 0.08747444, 0.028897  ,\n",
       "        0.09606762, 0.03125072, 0.09704933, 0.03544297, 0.10396819,\n",
       "        0.04447031, 0.10324841, 0.04562764, 0.10718603, 0.04687376,\n",
       "        0.11945133, 0.05400209, 0.11186371, 0.06249514, 0.12305927,\n",
       "        0.06425624, 0.11852117, 0.06909976, 0.12285438, 0.06711125,\n",
       "        0.13425517, 0.07829785, 0.13250742, 0.07983828, 0.13836136,\n",
       "        0.08390274, 0.14311442, 0.08451467, 0.14367871, 0.08980594,\n",
       "        0.25457497, 0.18090687, 0.24813418, 0.18444624, 0.24823279,\n",
       "        0.18874393, 0.25458241, 0.18948154, 0.25798783, 0.18835783,\n",
       "        0.25621619, 0.19444451, 0.25821018, 0.19686189, 0.26760221,\n",
       "        0.20000324, 0.28410659, 0.20535688, 0.26288314, 0.21344261,\n",
       "        0.27184248, 0.21713386, 0.27903371, 0.21959543, 0.28176708,\n",
       "        0.21368957, 0.27663488, 0.22564254, 0.27967696, 0.22641068,\n",
       "        0.28841991, 0.23786879, 0.28572984, 0.24428401, 0.2907299 ,\n",
       "        0.28825312, 0.31284666, 0.25077448, 0.3267004 , 0.25724134]),\n",
       " 'std_score_time': array([1.12367513e-02, 6.25002457e-03, 3.06024524e-03, 6.25009692e-03,\n",
       "        2.20635775e-02, 4.33083324e-03, 2.26736189e-03, 7.01067214e-03,\n",
       "        1.71318658e-03, 6.25119308e-03, 1.16207903e-03, 6.24687673e-03,\n",
       "        8.45366745e-03, 7.02587774e-03, 6.05485414e-03, 7.65819859e-03,\n",
       "        9.03156175e-03, 5.79286272e-03, 3.91288941e-03, 6.63426675e-03,\n",
       "        2.68497664e-03, 7.65177461e-03, 3.14049780e-03, 7.16123319e-03,\n",
       "        8.41194481e-03, 6.05464614e-03, 1.00371129e-03, 6.76867296e-03,\n",
       "        7.77174262e-04, 7.79349630e-03, 1.43265121e-03, 5.92132966e-03,\n",
       "        8.12350827e-03, 3.53346385e-03, 2.73495881e-03, 6.54381225e-03,\n",
       "        7.56994408e-03, 6.99001726e-03, 1.12326297e-02, 4.04987651e-03,\n",
       "        9.50047278e-03, 6.25247978e-03, 8.03644553e-03, 6.05368570e-03,\n",
       "        4.55192373e-04, 6.29255936e-03, 4.18466033e-03, 4.00687588e-03,\n",
       "        9.42568876e-03, 9.06580547e-03, 2.76348322e-03, 8.45600182e-03,\n",
       "        5.80240988e-04, 2.35945953e-03, 1.07381413e-02, 1.25512979e-04,\n",
       "        6.07674395e-03, 6.62124585e-03, 6.50453924e-03, 6.86636549e-03,\n",
       "        6.43118861e-03, 8.49749459e-03, 6.28207528e-03, 6.34323190e-03,\n",
       "        2.85243505e-03, 3.53583064e-04, 7.53146086e-03, 2.52883810e-03,\n",
       "        6.63633531e-03, 7.70943763e-03, 5.84605098e-03, 1.54413272e-04,\n",
       "        9.16048922e-03, 1.21298641e-02, 5.70907537e-03, 6.08442562e-03,\n",
       "        5.99913021e-03, 7.11988416e-03, 9.05243245e-03, 4.43875540e-03,\n",
       "        1.97158263e-04, 7.65411042e-03, 5.99835227e-03, 6.81493558e-06,\n",
       "        8.90516027e-03, 6.25135932e-03, 7.32119438e-03, 6.57290886e-03,\n",
       "        7.68184469e-03, 2.32384434e-03, 7.82754115e-03, 2.45929660e-06,\n",
       "        6.17111612e-03, 8.38892475e-03, 6.35446466e-03, 2.96655398e-03,\n",
       "        7.76333621e-03, 8.06162265e-03, 5.18127736e-03, 3.57913857e-06,\n",
       "        1.20395579e-02, 5.34354904e-03, 4.77471884e-03, 9.88099593e-03,\n",
       "        4.99950779e-03, 4.18765634e-03, 9.35988173e-03, 8.10299316e-03,\n",
       "        5.38389967e-03, 9.22584572e-03, 6.45225081e-03, 3.49308557e-04,\n",
       "        1.21178217e-02, 8.35574417e-03, 6.89099792e-03, 9.91370706e-03,\n",
       "        4.99017272e-03, 7.83189914e-03, 4.47165062e-03, 6.75873818e-03,\n",
       "        2.39649545e-02, 5.53436946e-03, 5.59680410e-03, 6.70242801e-03,\n",
       "        4.62969558e-03, 1.55162309e-03, 5.69681756e-03, 8.48707968e-03,\n",
       "        6.38149426e-03, 5.18371353e-03, 1.09935717e-02, 7.88755488e-03,\n",
       "        6.30725392e-03, 1.38402144e-02, 1.84178430e-03, 1.15929172e-02,\n",
       "        2.52964095e-02, 8.45739275e-03, 7.37555710e-03, 6.97072870e-03,\n",
       "        6.45013692e-03, 5.15315933e-03, 6.84611233e-03, 1.36544306e-02,\n",
       "        1.12389997e-02, 7.59654416e-03, 1.14783847e-02, 7.17266661e-03,\n",
       "        1.02953790e-02, 6.85975637e-03, 1.49222669e-02, 7.55217204e-03,\n",
       "        9.36553304e-03, 1.42180295e-02, 5.98249318e-03, 5.36403353e-02,\n",
       "        9.32379597e-03, 1.29288964e-02, 1.88125521e-02, 4.21469220e-03]),\n",
       " 'param_weights': masked_array(data=['uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance',\n",
       "                    'uniform', 'distance', 'uniform', 'distance'],\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",
       "                    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",
       "                    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",
       "                    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",
       "                    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=[20, 20, 34, 34, 49, 49, 64, 64, 78, 78, 93, 93, 108,\n",
       "                    108, 123, 123, 137, 137, 152, 152, 167, 167, 182, 182,\n",
       "                    196, 196, 211, 211, 226, 226, 241, 241, 255, 255, 270,\n",
       "                    270, 285, 285, 300, 300, 20, 20, 34, 34, 49, 49, 64,\n",
       "                    64, 78, 78, 93, 93, 108, 108, 123, 123, 137, 137, 152,\n",
       "                    152, 167, 167, 182, 182, 196, 196, 211, 211, 226, 226,\n",
       "                    241, 241, 255, 255, 270, 270, 285, 285, 300, 300, 20,\n",
       "                    20, 34, 34, 49, 49, 64, 64, 78, 78, 93, 93, 108, 108,\n",
       "                    123, 123, 137, 137, 152, 152, 167, 167, 182, 182, 196,\n",
       "                    196, 211, 211, 226, 226, 241, 241, 255, 255, 270, 270,\n",
       "                    285, 285, 300, 300, 20, 20, 34, 34, 49, 49, 64, 64, 78,\n",
       "                    78, 93, 93, 108, 108, 123, 123, 137, 137, 152, 152,\n",
