Regressor Hyperparameter Optimization.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "#%matplotlib notebook\n",
    "\n",
    "#Basic stuff\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "\n",
    "# ML models\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import explained_variance_score, median_absolute_error\n",
    "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n",
    "from sklearn.metrics import make_scorer\n",
    "\n",
    "# Initialize tqdm progress bar\n",
    "from tqdm import tqdm, tnrange, tqdm_notebook\n",
    "from tqdm.auto import tqdm\n",
    "tqdm.pandas(tqdm_notebook)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The averaged random forest model\n",
    "%reload_ext autoreload\n",
    "%autoreload 2\n",
    "from AverageRF import AverageRF\n",
    "from utils import train_test_split_data, evaluate_model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "active = pd.read_pickle(\"./active.pkl.bz2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare a DataFrame with only the data we need\n",
    "columns = ['PUBCHEM_SID', 'RDKit Fingerprints', 'Log IC50']\n",
    "data = active.filter(columns,axis=1)\n",
    "data.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train: 746 \t\ty_train: 746\n",
      "X_test:  83 \t\ty_test:  83\n",
      "------------------------------------\n",
      "TOTAL: 829\n"
     ]
    }
   ],
   "source": [
    "# This split will return the indices for X, and the y values\n",
    "X_data = np.array(list(data['RDKit Fingerprints'])).astype(int)\n",
    "y_data = data['Log IC50'].values\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_data, y_data, test_size=0.10, shuffle=True)\n",
    "\n",
    "print(\"X_train:\", len(X_train), \"\\t\\ty_train:\",len(y_train))\n",
    "print(\"X_test: \", len(X_test), \"\\t\\ty_test: \",len(y_test))\n",
    "print(\"-\"*36)\n",
    "print(\"TOTAL:\",len(X_train) + len(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((83, 2048), (83,))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_estimators=[150,250,350,450] # Number of estimators\n",
    "criterion=['mse','mae']\n",
    "max_features=[None,0.33,'sqrt']\n",
    "bootstrap=[True,False]\n",
    "\n",
    "## Create the random grid\n",
    "param_grid = {'n_estimators': n_estimators,\n",
    "              'criterion': criterion,\n",
    "              'max_features': max_features,\n",
    "              'bootstrap':bootstrap}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 48 candidates, totalling 240 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] bootstrap=True, criterion=mse, max_features=None, n_estimators=150 \n",
      "[CV]  bootstrap=True, criterion=mse, max_features=None, n_estimators=150, total=  13.4s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:   13.9s remaining:    0.0s\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] bootstrap=True, criterion=mse, max_features=None, n_estimators=150 \n",
      "[CV]  bootstrap=True, criterion=mse, max_features=None, n_estimators=150, total=   4.2s\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=True, criterion=mse, max_features=sqrt, n_estimators=350, total=   1.2s\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV] bootstrap=True, criterion=mae, max_features=sqrt, n_estimators=150 \n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, criterion=mse, max_features=0.33, n_estimators=350, total=   5.4s\n",
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      "[CV]  bootstrap=False, criterion=mae, max_features=None, n_estimators=450, total= 3.7min\n",
      "[CV] bootstrap=False, criterion=mae, max_features=None, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=None, n_estimators=450, total= 3.7min\n",
      "[CV] bootstrap=False, criterion=mae, max_features=None, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=None, n_estimators=450, total= 3.7min\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150, total=  25.3s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150, total=  25.0s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150 \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150, total=  26.2s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150, total=  25.3s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=150, total=  25.5s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250, total=  42.3s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250, total=  41.8s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250, total=  42.4s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250, total=  41.8s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=250, total=  42.1s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350, total=  58.5s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350, total=  57.9s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350, total=  58.8s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350, total=  59.9s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=350, total=  58.9s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450, total= 1.3min\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450, total= 1.2min\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450, total= 1.3min\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450, total= 1.2min\n",
