grid search.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import make_regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_regression(n_samples=1000, n_features=20, noise=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.11085077, -1.39854822, -0.07661084, ..., -0.35925105,\n",
       "         0.15303801, -1.65750589],\n",
       "       [-1.05750171,  1.03977359,  0.27847958, ..., -0.02630817,\n",
       "         0.69578606,  0.29527599],\n",
       "       [ 0.6392662 ,  1.2268471 , -1.20248918, ...,  0.50037255,\n",
       "         0.43156073,  0.92425099],\n",
       "       ...,\n",
       "       [ 0.07806157, -0.41318647,  1.24092779, ..., -1.23104134,\n",
       "         0.08183915, -2.15194383],\n",
       "       [ 0.57150346,  0.19738076, -0.16886074, ...,  0.07279976,\n",
       "        -1.20508294,  2.16069415],\n",
       "       [ 0.39308879,  0.37576137,  0.50388305, ..., -0.72232908,\n",
       "        -1.37657168,  0.58281299]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       -2.30902033e+02, -2.86889053e+02,  6.24563915e+01, -4.74681757e+00,\n",
       "        4.52221034e+01,  2.06831149e+02, -5.50905512e+01, -1.63963019e+01,\n",
       "       -1.04840839e+02, -2.95460636e+01,  1.84930225e+02, -7.02812870e+01,\n",
       "       -9.20345542e+01,  8.23751759e+01, -5.19995746e+01,  3.75495935e+02,\n",
       "       -1.28065354e+02,  7.99937929e+01,  1.50081764e+02, -4.48613671e+01,\n",
       "       -3.14942365e+02, -1.64074236e+02, -8.04427492e+01,  8.89100080e+01,\n",
       "       -1.96427856e+02, -3.40695824e+00, -7.16811807e+00,  1.62789846e+02,\n",
       "        2.75213763e+02, -1.19121799e+02,  2.16925917e+01, -3.36859160e+02,\n",
       "        3.84821214e+01, -3.15596345e+02,  6.61838683e+00,  5.87671237e+00,\n",
       "       -6.36979739e+01,  2.31364027e+02, -7.00066220e+01,  1.38272252e+02,\n",
       "        1.11784920e+02, -1.08440714e+02,  1.07083159e+02, -3.37633411e+02,\n",
       "        1.15332931e+02,  2.57568588e+02, -2.92891093e+02, -1.19404393e+02,\n",
       "       -3.11105937e+02,  3.20266143e+02,  1.07215319e+02,  1.41114964e+02,\n",
       "       -1.16775459e+02,  1.20630175e+02,  1.33478317e+02, -1.09356384e+02,\n",
       "       -3.61563032e+02,  3.67751936e+01,  1.48919560e+02,  3.06571625e+02,\n",
       "        2.76972099e+01, -4.00822142e+02, -9.06896185e+01,  3.44295484e+02,\n",
       "        2.19170001e+02,  3.29425466e+01, -7.02034233e+01, -2.02996694e+02,\n",
       "        1.72134635e+01,  4.27772635e+02,  1.92092903e+02, -1.98564707e+02,\n",
       "       -1.10821980e+02,  2.22757121e+02,  2.37390829e+02, -2.23315513e+02,\n",
       "        1.56685194e+01,  2.25729193e+02, -1.88747616e+01, -2.93111561e+02,\n",
       "       -2.91371861e+02, -1.90113689e+02,  7.14173194e+01,  6.78215931e+01,\n",
       "        3.47585548e+02, -9.06337875e+01, -6.95331550e+01, -1.43691714e+02,\n",
       "       -3.15315669e+01, -1.76811036e+02, -1.65414466e+02,  1.20774431e+02,\n",
       "        2.67572968e+02, -1.28648653e+02,  1.45282416e+02, -1.66516175e+01,\n",
       "        2.22699624e+02,  2.55647324e-01,  5.08626997e+01,  1.69983514e+02,\n",
       "       -1.49887252e+02,  2.82455016e+01, -2.55204049e+02, -1.27269258e+02,\n",
       "        2.40558258e+02,  2.30171926e+02,  2.22875030e+02, -6.87064205e+01,\n",
       "        3.10852505e+02, -4.49531955e+01, -1.79538445e+02,  1.30037053e+02,\n",
       "       -1.96982953e+02, -1.92989355e+02,  1.77101223e+01, -9.54740021e+01,\n",
       "        1.02502788e+02, -2.62400957e+02,  1.35833662e+01, -1.16140382e+01,\n",
       "       -4.02350745e+02, -1.94774690e+02,  2.16018399e+02, -2.67466763e+02,\n",
       "        4.80329145e+02, -1.30797810e+02, -3.80725820e+02, -2.82700838e+02,\n",