       "                    167, 167, 182, 182, 196, 196, 211, 211, 226, 226, 241,\n",
       "                    241, 255, 255, 270, 270, 285, 285, 300, 300],\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",
       "                    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",
       "                    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",
       "                    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",
       "                    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_algorithm': masked_array(data=['auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'auto', 'auto',\n",
       "                    'auto', 'auto', 'auto', 'auto', 'auto', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'ball_tree',\n",
       "                    'ball_tree', 'ball_tree', 'ball_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree',\n",
       "                    'kd_tree', 'kd_tree', 'kd_tree', 'kd_tree', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', 'brute', 'brute', 'brute', 'brute', 'brute',\n",
       "                    'brute', '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",
       "                    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",
       "                    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",
       "                    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",
       "                    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': [{'weights': 'uniform', 'n_neighbors': 20, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 20, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 34, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 34, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 49, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 49, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 64, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 64, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 78, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 78, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 93, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 93, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 108, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 108, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 123, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 123, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 137, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 137, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 152, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 152, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 167, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 167, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 182, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 182, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 196, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 196, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 211, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 211, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 226, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 226, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 241, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 241, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 255, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 255, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 270, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 270, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 285, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 285, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 300, 'algorithm': 'auto'},\n",
       "  {'weights': 'distance', 'n_neighbors': 300, 'algorithm': 'auto'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 20, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 20, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 34, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 34, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 49, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 49, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 64, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 64, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 78, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 78, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 93, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 93, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 108, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 108, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 123, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 123, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 137, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 137, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 152, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 152, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 167, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 167, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 182, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 182, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 196, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 196, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 211, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 211, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 226, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 226, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 241, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 241, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 255, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 255, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 270, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 270, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 285, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 285, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 300, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 300, 'algorithm': 'ball_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 20, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 20, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 34, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 34, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 49, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 49, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 64, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 64, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 78, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 78, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 93, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 93, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 108, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 108, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 123, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 123, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 137, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 137, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 152, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 152, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 167, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 167, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 182, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 182, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 196, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 196, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 211, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 211, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 226, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 226, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 241, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 241, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 255, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 255, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 270, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 270, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 285, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 285, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 300, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'distance', 'n_neighbors': 300, 'algorithm': 'kd_tree'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 20, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 20, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 34, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 34, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 49, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 49, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 64, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 64, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 78, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 78, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 93, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 93, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 108, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 108, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 123, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 123, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 137, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 137, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 152, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 152, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 167, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 167, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 182, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 182, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 196, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 196, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 211, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 211, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 226, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 226, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 241, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 241, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 255, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 255, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 270, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 270, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 285, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 285, 'algorithm': 'brute'},\n",
       "  {'weights': 'uniform', 'n_neighbors': 300, 'algorithm': 'brute'},\n",
       "  {'weights': 'distance', 'n_neighbors': 300, 'algorithm': 'brute'}],\n",
       " 'split0_test_score': array([0.90317195, 0.89899833, 0.90233723, 0.90066778, 0.90317195,\n",
       "        0.90233723, 0.90066778, 0.90233723, 0.89899833, 0.9015025 ,\n",
       "        0.89732888, 0.89983306, 0.89732888, 0.90066778, 0.89649416,\n",
       "        0.89983306, 0.89649416, 0.89565943, 0.89565943, 0.89649416,\n",
       "        0.89398998, 0.89649416, 0.89148581, 0.89565943, 0.89065109,\n",
       "        0.89482471, 0.88981636, 0.89482471, 0.88981636, 0.89398998,\n",
       "        0.88898164, 0.89398998, 0.88731219, 0.89398998, 0.88898164,\n",
       "        0.89482471, 0.88814691, 0.89482471, 0.88731219, 0.89482471,\n",
       "        0.90317195, 0.89899833, 0.90233723, 0.90066778, 0.90317195,\n",
       "        0.90233723, 0.90066778, 0.90233723, 0.89899833, 0.9015025 ,\n",
       "        0.89732888, 0.89983306, 0.89732888, 0.90066778, 0.89649416,\n",
       "        0.89983306, 0.89649416, 0.89565943, 0.89565943, 0.89649416,\n",
       "        0.89398998, 0.89649416, 0.89148581, 0.89565943, 0.89065109,\n",
       "        0.89482471, 0.88981636, 0.89482471, 0.88981636, 0.89398998,\n",
       "        0.88898164, 0.89398998, 0.88731219, 0.89398998, 0.88898164,\n",