      "[CV] bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=0.33, n_estimators=450, total= 1.3min\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150, total=   2.4s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150, total=   2.3s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150, total=   2.4s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150, total=   2.3s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=150, total=   2.3s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250, total=   3.7s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250, total=   3.7s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250, total=   3.6s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250, total=   3.6s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=250, total=   3.5s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350, total=   5.0s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350, total=   5.0s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350, total=   4.9s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350, total=   4.8s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=350, total=   4.9s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450, total=   6.0s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450, total=   6.0s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450, total=   6.2s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450, total=   6.1s\n",
      "[CV] bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450 \n",
      "[CV]  bootstrap=False, criterion=mae, max_features=sqrt, n_estimators=450, total=   6.0s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Done 240 out of 240 | elapsed: 120.3min finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, error_score='raise-deprecating',\n",
       "       estimator=RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators='warn', n_jobs=4,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False),\n",
       "       fit_params=None, iid='warn', n_jobs=None,\n",
       "       param_grid={'n_estimators': [150, 250, 350, 450], 'criterion': ['mse', 'mae'], 'max_features': [None, 0.33, 'sqrt'], 'bootstrap': [True, False]},\n",
       "       pre_dispatch='2*n_jobs', refit='R2', return_train_score='warn',\n",
       "       scoring={'Explained Variance': make_scorer(explained_variance_score), 'MAE': make_scorer(mean_absolute_error), 'MSE': make_scorer(mean_squared_error), 'MedAE': make_scorer(median_absolute_error), 'R2': make_scorer(r2_score)},\n",
       "       verbose=2)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestRegressor(random_state=42, n_jobs=4)\n",
    "\n",
    "scoring = {'Explained Variance': make_scorer(explained_variance_score),\n",
    "           'MAE':make_scorer(mean_absolute_error),\n",
    "           'MSE':make_scorer(mean_squared_error),\n",
    "           'MedAE':make_scorer(median_absolute_error),\n",
    "           'R2': make_scorer(r2_score)}\n",
    "\n",
    "gs = GridSearchCV(estimator=model, param_grid=param_grid,\n",
    "                  scoring=scoring, refit='R2',\n",
    "                  cv=5, verbose=2)\n",
    "\n",
    "gs.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mean_fit_time': array([  5.66562905,   6.34625769,   8.74882274,  11.96316328,\n",
       "          1.46592722,   2.28458366,   3.22367744,   4.02461114,\n",
       "          0.27884316,   0.49417739,   0.6183722 ,   0.65658426,\n",
       "         43.68025146,  72.63489833, 100.59993606, 127.84305511,\n",
       "         14.48612556,  23.75637474,  33.29442863,  45.12888284,\n",
       "          1.21605215,   1.93965707,   2.61587586,   3.30300055,\n",
       "          6.30323949,  10.54898219,  14.3578958 ,  18.45999236,\n",
       "          2.18993802,   3.67818298,   5.10872459,   6.67815838,\n",
       "          0.31887956,   0.61493864,   0.70256124,   0.96325731,\n",
       "         73.98010216, 123.77231889, 172.67761078, 220.71282892,\n",
       "         25.0406323 ,  41.66194897,  58.37813931,  74.65388637,\n",
       "          1.91794696,   3.19395065,   4.49367642,   5.64142504]),\n",
       " 'std_fit_time': array([3.57103062, 0.16352569, 0.10585864, 0.52821362, 0.02017219,\n",
       "        0.02584229, 0.0718101 , 0.04645089, 0.04092807, 0.06958584,\n",
       "        0.08267538, 0.04523503, 0.32809191, 0.67051894, 1.15351297,\n",
       "        1.02879802, 0.2280901 , 0.11130817, 0.19600306, 1.84748055,\n",
       "        0.00926264, 0.05001427, 0.04356641, 0.02179979, 0.1576714 ,\n",
       "        0.48491435, 0.510934  , 0.35312381, 0.0227465 , 0.06701178,\n",
       "        0.05996264, 0.21535841, 0.00963582, 0.00956473, 0.03140775,\n",
       "        0.0285348 , 1.05850365, 1.69234407, 2.60159474, 2.87044127,\n",
       "        0.39858824, 0.23423959, 0.64856329, 0.55951661, 0.04473395,\n",
       "        0.06402961, 0.06775127, 0.0714832 ]),\n",
       " 'mean_score_time': array([0.55317874, 0.52028542, 0.51995082, 0.51952114, 0.51994276,\n",
       "        0.52058029, 0.52023053, 0.51967707, 0.52055426, 0.51988654,\n",
       "        0.52014852, 0.52081876, 0.52134848, 0.52105145, 0.52028356,\n",
       "        0.52065873, 0.51972585, 0.51975217, 0.52099266, 0.52056351,\n",
       "        0.52044287, 0.52015672, 0.51961975, 0.51967611, 0.52050076,\n",
       "        0.52040524, 0.52010756, 0.52047696, 0.52005043, 0.51947432,\n",
       "        0.51944537, 0.52094049, 0.51974306, 0.52004232, 0.51986108,\n",
       "        0.5202805 , 0.52075853, 0.52061052, 0.52099619, 0.52070384,\n",
       "        0.52068229, 0.52083721, 0.52038693, 0.52100749, 0.5197763 ,\n",
       "        0.51918406, 0.51970119, 0.51963277]),\n",
       " 'std_score_time': array([0.067141  , 0.00061467, 0.00114174, 0.00099247, 0.00046358,\n",
       "        0.00132258, 0.0007927 , 0.00096608, 0.00086042, 0.00047676,\n",
       "        0.00051114, 0.00064762, 0.00060572, 0.00048672, 0.00062959,\n",
       "        0.00085346, 0.00058073, 0.00059492, 0.00096381, 0.00032306,\n",
       "        0.00064712, 0.00040358, 0.00043402, 0.00053273, 0.00090852,\n",