       "       -1.17391263e+02, -1.14512025e+02,  2.39377910e+02,  3.21787735e+02,\n",
       "        8.51813640e+01,  9.47632284e+01, -9.96591000e+01, -1.58535178e+02,\n",
       "        3.64874444e+01, -2.57128121e+02, -2.22671253e+01,  2.46921054e+01,\n",
       "       -2.91416116e+01,  1.49675648e+01, -1.08632899e+02, -6.39767839e+01,\n",
       "        5.76424106e+01,  2.12620701e+02,  1.66998472e+02, -8.19385638e+01,\n",
       "       -2.39272770e+02,  2.35638690e+01, -5.97999458e+01, -2.83502192e+02,\n",
       "        9.93183938e+00, -9.41033532e+01,  1.44270061e+02, -1.02365396e+02,\n",
       "       -3.58457969e+01, -1.26130191e+02, -7.65565864e+01,  2.33570740e+01,\n",
       "        1.18226355e+02, -4.55290875e+00,  1.38922902e+02, -1.72623158e+02,\n",
       "        8.12585915e+01,  2.15305591e+02, -4.15742594e+02, -1.08642552e+02,\n",
       "       -6.41722800e+01,  2.35244342e+02,  1.01805877e+02,  3.03814345e+02,\n",
       "        1.17128645e+02,  1.98544203e+02,  3.91536652e+01, -1.51665300e+02,\n",
       "       -1.63430289e+02,  4.40930766e+02, -2.05817453e+02,  1.70816074e+02,\n",
       "        2.53829992e+02,  3.43798596e+01, -1.83485676e+02, -3.46860724e+01,\n",
       "        3.98118010e+01,  3.57513207e+01, -2.07382648e+01, -4.73583634e+02,\n",
       "        3.52509332e+02,  2.29944507e+02,  1.26700518e+02,  2.48017150e+02,\n",
       "        3.04860572e+02,  1.95694549e+02,  3.44729905e+02, -4.60438840e+01,\n",
       "       -8.63630498e+00, -8.38725143e+00, -1.58507619e+02,  2.81167558e+02,\n",
       "        1.48530430e+02, -3.18879440e+02, -1.57636649e+02, -3.22258552e+02,\n",
       "        1.51002029e+02, -3.53333954e+02, -4.01313832e+01, -1.78955097e+02,\n",
       "       -1.14537038e+02,  5.25148277e+02,  2.58430962e+02,  1.53229489e+01,\n",
       "       -2.47478751e+02,  1.48237534e+02, -1.70398347e+01,  1.24773977e+02,\n",
       "        6.48032240e+01, -1.11339408e+02,  1.47948308e+02,  3.36480364e+00,\n",
       "       -4.43487412e+01,  1.56650777e+02,  2.41662062e+02, -1.33400409e+02,\n",
       "        2.80061974e+02, -4.85637977e+01,  9.35715711e+01, -1.16786064e+01,\n",
       "       -8.77276127e+01, -1.48830387e+02, -1.17694701e+02, -1.06677555e+02,\n",
       "        3.60164829e+02, -1.70447702e+02,  7.91872495e+01, -2.04176844e+02,\n",
       "       -1.21188502e+02,  6.29488180e+01,  1.16433632e+02, -2.45046485e+02,\n",
       "        3.92039750e+01,  3.37363619e+02,  2.50091823e+02,  2.62342795e+02,\n",
       "       -3.35129081e+02, -9.10810660e+01,  8.58532397e+00,  4.31417947e+01,\n",
       "       -2.61550140e+01,  6.06219406e+01, -1.49506856e+02,  1.13312876e+02,\n",
       "        1.96527264e+01, -3.53111663e+01,  7.72137981e+01, -2.51756379e+02,\n",
       "       -2.00295651e+02, -1.06828663e+02,  6.26561905e+01, -3.81090061e+01,\n",
       "       -4.01670769e+02, -2.27841772e+02,  1.73000339e+02, -1.61209325e+01,\n",
       "        1.27269960e+02, -8.88183495e+00, -3.58558683e+01,  3.10134576e+02,\n",
       "       -4.53888016e+00,  7.90678323e+01,  4.52803251e+02,  4.92221074e+01,\n",
       "        1.63879566e+02, -3.47936843e+02,  1.09892173e+02,  5.67770771e+02,\n",
       "        1.55666637e+02, -2.37978367e+01, -2.02144093e+01, -2.00230629e+02,\n",
       "        2.08252237e+02, -4.09119228e+01, -1.24166609e+01, -3.39445240e+02,\n",
       "        3.50453934e+02,  4.44832119e+01, -4.25234602e+02,  3.10214346e+02,\n",
       "        5.86850599e+01, -4.37756594e+01,  2.79476298e+02,  2.70527517e+02,\n",
       "        8.59950822e+01,  6.47969322e+01,  4.34933682e+02, -4.10733146e+01,\n",
       "       -6.56171614e+01, -4.64813740e+01,  1.22822507e+02, -2.67234927e+02,\n",
       "        1.06139090e+02,  3.57903900e+02,  1.61696747e+02, -2.60046687e+02,\n",