       "        0.89482471, 0.88814691, 0.89482471, 0.88731219, 0.89482471,\n",
       "        0.90317195, 0.89899833, 0.90233723, 0.90066778, 0.90317195,\n",
       "        0.90233723, 0.90066778, 0.90233723, 0.89899833, 0.9015025 ,\n",
       "        0.89732888, 0.89983306, 0.89732888, 0.90066778, 0.89649416,\n",
       "        0.89983306, 0.89649416, 0.89565943, 0.89565943, 0.89649416,\n",
       "        0.89398998, 0.89649416, 0.89148581, 0.89565943, 0.89065109,\n",
       "        0.89482471, 0.88981636, 0.89482471, 0.88981636, 0.89398998,\n",
       "        0.88898164, 0.89398998, 0.88731219, 0.89398998, 0.88898164,\n",
       "        0.89482471, 0.88814691, 0.89482471, 0.88731219, 0.89482471,\n",
       "        0.90317195, 0.89899833, 0.90233723, 0.90066778, 0.90317195,\n",
       "        0.90233723, 0.90066778, 0.90233723, 0.89899833, 0.9015025 ,\n",
       "        0.89732888, 0.89983306, 0.89732888, 0.90066778, 0.89649416,\n",
       "        0.89983306, 0.89649416, 0.89565943, 0.89565943, 0.89649416,\n",
       "        0.89398998, 0.89649416, 0.89148581, 0.89565943, 0.89065109,\n",
       "        0.89482471, 0.88981636, 0.89482471, 0.88981636, 0.89398998,\n",
       "        0.88898164, 0.89398998, 0.88731219, 0.89398998, 0.88898164,\n",
       "        0.89482471, 0.88814691, 0.89482471, 0.88731219, 0.89482471]),\n",
       " 'split1_test_score': array([0.9115192 , 0.9081803 , 0.9115192 , 0.91235392, 0.91318865,\n",
       "        0.91318865, 0.91068447, 0.91318865, 0.91235392, 0.91318865,\n",
       "        0.91402337, 0.91569282, 0.91318865, 0.91235392, 0.91235392,\n",
       "        0.91068447, 0.90984975, 0.91068447, 0.9081803 , 0.9115192 ,\n",
       "        0.90651085, 0.91068447, 0.90567613, 0.91068447, 0.9048414 ,\n",
       "        0.91068447, 0.90734558, 0.91068447, 0.90734558, 0.91068447,\n",
       "        0.90651085, 0.90984975, 0.90651085, 0.90984975, 0.90567613,\n",
       "        0.90901503, 0.90317195, 0.90901503, 0.90400668, 0.91068447,\n",
       "        0.9115192 , 0.9081803 , 0.9115192 , 0.91235392, 0.91318865,\n",
       "        0.91318865, 0.91068447, 0.91318865, 0.91235392, 0.91318865,\n",
       "        0.91402337, 0.91569282, 0.91318865, 0.91235392, 0.91235392,\n",
       "        0.91068447, 0.90984975, 0.91068447, 0.9081803 , 0.9115192 ,\n",
       "        0.90651085, 0.91068447, 0.90567613, 0.91068447, 0.9048414 ,\n",
       "        0.91068447, 0.90734558, 0.91068447, 0.90734558, 0.91068447,\n",
       "        0.90651085, 0.90984975, 0.90651085, 0.90984975, 0.90567613,\n",
       "        0.90901503, 0.90317195, 0.90901503, 0.90400668, 0.91068447,\n",
       "        0.9115192 , 0.9081803 , 0.9115192 , 0.91235392, 0.91318865,\n",
       "        0.91318865, 0.91068447, 0.91318865, 0.91235392, 0.91318865,\n",
       "        0.91402337, 0.91569282, 0.91318865, 0.91235392, 0.91235392,\n",
       "        0.91068447, 0.90984975, 0.91068447, 0.9081803 , 0.9115192 ,\n",
       "        0.90651085, 0.91068447, 0.90567613, 0.91068447, 0.9048414 ,\n",
       "        0.91068447, 0.90734558, 0.91068447, 0.90734558, 0.91068447,\n",
       "        0.90651085, 0.90984975, 0.90651085, 0.90984975, 0.90567613,\n",
       "        0.90901503, 0.90317195, 0.90901503, 0.90400668, 0.91068447,\n",
       "        0.9115192 , 0.9081803 , 0.9115192 , 0.91235392, 0.91318865,\n",
       "        0.91318865, 0.91068447, 0.91318865, 0.91235392, 0.91318865,\n",
       "        0.91402337, 0.91569282, 0.91318865, 0.91235392, 0.91235392,\n",
       "        0.91068447, 0.90984975, 0.91068447, 0.9081803 , 0.9115192 ,\n",
       "        0.90651085, 0.91068447, 0.90567613, 0.91068447, 0.9048414 ,\n",
       "        0.91068447, 0.90734558, 0.91068447, 0.90734558, 0.91068447,\n",
       "        0.90651085, 0.90984975, 0.90651085, 0.90984975, 0.90567613,\n",
       "        0.90901503, 0.90317195, 0.90901503, 0.90400668, 0.91068447]),\n",
       " 'split2_test_score': array([0.91819699, 0.9081803 , 0.9148581 , 0.91068447, 0.9148581 ,\n",
       "        0.91402337, 0.9148581 , 0.9148581 , 0.91402337, 0.91318865,\n",
       "        0.91235392, 0.91235392, 0.91235392, 0.91235392, 0.91235392,\n",
       "        0.91235392, 0.91235392, 0.9115192 , 0.91068447, 0.91068447,\n",
       "        0.90984975, 0.9115192 , 0.90901503, 0.91068447, 0.90901503,\n",
       "        0.9115192 , 0.90734558, 0.9115192 , 0.9081803 , 0.9115192 ,\n",
       "        0.90567613, 0.91068447, 0.90734558, 0.90984975, 0.90567613,\n",
       "        0.91068447, 0.90400668, 0.90984975, 0.9048414 , 0.90734558,\n",
       "        0.91819699, 0.9081803 , 0.9148581 , 0.91068447, 0.9148581 ,\n",
       "        0.91402337, 0.9148581 , 0.9148581 , 0.91402337, 0.91318865,\n",
       "        0.91235392, 0.91235392, 0.91235392, 0.91235392, 0.91235392,\n",
       "        0.91235392, 0.91235392, 0.9115192 , 0.91068447, 0.91068447,\n",
       "        0.90984975, 0.9115192 , 0.90901503, 0.91068447, 0.90901503,\n",
       "        0.9115192 , 0.90734558, 0.9115192 , 0.9081803 , 0.9115192 ,\n",
       "        0.90567613, 0.91068447, 0.90734558, 0.90984975, 0.90567613,\n",
       "        0.91068447, 0.90400668, 0.90984975, 0.9048414 , 0.90734558,\n",
       "        0.91819699, 0.9081803 , 0.9148581 , 0.91068447, 0.9148581 ,\n",
       "        0.91402337, 0.9148581 , 0.9148581 , 0.91402337, 0.91318865,\n",
       "        0.91235392, 0.91235392, 0.91235392, 0.91235392, 0.91235392,\n",
       "        0.91235392, 0.91235392, 0.9115192 , 0.91068447, 0.91068447,\n",
       "        0.90984975, 0.9115192 , 0.90901503, 0.91068447, 0.90901503,\n",
       "        0.9115192 , 0.90734558, 0.9115192 , 0.9081803 , 0.9115192 ,\n",
       "        0.90567613, 0.91068447, 0.90734558, 0.90984975, 0.90567613,\n",
       "        0.91068447, 0.90400668, 0.90984975, 0.9048414 , 0.90734558,\n",
       "        0.91819699, 0.9081803 , 0.9148581 , 0.91068447, 0.9148581 ,\n",
       "        0.91402337, 0.9148581 , 0.9148581 , 0.91402337, 0.91318865,\n",
       "        0.91235392, 0.91235392, 0.91235392, 0.91235392, 0.91235392,\n",