       "        0.00054932, 0.00035797, 0.00115521, 0.0008923 , 0.00027336,\n",
       "        0.00057709, 0.0004825 , 0.00044301, 0.00063206, 0.00073546,\n",
       "        0.00100496, 0.00038958, 0.0008775 , 0.00079459, 0.00059313,\n",
       "        0.00115913, 0.00071372, 0.00032623, 0.00138377, 0.00074904,\n",
       "        0.00155066, 0.00039557, 0.00061104]),\n",
       " 'param_bootstrap': masked_array(data=[True, True, True, True, True, True, True, True, True,\n",
       "                    True, True, True, True, True, True, True, True, True,\n",
       "                    True, True, True, True, True, True, 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],\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",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_criterion': masked_array(data=['mse', 'mse', 'mse', 'mse', 'mse', 'mse', 'mse', 'mse',\n",
       "                    'mse', 'mse', 'mse', 'mse', 'mae', 'mae', 'mae', 'mae',\n",
       "                    'mae', 'mae', 'mae', 'mae', 'mae', 'mae', 'mae', 'mae',\n",
       "                    'mse', 'mse', 'mse', 'mse', 'mse', 'mse', 'mse', 'mse',\n",
       "                    'mse', 'mse', 'mse', 'mse', 'mae', 'mae', 'mae', 'mae',\n",
       "                    'mae', 'mae', 'mae', 'mae', 'mae', 'mae', 'mae', 'mae'],\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",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_max_features': masked_array(data=[None, None, None, None, 0.33, 0.33, 0.33, 0.33, 'sqrt',\n",
       "                    'sqrt', 'sqrt', 'sqrt', None, None, None, None, 0.33,\n",
       "                    0.33, 0.33, 0.33, 'sqrt', 'sqrt', 'sqrt', 'sqrt', None,\n",
       "                    None, None, None, 0.33, 0.33, 0.33, 0.33, 'sqrt',\n",
       "                    'sqrt', 'sqrt', 'sqrt', None, None, None, None, 0.33,\n",
       "                    0.33, 0.33, 0.33, 'sqrt', 'sqrt', 'sqrt', 'sqrt'],\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",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'param_n_estimators': masked_array(data=[150, 250, 350, 450, 150, 250, 350, 450, 150, 250, 350,\n",
       "                    450, 150, 250, 350, 450, 150, 250, 350, 450, 150, 250,\n",
       "                    350, 450, 150, 250, 350, 450, 150, 250, 350, 450, 150,\n",
       "                    250, 350, 450, 150, 250, 350, 450, 150, 250, 350, 450,\n",
       "                    150, 250, 350, 450],\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",
       "        fill_value='?',\n",
       "             dtype=object),\n",
       " 'params': [{'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': True,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mse',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': None,\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 0.33,\n",
       "   'n_estimators': 450},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 150},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 250},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 350},\n",
       "  {'bootstrap': False,\n",
       "   'criterion': 'mae',\n",
       "   'max_features': 'sqrt',\n",
       "   'n_estimators': 450}],\n",
       " 'split0_test_Explained Variance': array([ 0.31617829,  0.3217243 ,  0.32477215,  0.32495129,  0.32467949,\n",
       "         0.33426938,  0.3274212 ,  0.32366113,  0.33638395,  0.33485854,\n",
       "         0.33479707,  0.32849091,  0.30444253,  0.29954724,  0.30029685,\n",
       "         0.29856281,  0.30418188,  0.31248637,  0.30668714,  0.30987091,\n",
       "         0.31572881,  0.32616027,  0.32431739,  0.32873708, -0.30358302,\n",
       "        -0.30275207, -0.30067704, -0.3009259 ,  0.29848377,  0.30134446,\n",
       "         0.31241242,  0.31215911,  0.35965023,  0.36166045,  0.35960616,\n",
       "         0.36061063, -0.04315726, -0.04218043, -0.03884073, -0.03905332,\n",
       "         0.28319959,  0.29387312,  0.30272   ,  0.30942835,  0.37627998,\n",
       "         0.35890812,  0.3511331 ,  0.35674148]),\n",
       " 'split1_test_Explained Variance': array([ 0.23206988,  0.23185833,  0.23272003,  0.2410998 ,  0.25521917,\n",
       "         0.25916189,  0.25618572,  0.25043268,  0.23291624,  0.22034425,\n",
       "         0.22139628,  0.22233916,  0.20289426,  0.21865758,  0.22013097,\n",
       "         0.22052488,  0.22643149,  0.23928386,  0.23573614,  0.22848981,\n",
       "         0.21437316,  0.20984611,  0.21255982,  0.2155123 , -0.25965095,\n",
       "        -0.25812486, -0.25891431, -0.25846249,  0.26981723,  0.26048235,\n",
       "         0.26821824,  0.26899097,  0.25230523,  0.25835637,  0.25561611,\n",
       "         0.2546115 , -0.35401472, -0.34992784, -0.35587632, -0.35287797,\n",
       "         0.24174027,  0.24277274,  0.24311732,  0.24681565,  0.24976152,\n",
       "         0.24429983,  0.24488509,  0.24371872]),\n",
       " 'split2_test_Explained Variance': array([ 0.26587477,  0.26421199,  0.26899946,  0.26875575,  0.23823632,\n",
       "         0.25567923,  0.25926293,  0.25917296,  0.25622978,  0.2480105 ,\n",
       "         0.24019564,  0.24136863,  0.24042651,  0.24574483,  0.24741346,\n",
       "         0.25355981,  0.23994859,  0.23964963,  0.23722634,  0.23869526,\n",
       "         0.2136663 ,  0.22125852,  0.23078803,  0.23453657, -0.39644366,\n",
       "        -0.39864961, -0.3981113 , -0.39969038,  0.26961059,  0.26406059,\n",
       "         0.27064182,  0.26845054,  0.24069901,  0.2545327 ,  0.25718401,\n",
       "         0.26173759, -0.11101041, -0.11276107, -0.11784052, -0.11583328,\n",
       "         0.19057953,  0.21432558,  0.2189318 ,  0.21793772,  0.23388666,\n",