       "       -2.39519874e+02,  5.96005438e+01,  1.57334802e+02,  3.95481464e+01,\n",
       "       -4.96903244e+01,  1.47542112e+02, -1.48952750e+02, -1.99730485e+02,\n",
       "        2.15107111e+02,  1.79640298e+02,  6.50937992e+01, -1.84264531e+02,\n",
       "        1.44473560e+02, -1.10899819e+00, -4.88116610e+01,  2.51826079e+02,\n",
       "        1.65187482e+02,  1.79978411e+01, -2.36878792e+02, -1.24839259e+02,\n",
       "        2.61457022e+02,  2.04584395e+02,  4.09968532e+01, -2.71283411e+02,\n",
       "       -8.58031408e+01, -1.29596632e+02,  1.42948940e+02, -2.89255608e+01,\n",
       "       -3.14958623e+02,  1.12029615e+02, -2.57572374e+01,  2.53477913e+02,\n",
       "        2.06582322e+01,  8.45831130e+01,  4.02329594e+01, -3.22958419e+02,\n",
       "        1.41429996e+02,  1.45924996e+02,  2.21985304e+02,  1.88214415e+02,\n",
       "        3.87264538e+00,  4.02667634e+02,  5.14245850e+01, -5.26946777e+01,\n",
       "        1.82337797e+02,  1.33802772e+02,  4.67845064e+01, -2.98605987e+02,\n",
       "        2.61809261e+02, -5.50032205e+02,  1.34306289e+02,  2.02805846e+02,\n",
       "        1.52149136e+02, -2.26507096e+02, -1.41352393e+02, -7.68736302e+01,\n",
       "       -5.76353624e+01,  3.11918254e+02, -1.34571099e+02,  3.08006634e+01,\n",
       "       -2.92523674e+02,  1.67820368e+02, -8.50518671e+01, -2.11275789e+01,\n",
       "        2.27478548e+01,  1.33935723e+02, -4.73153304e+02,  2.54566910e+01,\n",
       "       -1.33303107e+01, -1.11181228e+02,  6.55313640e+01, -8.47275226e+01,\n",
       "       -4.94361855e+01,  1.38074798e+02, -2.97001291e+02,  7.75359086e+01,\n",
       "       -2.04281169e+02,  2.32666592e+01, -1.81223164e+02,  2.01741779e+02,\n",
       "       -3.56741070e+01, -1.93478751e+02, -1.66562911e+02, -1.16305117e+02,\n",
       "       -3.29730040e+02,  1.72703807e+02,  1.64025237e+01,  1.89975294e+00,\n",
       "       -3.56054358e+02, -4.82446370e+02,  1.67012205e+02, -5.86718076e+01])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800, 20)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200,)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(800,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Ridge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Ridge()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# know parameters of different models\n",
    "param_grid = {\n",
    "    'alpha': [0.001,0.01,0.1, 1.0, 10.0, 100.0],\n",
    "    'solver': ['auto', 'svd', 'lsqr']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='neg_mean_squared_error')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={&#x27;alpha&#x27;: [0.001, 0.01, 0.1, 1.0, 10.0, 100.0],\n",
       "                         &#x27;solver&#x27;: [&#x27;auto&#x27;, &#x27;svd&#x27;, &#x27;lsqr&#x27;]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={&#x27;alpha&#x27;: [0.001, 0.01, 0.1, 1.0, 10.0, 100.0],\n",
       "                         &#x27;solver&#x27;: [&#x27;auto&#x27;, &#x27;svd&#x27;, &#x27;lsqr&#x27;]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Ridge</label><div class=\"sk-toggleable__content\"><pre>Ridge()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={'alpha': [0.001, 0.01, 0.1, 1.0, 10.0, 100.0],\n",
       "                         'solver': ['auto', 'svd', 'lsqr']},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_params = grid_search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 0.001, 'solver': 'lsqr'}"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = best_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "mse = mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.011288942998358283"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse"
   ]
  }
 ],
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