       "        0.91235392, 0.91235392, 0.9115192 , 0.91068447, 0.91068447,\n",
       "        0.90984975, 0.9115192 , 0.90901503, 0.91068447, 0.90901503,\n",
       "        0.9115192 , 0.90734558, 0.9115192 , 0.9081803 , 0.9115192 ,\n",
       "        0.90567613, 0.91068447, 0.90734558, 0.90984975, 0.90567613,\n",
       "        0.91068447, 0.90400668, 0.90984975, 0.9048414 , 0.90734558]),\n",
       " 'split3_test_score': array([0.90726817, 0.9122807 , 0.9047619 , 0.91144528, 0.90643275,\n",
       "        0.91060986, 0.9047619 , 0.90893901, 0.90643275, 0.90893901,\n",
       "        0.9047619 , 0.90810359, 0.9047619 , 0.90726817, 0.90559733,\n",
       "        0.90726817, 0.90559733, 0.90726817, 0.90726817, 0.90726817,\n",
       "        0.90559733, 0.90643275, 0.90726817, 0.90643275, 0.9047619 ,\n",
       "        0.90643275, 0.90392648, 0.90559733, 0.9047619 , 0.90559733,\n",
       "        0.90559733, 0.90559733, 0.90559733, 0.90559733, 0.90643275,\n",
       "        0.90559733, 0.90392648, 0.90559733, 0.90559733, 0.90559733,\n",
       "        0.90726817, 0.9122807 , 0.9047619 , 0.91144528, 0.90643275,\n",
       "        0.91060986, 0.9047619 , 0.90893901, 0.90643275, 0.90893901,\n",
       "        0.9047619 , 0.90810359, 0.9047619 , 0.90726817, 0.90559733,\n",
       "        0.90726817, 0.90559733, 0.90726817, 0.90726817, 0.90726817,\n",
       "        0.90559733, 0.90643275, 0.90726817, 0.90643275, 0.9047619 ,\n",
       "        0.90643275, 0.90392648, 0.90559733, 0.9047619 , 0.90559733,\n",
       "        0.90559733, 0.90559733, 0.90559733, 0.90559733, 0.90643275,\n",
       "        0.90559733, 0.90392648, 0.90559733, 0.90559733, 0.90559733,\n",
       "        0.90726817, 0.9122807 , 0.9047619 , 0.91144528, 0.90643275,\n",
       "        0.91060986, 0.9047619 , 0.90893901, 0.90643275, 0.90893901,\n",
       "        0.9047619 , 0.90810359, 0.9047619 , 0.90726817, 0.90559733,\n",
       "        0.90726817, 0.90559733, 0.90726817, 0.90726817, 0.90726817,\n",
       "        0.90559733, 0.90643275, 0.90726817, 0.90643275, 0.9047619 ,\n",
       "        0.90643275, 0.90392648, 0.90559733, 0.9047619 , 0.90559733,\n",
       "        0.90559733, 0.90559733, 0.90559733, 0.90559733, 0.90643275,\n",
       "        0.90559733, 0.90392648, 0.90559733, 0.90559733, 0.90559733,\n",
       "        0.90726817, 0.9122807 , 0.9047619 , 0.91144528, 0.90643275,\n",
       "        0.91060986, 0.9047619 , 0.90893901, 0.90643275, 0.90893901,\n",
       "        0.9047619 , 0.90810359, 0.9047619 , 0.90726817, 0.90559733,\n",
       "        0.90726817, 0.90559733, 0.90726817, 0.90726817, 0.90726817,\n",
       "        0.90559733, 0.90643275, 0.90726817, 0.90643275, 0.9047619 ,\n",
       "        0.90643275, 0.90392648, 0.90559733, 0.9047619 , 0.90559733,\n",
       "        0.90559733, 0.90559733, 0.90559733, 0.90559733, 0.90643275,\n",
       "        0.90559733, 0.90392648, 0.90559733, 0.90559733, 0.90559733]),\n",
       " 'split4_test_score': array([0.92564745, 0.92481203, 0.92564745, 0.92648287, 0.9273183 ,\n",
       "        0.92898914, 0.93065998, 0.92898914, 0.93233083, 0.92982456,\n",
       "        0.93233083, 0.93065998, 0.93316625, 0.92982456, 0.93400167,\n",
       "        0.93065998, 0.93316625, 0.93149541, 0.93316625, 0.93065998,\n",
       "        0.93149541, 0.93065998, 0.93065998, 0.93233083, 0.92982456,\n",
       "        0.93149541, 0.92982456, 0.93149541, 0.92898914, 0.93483709,\n",
       "        0.92815372, 0.93400167, 0.93065998, 0.93400167, 0.92982456,\n",
       "        0.93400167, 0.92898914, 0.93400167, 0.92898914, 0.93400167,\n",
       "        0.92564745, 0.92481203, 0.92564745, 0.92648287, 0.9273183 ,\n",
       "        0.92898914, 0.93065998, 0.92898914, 0.93233083, 0.92982456,\n",
       "        0.93233083, 0.93065998, 0.93316625, 0.92982456, 0.93400167,\n",
       "        0.93065998, 0.93316625, 0.93149541, 0.93316625, 0.93065998,\n",
       "        0.93149541, 0.93065998, 0.93065998, 0.93233083, 0.92982456,\n",
       "        0.93149541, 0.92982456, 0.93149541, 0.92898914, 0.93483709,\n",
       "        0.92815372, 0.93400167, 0.93065998, 0.93400167, 0.92982456,\n",
       "        0.93400167, 0.92898914, 0.93400167, 0.92898914, 0.93400167,\n",
       "        0.92564745, 0.92481203, 0.92564745, 0.92648287, 0.9273183 ,\n",
       "        0.92898914, 0.93065998, 0.92898914, 0.93233083, 0.92982456,\n",
       "        0.93233083, 0.93065998, 0.93316625, 0.92982456, 0.93400167,\n",
       "        0.93065998, 0.93316625, 0.93149541, 0.93316625, 0.93065998,\n",
       "        0.93149541, 0.93065998, 0.93065998, 0.93233083, 0.92982456,\n",
       "        0.93149541, 0.92982456, 0.93149541, 0.92898914, 0.93483709,\n",
       "        0.92815372, 0.93400167, 0.93065998, 0.93400167, 0.92982456,\n",
       "        0.93400167, 0.92898914, 0.93400167, 0.92898914, 0.93400167,\n",
       "        0.92564745, 0.92481203, 0.92564745, 0.92648287, 0.9273183 ,\n",
       "        0.92898914, 0.93065998, 0.92898914, 0.93233083, 0.92982456,\n",
       "        0.93233083, 0.93065998, 0.93316625, 0.92982456, 0.93400167,\n",
       "        0.93065998, 0.93316625, 0.93149541, 0.93316625, 0.93065998,\n",
       "        0.93149541, 0.93065998, 0.93065998, 0.93233083, 0.92982456,\n",
       "        0.93149541, 0.92982456, 0.93149541, 0.92898914, 0.93483709,\n",
       "        0.92815372, 0.93400167, 0.93065998, 0.93400167, 0.92982456,\n",
       "        0.93400167, 0.92898914, 0.93400167, 0.92898914, 0.93400167]),\n",
       " 'mean_test_score': array([0.91316075, 0.91049033, 0.91182478, 0.91232687, 0.91299395,\n",
       "        0.91382965, 0.91232645, 0.91366243, 0.91282784, 0.91332868,\n",
       "        0.91215978, 0.91332868, 0.91215992, 0.91249367, 0.9121602 ,\n",
       "        0.91215992, 0.91149228, 0.91132534, 0.91099173, 0.9113252 ,\n",
       "        0.90948866, 0.91115811, 0.90882102, 0.91115839, 0.9078188 ,\n",
       "        0.91099131, 0.90765171, 0.91082422, 0.90781866, 0.91132562,\n",