       "         0.23623444,  0.24269716,  0.23894145]),\n",
       " 'split3_test_Explained Variance': array([ 0.3956285 ,  0.40493619,  0.40830121,  0.40613848,  0.40936491,\n",
       "         0.40888827,  0.40824687,  0.41287708,  0.37136226,  0.37869445,\n",
       "         0.38325605,  0.38163707,  0.36240649,  0.37486246,  0.37671385,\n",
       "         0.37432444,  0.37578137,  0.37265124,  0.37473003,  0.37482276,\n",
       "         0.34737317,  0.36300583,  0.36086522,  0.36261706, -0.04160543,\n",
       "        -0.0408086 , -0.04027621, -0.04126913,  0.43052187,  0.43481531,\n",
       "         0.4338683 ,  0.438062  ,  0.43867693,  0.42922745,  0.42666925,\n",
       "         0.42169062,  0.222608  ,  0.21341968,  0.21944041,  0.21793976,\n",
       "         0.41276944,  0.40819414,  0.40549277,  0.40414008,  0.39852074,\n",
       "         0.40850451,  0.40725271,  0.40718173]),\n",
       " 'split4_test_Explained Variance': array([ 0.23184933,  0.23641116,  0.24620109,  0.25093378,  0.2551103 ,\n",
       "         0.25932368,  0.2660681 ,  0.26491726,  0.24053711,  0.23995569,\n",
       "         0.24460309,  0.250766  ,  0.20470362,  0.21975513,  0.22099697,\n",
       "         0.22486267,  0.22672745,  0.22859777,  0.22495487,  0.21964825,\n",
       "         0.24034565,  0.23238897,  0.2332334 ,  0.24152281, -0.08693134,\n",
       "        -0.08566296, -0.08730681, -0.08537274,  0.26382171,  0.25944726,\n",
       "         0.2674085 ,  0.26733583,  0.28999463,  0.2870827 ,  0.28528974,\n",
       "         0.28043072, -0.01465073, -0.02755727, -0.02417577, -0.01798828,\n",
       "         0.23948882,  0.23864215,  0.23894411,  0.23777091,  0.25891785,\n",
       "         0.25356657,  0.25608673,  0.25657581]),\n",
       " 'mean_test_Explained Variance': array([ 0.2883575 ,  0.29186847,  0.29623709,  0.29841144,  0.29655978,\n",
       "         0.30350579,  0.30346912,  0.30224097,  0.28755141,  0.28444036,\n",
       "         0.28491658,  0.28497876,  0.26303027,  0.27175076,  0.27314686,\n",
       "         0.27439935,  0.27465379,  0.27857929,  0.27590822,  0.27435307,\n",
       "         0.26636368,  0.27060651,  0.27242243,  0.27665507, -0.21775808,\n",
       "        -0.2173143 , -0.21716923, -0.21725644,  0.30644035,  0.30402639,\n",
       "         0.31051241,  0.31100125,  0.31632336,  0.31823023,  0.31693034,\n",
       "         0.31587626, -0.06002239, -0.0637724 , -0.06342558, -0.06153245,\n",
       "         0.27356846,  0.27958073,  0.28186919,  0.28325368,  0.30357095,\n",
       "         0.30038125,  0.30047895,  0.30070705]),\n",
       " 'std_test_Explained Variance': array([0.06185067, 0.06495377, 0.06423441, 0.06116749, 0.06375339,\n",
       "        0.06039794, 0.05851785, 0.06100702, 0.05582178, 0.06137091,\n",
       "        0.0629425 , 0.06036632, 0.06181544, 0.05930428, 0.05939195,\n",
       "        0.05716906, 0.05815261, 0.05575639, 0.05727785, 0.05951119,\n",
       "        0.05504986, 0.06197809, 0.05892829, 0.05804184, 0.13351005,\n",
       "        0.13435906, 0.13381946, 0.13436615, 0.06316204, 0.0671764 ,\n",
       "        0.06392073, 0.06572501, 0.07393793, 0.06752932, 0.06659804,\n",
       "        0.06499796, 0.18494679, 0.18037892, 0.18461953, 0.18336556,\n",
       "        0.07548995, 0.06928761, 0.0678159 , 0.06772745, 0.06935491,\n",
       "        0.07008226, 0.06686371, 0.06852226]),\n",
       " 'rank_test_Explained Variance': array([20, 19, 18, 16, 17, 10, 11, 12, 21, 24, 23, 22, 40, 37, 35, 32, 31,\n",
       "        28, 30, 33, 39, 38, 36, 29, 48, 47, 45, 46,  7,  8,  6,  5,  3,  1,\n",
       "         2,  4, 41, 44, 43, 42, 34, 27, 26, 25,  9, 15, 14, 13]),\n",
       " 'split0_train_Explained Variance': array([0.90075457, 0.90291739, 0.90403103, 0.90367303, 0.89967616,\n",
       "        0.90145358, 0.90324767, 0.90263805, 0.89916276, 0.90054428,\n",
       "        0.90214588, 0.90160302, 0.89639247, 0.89904331, 0.89944209,\n",
       "        0.89943478, 0.89515015, 0.89639224, 0.89783084, 0.89784637,\n",
       "        0.8926772 , 0.89386233, 0.89455877, 0.89417793, 0.99994824,\n",
       "        0.99994824, 0.99994824, 0.99994824, 0.99994824, 0.99994824,\n",
       "        0.99994824, 0.99994824, 0.99994824, 0.99994824, 0.99994823,\n",
       "        0.99994824, 0.99994824, 0.99994824, 0.99994824, 0.99994824,\n",
       "        0.99994824, 0.99994824, 0.99994824, 0.99994824, 0.99994824,\n",
       "        0.99994824, 0.99994824, 0.99994824]),\n",
       " 'split1_train_Explained Variance': array([0.90280201, 0.90519232, 0.90592973, 0.90592671, 0.9055175 ,\n",
       "        0.90624176, 0.9070825 , 0.90694943, 0.90551005, 0.90649813,\n",
       "        0.90713581, 0.90763059, 0.89799053, 0.89975207, 0.90104139,\n",
       "        0.90191387, 0.90023757, 0.90223645, 0.90238322, 0.90240868,\n",
       "        0.8938772 , 0.89582594, 0.89684876, 0.89738269, 0.99994724,\n",
       "        0.99994724, 0.99994724, 0.99994724, 0.99994724, 0.99994724,\n",
       "        0.99994724, 0.99994724, 0.99994723, 0.99994723, 0.99994723,\n",
       "        0.99994723, 0.99994724, 0.99994724, 0.99994724, 0.99994724,\n",
       "        0.99994724, 0.99994724, 0.99994724, 0.99994724, 0.99994724,\n",
       "        0.99994724, 0.99994724, 0.99994724]),\n",
       " 'split2_train_Explained Variance': array([0.9044063 , 0.90560786, 0.90469576, 0.90526403, 0.90281886,\n",
       "        0.90513205, 0.90438657, 0.90500394, 0.90175167, 0.90426918,\n",
       "        0.90424705, 0.90571882, 0.89984568, 0.9022857 , 0.90196815,\n",
       "        0.90220992, 0.89923119, 0.90092978, 0.90068283, 0.90138986,\n",
       "        0.89109876, 0.89579326, 0.89553969, 0.89577801, 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        ]),\n",
       " 'split3_train_Explained Variance': array([0.89316464, 0.89610148, 0.89641915, 0.89656585, 0.89673119,\n",
       "        0.89848398, 0.89712562, 0.89702079, 0.89666642, 0.89870392,\n",
       "        0.89757236, 0.89689515, 0.88890102, 0.89114206, 0.89103386,\n",
       "        0.89166587, 0.8875213 , 0.88971364, 0.89029714, 0.89135562,\n",
       "        0.88377175, 0.88652461, 0.88560631, 0.88603413, 0.99994265,\n",
       "        0.99994265, 0.99994265, 0.99994265, 0.99994265, 0.99994265,\n",
       "        0.99994265, 0.99994265, 0.9999426 , 0.99994263, 0.99994264,\n",
       "        0.99994264, 0.99994265, 0.99994265, 0.99994265, 0.99994265,\n",
       "        0.99994265, 0.99994265, 0.99994265, 0.99994265, 0.99994265,\n",
       "        0.99994265, 0.99994265, 0.99994265]),\n",
       " 'split4_train_Explained Variance': array([0.90298079, 0.90717644, 0.90636433, 0.90552626, 0.90613686,\n",
       "        0.90944581, 0.90760648, 0.90690043, 0.90545219, 0.90833762,\n",
       "        0.90657912, 0.90638339, 0.90158891, 0.90383045, 0.90209921,\n",
       "        0.9019786 , 0.90127881, 0.90408477, 0.90185999, 0.90158986,\n",
       "        0.89788725, 0.89922271, 0.89845113, 0.89723163, 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.99999991, 0.99999992, 0.99999991,\n",
       "        0.99999995, 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        ]),\n",
       " 'mean_train_Explained Variance': array([0.90082166, 0.9033991 , 0.903488  , 0.90339118, 0.90217611,\n",
       "        0.90415144, 0.90388977, 0.90370253, 0.90170862, 0.90367063,\n",
       "        0.90353604, 0.90364619, 0.89694372, 0.89921072, 0.89911694,\n",
       "        0.89944061, 0.89668381, 0.89867138, 0.89861081, 0.89891808,\n",
       "        0.89186243, 0.89424577, 0.89420093, 0.89412088, 0.99996762,\n",
       "        0.99996762, 0.99996762, 0.99996762, 0.99996762, 0.99996762,\n",
       "        0.99996762, 0.99996762, 0.9999676 , 0.9999676 , 0.9999676 ,\n",
       "        0.99996761, 0.99996762, 0.99996762, 0.99996762, 0.99996762,\n",
       "        0.99996762, 0.99996762, 0.99996762, 0.99996762, 0.99996762,\n",
       "        0.99996762, 0.99996762, 0.99996762]),\n",
       " 'std_train_Explained Variance': array([4.00169392e-03, 3.89486247e-03, 3.63201222e-03, 3.49741546e-03,\n",
       "        3.55422206e-03, 3.81470639e-03, 3.75287336e-03, 3.69435734e-03,\n",
       "        3.47483053e-03, 3.59377538e-03, 3.47051039e-03, 3.93483378e-03,\n",
       "        4.38369347e-03, 4.38683726e-03, 4.15135629e-03, 4.01681746e-03,\n",
       "        5.03062850e-03, 5.14879275e-03, 4.44538096e-03, 4.09320865e-03,\n",
       "        4.62739615e-03, 4.22906192e-03, 4.49139341e-03, 4.20620850e-03,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65019848e-05, 2.65019848e-05,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65015529e-05, 2.65009397e-05,\n",
       "        2.64911723e-05, 2.64866686e-05, 2.64825671e-05, 2.64901042e-05,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65019848e-05, 2.65019848e-05,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65019848e-05, 2.65019848e-05,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65019848e-05, 2.65019848e-05]),\n",
       " 'split0_test_MAE': array([0.17216096, 0.17055433, 0.17025659, 0.17000671, 0.1714307 ,\n",
       "        0.1693203 , 0.17014748, 0.17002984, 0.16891607, 0.16924754,\n",
       "        0.16948958, 0.16986407, 0.17744222, 0.17844933, 0.17916143,\n",
       "        0.17930963, 0.17869889, 0.177866  , 0.17898619, 0.1776937 ,\n",
       "        0.17677111, 0.175736  , 0.17552857, 0.17442037, 0.20282   ,\n",
       "        0.20218133, 0.20226048, 0.20228111, 0.16428111, 0.16436933,\n",
       "        0.16327762, 0.16278593, 0.16131778, 0.160428  , 0.16060857,\n",
       "        0.16075556, 0.18741778, 0.18825733, 0.18786333, 0.1877937 ,\n",
       "        0.17209667, 0.17009733, 0.16982762, 0.16873074, 0.16298667,\n",
       "        0.16378933, 0.16545714, 0.16458148]),\n",
       " 'split1_test_MAE': array([0.18375179, 0.1830164 , 0.18276961, 0.18185721, 0.18009635,\n",
       "        0.17867785, 0.17929404, 0.18037918, 0.18066417, 0.18169168,\n",
       "        0.18245812, 0.18185472, 0.19034228, 0.1893651 , 0.19021285,\n",
       "        0.1898654 , 0.1880906 , 0.18586309, 0.18701151, 0.18718792,\n",
       "        0.18569016, 0.18650604, 0.1862047 , 0.18611969, 0.21864989,\n",
       "        0.21869396, 0.21876942, 0.21874236, 0.17515101, 0.17620336,\n",
       "        0.17513663, 0.17509955, 0.17657159, 0.17618054, 0.17660786,\n",
       "        0.17659955, 0.23373154, 0.23365168, 0.23445829, 0.23441536,\n",
       "        0.18386913, 0.18402013, 0.18362272, 0.18309769, 0.17894295,\n",
       "        0.17951409, 0.18028188, 0.18046607]),\n",
       " 'split2_test_MAE': array([0.17350112, 0.17442013, 0.1739674 , 0.17386503, 0.1759821 ,\n",
       "        0.17577987, 0.17521285, 0.17494705, 0.17343177, 0.17432215,\n",
       "        0.1754094 , 0.17586428, 0.17975839, 0.17894832, 0.17873394,\n",
       "        0.17831618, 0.18018121, 0.1796302 , 0.17974161, 0.17955145,\n",
       "        0.18279306, 0.18121946, 0.18040604, 0.17919351, 0.21390157,\n",
       "        0.2147396 , 0.2146721 , 0.21457345, 0.17186353, 0.17197181,\n",
       "        0.17075455, 0.17074198, 0.17442058, 0.17244161, 0.17196261,\n",
       "        0.17110067, 0.2010604 , 0.2012094 , 0.20155225, 0.20132289,\n",
       "        0.18490604, 0.18284161, 0.18155705, 0.1817308 , 0.17870022,\n",
       "        0.17699195, 0.17616012, 0.17653095]),\n",