       "        0.90698393, 0.91082464, 0.90748518, 0.9106577 , 0.90731824,\n",
       "        0.91082464, 0.90564823, 0.9106577 , 0.90614935, 0.91049075,\n",
       "        0.91316075, 0.91049033, 0.91182478, 0.91232687, 0.91299395,\n",
       "        0.91382965, 0.91232645, 0.91366243, 0.91282784, 0.91332868,\n",
       "        0.91215978, 0.91332868, 0.91215992, 0.91249367, 0.9121602 ,\n",
       "        0.91215992, 0.91149228, 0.91132534, 0.91099173, 0.9113252 ,\n",
       "        0.90948866, 0.91115811, 0.90882102, 0.91115839, 0.9078188 ,\n",
       "        0.91099131, 0.90765171, 0.91082422, 0.90781866, 0.91132562,\n",
       "        0.90698393, 0.91082464, 0.90748518, 0.9106577 , 0.90731824,\n",
       "        0.91082464, 0.90564823, 0.9106577 , 0.90614935, 0.91049075,\n",
       "        0.91316075, 0.91049033, 0.91182478, 0.91232687, 0.91299395,\n",
       "        0.91382965, 0.91232645, 0.91366243, 0.91282784, 0.91332868,\n",
       "        0.91215978, 0.91332868, 0.91215992, 0.91249367, 0.9121602 ,\n",
       "        0.91215992, 0.91149228, 0.91132534, 0.91099173, 0.9113252 ,\n",
       "        0.90948866, 0.91115811, 0.90882102, 0.91115839, 0.9078188 ,\n",
       "        0.91099131, 0.90765171, 0.91082422, 0.90781866, 0.91132562,\n",
       "        0.90698393, 0.91082464, 0.90748518, 0.9106577 , 0.90731824,\n",
       "        0.91082464, 0.90564823, 0.9106577 , 0.90614935, 0.91049075,\n",
       "        0.91316075, 0.91049033, 0.91182478, 0.91232687, 0.91299395,\n",
       "        0.91382965, 0.91232645, 0.91366243, 0.91282784, 0.91332868,\n",
       "        0.91215978, 0.91332868, 0.91215992, 0.91249367, 0.9121602 ,\n",
       "        0.91215992, 0.91149228, 0.91132534, 0.91099173, 0.9113252 ,\n",
       "        0.90948866, 0.91115811, 0.90882102, 0.91115839, 0.9078188 ,\n",
       "        0.91099131, 0.90765171, 0.91082422, 0.90781866, 0.91132562,\n",
       "        0.90698393, 0.91082464, 0.90748518, 0.9106577 , 0.90731824,\n",
       "        0.91082464, 0.90564823, 0.9106577 , 0.90614935, 0.91049075]),\n",
       " 'std_test_score': array([0.00798091, 0.0083792 , 0.00824935, 0.00824381, 0.00834531,\n",
       "        0.00863367, 0.01037662, 0.00879924, 0.01108308, 0.00928742,\n",
       "        0.01170526, 0.01016215, 0.01197541, 0.00967012, 0.01237434,\n",
       "        0.01020145, 0.01210699, 0.01156859, 0.01223644, 0.01105171,\n",
       "        0.01223639, 0.0111179 , 0.01256813, 0.01192703, 0.01263823,\n",
       "        0.0118569 , 0.01283589, 0.01192565, 0.01250576, 0.01331597,\n",
       "        0.01245873, 0.01302978, 0.01377355, 0.01303585, 0.01303029,\n",
       "        0.01283582, 0.01313686, 0.01284199, 0.01328985, 0.0129001 ,\n",
       "        0.00798091, 0.0083792 , 0.00824935, 0.00824381, 0.00834531,\n",
       "        0.00863367, 0.01037662, 0.00879924, 0.01108308, 0.00928742,\n",
       "        0.01170526, 0.01016215, 0.01197541, 0.00967012, 0.01237434,\n",
       "        0.01020145, 0.01210699, 0.01156859, 0.01223644, 0.01105171,\n",
       "        0.01223639, 0.0111179 , 0.01256813, 0.01192703, 0.01263823,\n",
       "        0.0118569 , 0.01283589, 0.01192565, 0.01250576, 0.01331597,\n",
       "        0.01245873, 0.01302978, 0.01377355, 0.01303585, 0.01303029,\n",
       "        0.01283582, 0.01313686, 0.01284199, 0.01328985, 0.0129001 ,\n",
       "        0.00798091, 0.0083792 , 0.00824935, 0.00824381, 0.00834531,\n",
       "        0.00863367, 0.01037662, 0.00879924, 0.01108308, 0.00928742,\n",
       "        0.01170526, 0.01016215, 0.01197541, 0.00967012, 0.01237434,\n",
       "        0.01020145, 0.01210699, 0.01156859, 0.01223644, 0.01105171,\n",
       "        0.01223639, 0.0111179 , 0.01256813, 0.01192703, 0.01263823,\n",
       "        0.0118569 , 0.01283589, 0.01192565, 0.01250576, 0.01331597,\n",
       "        0.01245873, 0.01302978, 0.01377355, 0.01303585, 0.01303029,\n",
       "        0.01283582, 0.01313686, 0.01284199, 0.01328985, 0.0129001 ,\n",
       "        0.00798091, 0.0083792 , 0.00824935, 0.00824381, 0.00834531,\n",
       "        0.00863367, 0.01037662, 0.00879924, 0.01108308, 0.00928742,\n",
       "        0.01170526, 0.01016215, 0.01197541, 0.00967012, 0.01237434,\n",
       "        0.01020145, 0.01210699, 0.01156859, 0.01223644, 0.01105171,\n",
       "        0.01223639, 0.0111179 , 0.01256813, 0.01192703, 0.01263823,\n",
       "        0.0118569 , 0.01283589, 0.01192565, 0.01250576, 0.01331597,\n",
       "        0.01245873, 0.01302978, 0.01377355, 0.01303585, 0.01303029,\n",
       "        0.01283582, 0.01313686, 0.01284199, 0.01328985, 0.0129001 ]),\n",
       " 'rank_test_score': array([ 17, 117,  57,  33,  21,   1,  37,   5,  25,  13,  53,   9,  45,\n",
       "         29,  41,  45,  61,  69,  85,  73, 121,  81, 125,  77, 129,  89,\n",
       "        137, 101, 133,  65, 149,  93, 141, 105, 145,  93, 157, 105, 153,\n",
       "        113,  17, 117,  57,  33,  21,   1,  37,   5,  25,  13,  53,   9,\n",
       "         45,  29,  41,  45,  61,  69,  85,  73, 121,  81, 125,  77, 129,\n",
       "         89, 137, 101, 133,  65, 149,  93, 141, 105, 145,  93, 157, 105,\n",
       "        153, 113,  17, 117,  57,  33,  21,   1,  37,   5,  25,  13,  53,\n",
       "          9,  45,  29,  41,  45,  61,  69,  85,  73, 121,  81, 125,  77,\n",
       "        129,  89, 137, 101, 133,  65, 149,  93, 141, 105, 145,  93, 157,\n",
       "        105, 153, 113,  17, 117,  57,  33,  21,   1,  37,   5,  25,  13,\n",
       "         53,   9,  45,  29,  41,  45,  61,  69,  85,  73, 121,  81, 125,\n",
       "         77, 129,  89, 137, 101, 133,  65, 149,  93, 141, 105, 145,  93,\n",
       "        157, 105, 153, 113])}"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv_clf.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "4924bfe9",
   "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>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_weights</th>\n",
       "      <th>param_n_neighbors</th>\n",
       "      <th>param_algorithm</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>0.006723</td>\n",