       " 'split3_test_MAE': array([0.19559154, 0.19411477, 0.19376052, 0.1940223 , 0.19403098,\n",
       "        0.19412407, 0.1944651 , 0.19372477, 0.19924928, 0.19739841,\n",
       "        0.19664979, 0.19724096, 0.20555817, 0.20480671, 0.20430825,\n",
       "        0.20480313, 0.20319799, 0.20380805, 0.20390412, 0.20432923,\n",
       "        0.20368456, 0.2008    , 0.20171381, 0.20189485, 0.23347539,\n",
       "        0.23324564, 0.23302733, 0.23320321, 0.1836085 , 0.18360872,\n",
       "        0.18391946, 0.18364728, 0.18530089, 0.18629933, 0.1863164 ,\n",
       "        0.18688441, 0.22277069, 0.22379597, 0.22284564, 0.22344594,\n",
       "        0.19280313, 0.19394228, 0.19505034, 0.19539075, 0.19368904,\n",
       "        0.19272819, 0.19332502, 0.19281842]),\n",
       " 'split4_test_MAE': array([0.19099329, 0.19101611, 0.18949856, 0.18916406, 0.18833333,\n",
       "        0.18805369, 0.18775743, 0.18758613, 0.18773602, 0.18772483,\n",
       "        0.18721285, 0.18616406, 0.19782327, 0.19668054, 0.19574017,\n",
       "        0.19553169, 0.19576286, 0.19639933, 0.19655369, 0.19714169,\n",
       "        0.18927629, 0.19099195, 0.19118456, 0.19064653, 0.21687025,\n",
       "        0.21654497, 0.21662224, 0.21646831, 0.18315213, 0.18411275,\n",
       "        0.18327709, 0.18340567, 0.17959284, 0.18072483, 0.18064238,\n",
       "        0.18176957, 0.20321924, 0.20489799, 0.20460211, 0.20279567,\n",
       "        0.19008725, 0.1903651 , 0.1908092 , 0.19031991, 0.18547875,\n",
       "        0.18654765, 0.18648706, 0.18652274]),\n",
       " 'mean_test_MAE': array([0.18318494, 0.18260817, 0.18203473, 0.18176727, 0.18196056,\n",
       "        0.18117524, 0.18136033, 0.18131824, 0.18198192, 0.18205972,\n",
       "        0.18222685, 0.18218109, 0.19016778, 0.18963499, 0.18961729,\n",
       "        0.18955146, 0.18917225, 0.18869879, 0.18922568, 0.1891654 ,\n",
       "        0.18762846, 0.18703552, 0.18699215, 0.18643886, 0.21712422,\n",
       "        0.21706113, 0.21705046, 0.21703388, 0.17559607, 0.17603753,\n",
       "        0.17525699, 0.17511953, 0.17542181, 0.17519504, 0.17520797,\n",
       "        0.17540229, 0.20961014, 0.21033284, 0.2102343 , 0.20992501,\n",
       "        0.18473548, 0.18423432, 0.18415416, 0.18383371, 0.17993677,\n",
       "        0.17989263, 0.18032229, 0.18016302]),\n",
       " 'std_test_MAE': array([0.00928178, 0.0091216 , 0.00892128, 0.00901161, 0.00820603,\n",
       "        0.00884635, 0.00872674, 0.00851445, 0.0107443 , 0.00991935,\n",
       "        0.00940368, 0.0093254 , 0.01064071, 0.01019176, 0.00981354,\n",
       "        0.00999635, 0.00929531, 0.0099533 , 0.0096843 , 0.01021454,\n",
       "        0.00900867, 0.00857046, 0.00905589, 0.00952883, 0.00985364,\n",
       "        0.00992465, 0.00983481, 0.00989129, 0.00726576, 0.00742671,\n",
       "        0.00779368, 0.00791535, 0.00796431, 0.00873445, 0.00871189,\n",
       "        0.00903037, 0.01651036, 0.01628875, 0.01646176, 0.01672496,\n",
       "        0.00714111, 0.00818001, 0.00867371, 0.00905186, 0.01010845,\n",
       "        0.00977766, 0.0094473 , 0.00956753]),\n",
       " 'rank_test_MAE': array([25, 26, 30, 33, 32, 36, 34, 35, 31, 29, 27, 28,  9, 10, 11, 12, 14,\n",
       "        16, 13, 15, 17, 18, 19, 20,  1,  2,  3,  4, 42, 41, 45, 48, 43, 47,\n",
       "        46, 44,  8,  5,  6,  7, 21, 22, 23, 24, 39, 40, 37, 38]),\n",
       " 'split0_train_MAE': array([0.06928618, 0.0685629 , 0.06855146, 0.06837519, 0.06964192,\n",
       "        0.06929068, 0.06883892, 0.0690034 , 0.0693112 , 0.06898524,\n",
       "        0.06867212, 0.06873951, 0.07190464, 0.07154346, 0.07173646,\n",
       "        0.07167711, 0.07234704, 0.07184497, 0.07172591, 0.07159349,\n",
       "        0.07262668, 0.07231628, 0.0723406 , 0.07226342, 0.00016779,\n",
       "        0.00016779, 0.00016779, 0.00016779, 0.00016779, 0.00016779,\n",
       "        0.00016779, 0.00016779, 0.00016779, 0.00016779, 0.00016922,\n",
       "        0.0001689 , 0.00016779, 0.00016779, 0.00016779, 0.00016779,\n",
       "        0.00016779, 0.00016779, 0.00016779, 0.00016779, 0.00016779,\n",
       "        0.00016779, 0.00016779, 0.00016779]),\n",
       " 'split1_train_MAE': array([0.06846608, 0.06755604, 0.06757931, 0.06758314, 0.06730813,\n",
       "        0.06684744, 0.06686092, 0.0669133 , 0.0668629 , 0.06690399,\n",
       "        0.06669341, 0.06671741, 0.07203908, 0.0712005 , 0.07087413,\n",
       "        0.07066332, 0.07053769, 0.06999213, 0.07019466, 0.07041746,\n",
       "        0.07179955, 0.07132864, 0.07120627, 0.07114126, 0.0001675 ,\n",
       "        0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 ,\n",
       "        0.0001675 , 0.0001675 , 0.00016862, 0.00016817, 0.00016894,\n",
       "        0.00016862, 0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 ,\n",
       "        0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 ,\n",
       "        0.0001675 , 0.0001675 , 0.0001675 ]),\n",
       " 'split2_train_MAE': array([6.79369068e-02, 6.78123953e-02, 6.82206269e-02, 6.80647683e-02,\n",
       "        6.85220547e-02, 6.80288107e-02, 6.82866715e-02, 6.83934487e-02,\n",
       "        6.90848688e-02, 6.82974874e-02, 6.81263460e-02, 6.76318630e-02,\n",
       "        7.15745394e-02, 7.06525963e-02, 7.06996889e-02, 7.08101619e-02,\n",
       "        7.15812395e-02, 7.14045226e-02, 7.13743719e-02, 7.12892239e-02,\n",
       "        7.27682859e-02, 7.16227806e-02, 7.18898062e-02, 7.19379304e-02,\n",
       "        9.71491638e-15, 1.26293913e-14, 2.19203029e-14, 2.79396830e-14,\n",
       "        9.71640412e-15, 1.26308790e-14, 2.19217906e-14, 2.79396830e-14,\n",
       "        9.71789186e-15, 1.26323668e-14, 9.57166808e-07, 7.44463084e-07,\n",
       "        9.72681827e-15, 1.26353422e-14, 2.19247661e-14, 2.79411707e-14,\n",
       "        9.71491638e-15, 1.26293913e-14, 2.19203029e-14, 2.79396830e-14,\n",
       "        9.71789186e-15, 1.26323668e-14, 2.19232784e-14, 2.79396830e-14]),\n",
       " 'split3_train_MAE': array([0.06766629, 0.06728894, 0.06695166, 0.06688456, 0.06669101,\n",
       "        0.06635091, 0.06658877, 0.0666056 , 0.06621441, 0.06570403,\n",
       "        0.06614068, 0.06621682, 0.07060553, 0.07047789, 0.07041015,\n",
       "        0.07028438, 0.07057203, 0.07046097, 0.07029636, 0.07004941,\n",
       "        0.07085902, 0.06991156, 0.06984207, 0.06981965, 0.0001675 ,\n",
       "        0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 ,\n",
       "        0.0001675 , 0.0001675 , 0.00017197, 0.00017018, 0.00016942,\n",
       "        0.00016899, 0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 ,\n",
       "        0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 , 0.0001675 ,\n",
       "        0.0001675 , 0.0001675 , 0.0001675 ]),\n",
       " 'split4_train_MAE': array([6.76063652e-02, 6.64294807e-02, 6.62481455e-02, 6.62897822e-02,\n",
       "        6.64349525e-02, 6.56184255e-02, 6.57702800e-02, 6.60396427e-02,\n",
       "        6.65628141e-02, 6.58619765e-02, 6.61402249e-02, 6.57472548e-02,\n",
       "        6.87526521e-02, 6.88264657e-02, 6.92586743e-02, 6.92460450e-02,\n",
       "        6.92141262e-02, 6.90276382e-02, 6.93267528e-02, 6.93652522e-02,\n",
       "        6.93520380e-02, 6.91951424e-02, 6.93826274e-02, 6.95828215e-02,\n",
       "        9.79525413e-15, 1.25356639e-14, 2.19292293e-14, 2.80884566e-14,\n",
       "        9.79525413e-15, 1.25356639e-14, 9.57166808e-07, 1.48892614e-06,\n",
       "        8.93355668e-06, 7.37018427e-06, 8.13591771e-06, 6.70016753e-06,\n",
       "        9.79525413e-15, 1.25356639e-14, 2.19292293e-14, 2.80884566e-14,\n",
       "        9.79525413e-15, 1.25356639e-14, 2.19292293e-14, 2.80884566e-14,\n",
       "        9.79525413e-15, 1.25356639e-14, 2.19292293e-14, 2.80884566e-14]),\n",
       " 'mean_train_MAE': array([0.06819236, 0.06752995, 0.06751024, 0.06743949, 0.06771961,\n",
       "        0.06722725, 0.06726911, 0.06739108, 0.06760724, 0.06715054,\n",
       "        0.06715456, 0.06701057, 0.07097529, 0.07054018, 0.07059582,\n",
       "        0.0705362 , 0.07085042, 0.07054605, 0.07058361, 0.07054297,\n",
       "        0.07148111, 0.07087488, 0.07093227, 0.07094902, 0.00010056,\n",
       "        0.00010056, 0.00010056, 0.00010056, 0.00010056, 0.00010056,\n",
       "        0.00010075, 0.00010086, 0.00010346, 0.0001027 , 0.00010333,\n",
       "        0.00010279, 0.00010056, 0.00010056, 0.00010056, 0.00010056,\n",
       "        0.00010056, 0.00010056, 0.00010056, 0.00010056, 0.00010056,\n",
       "        0.00010056, 0.00010056, 0.00010056]),\n",
       " 'std_train_MAE': array([6.25581038e-04, 6.95206860e-04, 8.36476111e-04, 7.63795926e-04,\n",
       "        1.20140476e-03, 1.29620758e-03, 1.12922236e-03, 1.12035194e-03,\n",
       "        1.31694680e-03, 1.30347164e-03, 1.05041345e-03, 1.06650747e-03,\n",
       "        1.21912236e-03, 9.37648514e-04, 8.01465354e-04, 7.90037010e-04,\n",
       "        1.06082206e-03, 1.00437068e-03, 8.65290197e-04, 8.13248452e-04,\n",
       "        1.26415985e-03, 1.14824949e-03, 1.14588739e-03, 1.08485222e-03,\n",
       "        8.21059168e-05, 8.21059168e-05, 8.21059168e-05, 8.21059168e-05,\n",
       "        8.21059168e-05, 8.21059168e-05, 8.18720195e-05, 8.17425622e-05,\n",
       "        8.08908511e-05, 8.08852862e-05, 8.06925103e-05, 8.09124709e-05,\n",
       "        8.21059168e-05, 8.21059168e-05, 8.21059168e-05, 8.21059168e-05,\n",
       "        8.21059168e-05, 8.21059168e-05, 8.21059168e-05, 8.21059168e-05,\n",
       "        8.21059168e-05, 8.21059168e-05, 8.21059168e-05, 8.21059168e-05]),\n",
       " 'split0_test_MSE': array([0.04692836, 0.04651508, 0.04622431, 0.04617642, 0.04610486,\n",
       "        0.0455021 , 0.04599822, 0.04621828, 0.04518418, 0.04530889,\n",
       "        0.04530995, 0.04564713, 0.04891003, 0.04934537, 0.04942389,\n",
       "        0.0495041 , 0.04891198, 0.04832393, 0.04877986, 0.04846841,\n",
       "        0.04723913, 0.04643744, 0.04653455, 0.04619358, 0.08700721,\n",
       "        0.08697459, 0.0868606 , 0.08688737, 0.04725641, 0.04702594,\n",
       "        0.0462422 , 0.04626837, 0.04327535, 0.04322718, 0.04337264,\n",
       "        0.04328361, 0.07038607, 0.07040379, 0.0701618 , 0.07017575,\n",
       "        0.04960244, 0.04882619, 0.04827978, 0.04780631, 0.04257265,\n",
       "        0.04370847, 0.04433186, 0.04393894]),\n",
       " 'split1_test_MSE': array([0.05688285, 0.05686184, 0.05683839, 0.05624957, 0.05518104,\n",
       "        0.05483197, 0.05509838, 0.05549924, 0.05674344, 0.05758771,\n",
       "        0.05753949, 0.05744679, 0.05999313, 0.05882314, 0.05888491,\n",
       "        0.05887493, 0.05833115, 0.05722224, 0.05751814, 0.05803126,\n",
       "        0.05831857, 0.0586221 , 0.05846961, 0.05828008, 0.092052  ,\n",
       "        0.0919334 , 0.09199388, 0.09195941, 0.05383742, 0.05452827,\n",
       "        0.053944  , 0.05388723, 0.05493689, 0.05453899, 0.0547191 ,\n",
       "        0.05478968, 0.10240501, 0.10209827, 0.10264193, 0.10237156,\n",
       "        0.05660531, 0.05661557, 0.05648651, 0.05619788, 0.0555135 ,\n",
       "        0.05585975, 0.05585358, 0.05595506]),\n",
       " 'split2_test_MSE': array([0.05259683, 0.05276733, 0.05239846, 0.05243166, 0.05454005,\n",