       "      <td>0.006033</td>\n",
       "      <td>0.088251</td>\n",
       "      <td>0.011237</td>\n",
       "      <td>uniform</td>\n",
       "      <td>20</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 20, 'alg...</td>\n",
       "      <td>0.903172</td>\n",
       "      <td>0.911519</td>\n",
       "      <td>0.918197</td>\n",
       "      <td>0.907268</td>\n",
       "      <td>0.925647</td>\n",
       "      <td>0.913161</td>\n",
       "      <td>0.007981</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.003126</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>0.012500</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>distance</td>\n",
       "      <td>20</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 20, 'al...</td>\n",
       "      <td>0.898998</td>\n",
       "      <td>0.908180</td>\n",
       "      <td>0.908180</td>\n",
       "      <td>0.912281</td>\n",
       "      <td>0.924812</td>\n",
       "      <td>0.910490</td>\n",
       "      <td>0.008379</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.007648</td>\n",
       "      <td>0.006994</td>\n",
       "      <td>0.080010</td>\n",
       "      <td>0.003060</td>\n",
       "      <td>uniform</td>\n",
       "      <td>34</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 34, 'alg...</td>\n",
       "      <td>0.902337</td>\n",
       "      <td>0.911519</td>\n",
       "      <td>0.914858</td>\n",
       "      <td>0.904762</td>\n",
       "      <td>0.925647</td>\n",
       "      <td>0.911825</td>\n",
       "      <td>0.008249</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.003127</td>\n",
       "      <td>0.006253</td>\n",
       "      <td>0.018747</td>\n",
       "      <td>0.006250</td>\n",
       "      <td>distance</td>\n",
       "      <td>34</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 34, 'al...</td>\n",
       "      <td>0.900668</td>\n",
       "      <td>0.912354</td>\n",
       "      <td>0.910684</td>\n",
       "      <td>0.911445</td>\n",
       "      <td>0.926483</td>\n",
       "      <td>0.912327</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.004606</td>\n",
       "      <td>0.003829</td>\n",
       "      <td>0.098982</td>\n",
       "      <td>0.022064</td>\n",
       "      <td>uniform</td>\n",
       "      <td>49</td>\n",
       "      <td>auto</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 49, 'alg...</td>\n",
       "      <td>0.903172</td>\n",
       "      <td>0.913189</td>\n",
       "      <td>0.914858</td>\n",
       "      <td>0.906433</td>\n",
       "      <td>0.927318</td>\n",
       "      <td>0.912994</td>\n",
       "      <td>0.008345</td>\n",
       "      <td>21</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0.005543</td>\n",
       "      <td>0.005153</td>\n",
       "      <td>0.288253</td>\n",
       "      <td>0.053640</td>\n",
       "      <td>distance</td>\n",
       "      <td>270</td>\n",
       "      <td>brute</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 270, 'a...</td>\n",
       "      <td>0.894825</td>\n",
       "      <td>0.909015</td>\n",
       "      <td>0.910684</td>\n",
       "      <td>0.905597</td>\n",
       "      <td>0.934002</td>\n",
       "      <td>0.910825</td>\n",
       "      <td>0.012836</td>\n",
       "      <td>93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>0.004924</td>\n",
       "      <td>0.005475</td>\n",
       "      <td>0.312847</td>\n",
       "      <td>0.009324</td>\n",
       "      <td>uniform</td>\n",
       "      <td>285</td>\n",
       "      <td>brute</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 285, 'al...</td>\n",
       "      <td>0.888147</td>\n",
       "      <td>0.903172</td>\n",
       "      <td>0.904007</td>\n",
       "      <td>0.903926</td>\n",
       "      <td>0.928989</td>\n",
       "      <td>0.905648</td>\n",
       "      <td>0.013137</td>\n",
       "      <td>157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>157</th>\n",
       "      <td>0.004523</td>\n",
       "      <td>0.005776</td>\n",
       "      <td>0.250774</td>\n",
       "      <td>0.012929</td>\n",
       "      <td>distance</td>\n",
       "      <td>285</td>\n",
       "      <td>brute</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 285, 'a...</td>\n",
       "      <td>0.894825</td>\n",
       "      <td>0.909015</td>\n",
       "      <td>0.909850</td>\n",
       "      <td>0.905597</td>\n",
       "      <td>0.934002</td>\n",
       "      <td>0.910658</td>\n",
       "      <td>0.012842</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>158</th>\n",
       "      <td>0.001798</td>\n",
       "      <td>0.001468</td>\n",
       "      <td>0.326700</td>\n",
       "      <td>0.018813</td>\n",
       "      <td>uniform</td>\n",
       "      <td>300</td>\n",
       "      <td>brute</td>\n",
       "      <td>{'weights': 'uniform', 'n_neighbors': 300, 'al...</td>\n",
       "      <td>0.887312</td>\n",
       "      <td>0.904007</td>\n",
       "      <td>0.904841</td>\n",
       "      <td>0.905597</td>\n",
       "      <td>0.928989</td>\n",
       "      <td>0.906149</td>\n",
       "      <td>0.013290</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>0.003197</td>\n",
       "      <td>0.000400</td>\n",
       "      <td>0.257241</td>\n",
       "      <td>0.004215</td>\n",
       "      <td>distance</td>\n",
       "      <td>300</td>\n",
       "      <td>brute</td>\n",
       "      <td>{'weights': 'distance', 'n_neighbors': 300, 'a...</td>\n",
       "      <td>0.894825</td>\n",
       "      <td>0.910684</td>\n",
       "      <td>0.907346</td>\n",
       "      <td>0.905597</td>\n",
       "      <td>0.934002</td>\n",
       "      <td>0.910491</td>\n",
       "      <td>0.012900</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>160 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "0         0.006723      0.006033         0.088251        0.011237   \n",
       "1         0.003126      0.006252         0.012500        0.006250   \n",
       "2         0.007648      0.006994         0.080010        0.003060   \n",
       "3         0.003127      0.006253         0.018747        0.006250   \n",
       "4         0.004606      0.003829         0.098982        0.022064   \n",
       "..             ...           ...              ...             ...   \n",
       "155       0.005543      0.005153         0.288253        0.053640   \n",
       "156       0.004924      0.005475         0.312847        0.009324   \n",
       "157       0.004523      0.005776         0.250774        0.012929   \n",
       "158       0.001798      0.001468         0.326700        0.018813   \n",
       "159       0.003197      0.000400         0.257241        0.004215   \n",
       "\n",
       "    param_weights param_n_neighbors param_algorithm  \\\n",
       "0         uniform                20            auto   \n",
       "1        distance                20            auto   \n",
       "2         uniform                34            auto   \n",
       "3        distance                34            auto   \n",
       "4         uniform                49            auto   \n",
       "..            ...               ...             ...   \n",
       "155      distance               270           brute   \n",
       "156       uniform               285           brute   \n",
       "157      distance               285           brute   \n",
       "158       uniform               300           brute   \n",
       "159      distance               300           brute   \n",
       "\n",
       "                                                params  split0_test_score  \\\n",
       "0    {'weights': 'uniform', 'n_neighbors': 20, 'alg...           0.903172   \n",
       "1    {'weights': 'distance', 'n_neighbors': 20, 'al...           0.898998   \n",
       "2    {'weights': 'uniform', 'n_neighbors': 34, 'alg...           0.902337   \n",
       "3    {'weights': 'distance', 'n_neighbors': 34, 'al...           0.900668   \n",
       "4    {'weights': 'uniform', 'n_neighbors': 49, 'alg...           0.903172   \n",
       "..                                                 ...                ...   \n",
       "155  {'weights': 'distance', 'n_neighbors': 270, 'a...           0.894825   \n",
       "156  {'weights': 'uniform', 'n_neighbors': 285, 'al...           0.888147   \n",
       "157  {'weights': 'distance', 'n_neighbors': 285, 'a...           0.894825   \n",
       "158  {'weights': 'uniform', 'n_neighbors': 300, 'al...           0.887312   \n",
       "159  {'weights': 'distance', 'n_neighbors': 300, 'a...           0.894825   \n",
       "\n",
       "     split1_test_score  split2_test_score  split3_test_score  \\\n",
       "0             0.911519           0.918197           0.907268   \n",
       "1             0.908180           0.908180           0.912281   \n",
       "2             0.911519           0.914858           0.904762   \n",
       "3             0.912354           0.910684           0.911445   \n",
       "4             0.913189           0.914858           0.906433   \n",
       "..                 ...                ...                ...   \n",
       "155           0.909015           0.910684           0.905597   \n",
       "156           0.903172           0.904007           0.903926   \n",
       "157           0.909015           0.909850           0.905597   \n",
       "158           0.904007           0.904841           0.905597   \n",
       "159           0.910684           0.907346           0.905597   \n",
       "\n",
       "     split4_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "0             0.925647         0.913161        0.007981               17  \n",
       "1             0.924812         0.910490        0.008379              117  \n",
       "2             0.925647         0.911825        0.008249               57  \n",
       "3             0.926483         0.912327        0.008244               33  \n",
       "4             0.927318         0.912994        0.008345               21  \n",
       "..                 ...              ...             ...              ...  \n",
       "155           0.934002         0.910825        0.012836               93  \n",
       "156           0.928989         0.905648        0.013137              157  \n",
       "157           0.934002         0.910658        0.012842              105  \n",
       "158           0.928989         0.906149        0.013290              153  \n",
       "159           0.934002         0.910491        0.012900              113  \n",
       "\n",
       "[160 rows x 16 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results_clf = pd.DataFrame(rmcv_clf.cv_results_)\n",
    "results_clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "fb4a1fa9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=49, weights='distance')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv_clf.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "fbfc275a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'weights': 'distance', 'n_neighbors': 49, 'algorithm': 'auto'}"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv_clf.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "8896ce11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9138296492483295"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmcv_clf.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "1b6be8dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=49, weights='distance')"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_clf3 = rmcv_clf.best_estimator_   # model assign with best parameters\n",
    "KNN_clf3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "6b6124e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9216984807167901"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KNN_clf3.score(xtest, ytest)        # KNN classification best score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85ea142c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.9.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}