       "        0.05336577, 0.05310552, 0.05312214, 0.05323733, 0.05380536,\n",
       "        0.05437305, 0.05429982, 0.05488042, 0.05448729, 0.05437287,\n",
       "        0.05398946, 0.05477097, 0.05484763, 0.05504408, 0.05492154,\n",
       "        0.05640318, 0.05588001, 0.05522747, 0.05498508, 0.10019043,\n",
       "        0.10033181, 0.10032117, 0.10042613, 0.05221555, 0.05259649,\n",
       "        0.05213147, 0.05229167, 0.0542539 , 0.05326172, 0.05307402,\n",
       "        0.05274461, 0.07946613, 0.07958549, 0.07994838, 0.07981137,\n",
       "        0.05818279, 0.05646246, 0.05611107, 0.05623317, 0.05483294,\n",
       "        0.05462968, 0.05417986, 0.05445196]),\n",
       " 'split3_test_MSE': array([0.05918703, 0.05828136, 0.05796034, 0.05817123, 0.05784631,\n",
       "        0.0579212 , 0.05799162, 0.05754968, 0.06155874, 0.06082989,\n",
       "        0.0603871 , 0.06054511, 0.06269132, 0.06165021, 0.06146554,\n",
       "        0.0617276 , 0.06129945, 0.06169583, 0.06154322, 0.06158618,\n",
       "        0.06389901, 0.06237171, 0.06259118, 0.06243198, 0.10197475,\n",
       "        0.10189966, 0.10184639, 0.10194191, 0.05575205, 0.05533217,\n",
       "        0.05542587, 0.05501712, 0.05495999, 0.05588218, 0.05613091,\n",
       "        0.05662032, 0.07708983, 0.07812886, 0.0775259 , 0.07768467,\n",
       "        0.05790991, 0.05834278, 0.05861546, 0.058758  , 0.05897353,\n",
       "        0.05800625, 0.05813013, 0.05813192]),\n",
       " 'split4_test_MSE': array([0.06303912, 0.06275731, 0.06189623, 0.06148595, 0.06111652,\n",
       "        0.06080495, 0.06027576, 0.06035928, 0.0620282 , 0.06207811,\n",
       "        0.06169299, 0.06113622, 0.06580193, 0.06463411, 0.06451314,\n",
       "        0.0642145 , 0.06436024, 0.06425141, 0.06440315, 0.06476177,\n",
       "        0.06209438, 0.0627879 , 0.06270048, 0.06208156, 0.08899837,\n",
       "        0.08888102, 0.08902229, 0.08885267, 0.06031626, 0.06071396,\n",
       "        0.06009156, 0.06009924, 0.05793544, 0.05825132, 0.05836461,\n",
       "        0.05872327, 0.08330518, 0.08436393, 0.08403779, 0.08348232,\n",
       "        0.06285318, 0.0629268 , 0.06292978, 0.06293867, 0.06082794,\n",
       "        0.0611913 , 0.06096638, 0.06089933]),\n",
       " 'mean_test_MSE': array([0.05571504, 0.05542462, 0.0550517 , 0.05489127, 0.05494589,\n",
       "        0.05447316, 0.05448251, 0.05453856, 0.05573621, 0.05590777,\n",
       "        0.05584637, 0.05580138, 0.05844257, 0.0577767 , 0.05772093,\n",
       "        0.05765118, 0.0575232 , 0.05725622, 0.05744606, 0.05754165,\n",
       "        0.05757698, 0.05720538, 0.05709049, 0.05678024, 0.09403512,\n",
       "        0.09399467, 0.09399928, 0.09400395, 0.05386667, 0.05402997,\n",
       "        0.0535572 , 0.05350301, 0.05305918, 0.05301913, 0.05311918,\n",
       "        0.05321896, 0.08251416, 0.08289929, 0.08284613, 0.08268834,\n",
       "        0.05702077, 0.05662429, 0.05647352, 0.0563753 , 0.05452806,\n",
       "        0.05466438, 0.05467848, 0.05466105]),\n",
       " 'std_test_MSE': array([0.00555519, 0.0054912 , 0.0053627 , 0.00526147, 0.00500459,\n",
       "        0.00518026, 0.00490827, 0.00480445, 0.00620619, 0.00603967,\n",
       "        0.0058544 , 0.00564823, 0.0059757 , 0.00539097, 0.00532603,\n",
       "        0.00531471, 0.0053608 , 0.00555984, 0.0054106 , 0.00562595,\n",
       "        0.00582518, 0.00596836, 0.00598394, 0.0059684 , 0.00599515,\n",
       "        0.0060401 , 0.0060232 , 0.00609439, 0.00428356, 0.00442558,\n",
       "        0.00451892, 0.00447157, 0.00507025, 0.00518242, 0.00518843,\n",
       "        0.00536171, 0.01079149, 0.01059166, 0.01087358, 0.01075372,\n",
       "        0.00428039, 0.00455588, 0.00477154, 0.00495101, 0.00638955,\n",
       "        0.0059296 , 0.00566809, 0.00579984]),\n",
       " 'rank_test_MSE': array([29, 30, 31, 33, 32, 40, 39, 37, 28, 25, 26, 27,  9, 10, 11, 12, 15,\n",
       "        17, 16, 14, 13, 18, 19, 21,  1,  4,  3,  2, 42, 41, 43, 44, 47, 48,\n",
       "        46, 45,  8,  5,  6,  7, 20, 22, 23, 24, 38, 35, 34, 36]),\n",
       " 'split0_train_MSE': array([8.12954575e-03, 7.95883945e-03, 7.87289023e-03, 7.89348662e-03,\n",
       "        8.22291343e-03, 8.08237331e-03, 7.93429392e-03, 7.97770350e-03,\n",
       "        8.22035236e-03, 8.10810049e-03, 7.98029149e-03, 8.02173049e-03,\n",
       "        8.58992249e-03, 8.38071747e-03, 8.36213115e-03, 8.35467757e-03,\n",
       "        8.68842016e-03, 8.58853409e-03, 8.46637852e-03, 8.45776592e-03,\n",
       "        8.79559000e-03, 8.69694646e-03, 8.64384596e-03, 8.66251205e-03,\n",
       "        4.19463087e-06, 4.19463087e-06, 4.19463087e-06, 4.19463087e-06,\n",
       "        4.19463087e-06, 4.19463087e-06, 4.19463087e-06, 4.19463087e-06,\n",
       "        4.19463087e-06, 4.19463087e-06, 4.19483632e-06, 4.19475516e-06,\n",
       "        4.19463087e-06, 4.19463087e-06, 4.19463087e-06, 4.19463087e-06,\n",
       "        4.19463087e-06, 4.19463087e-06, 4.19463087e-06, 4.19463087e-06,\n",
       "        4.19463087e-06, 4.19463087e-06, 4.19463087e-06, 4.19463087e-06]),\n",
       " 'split1_train_MSE': array([7.80628595e-03, 7.61582376e-03, 7.56334596e-03, 7.56332596e-03,\n",
       "        7.58667261e-03, 7.51705740e-03, 7.45996369e-03, 7.46746720e-03,\n",
       "        7.55279966e-03, 7.47905786e-03, 7.43470437e-03, 7.39709568e-03,\n",
       "        8.33810469e-03, 8.18164159e-03, 8.08652725e-03, 8.01098389e-03,\n",
       "        8.10861241e-03, 7.93915623e-03, 7.93252797e-03, 7.93653206e-03,\n",
       "        8.50220677e-03, 8.34918047e-03, 8.27528696e-03, 8.23641971e-03,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06,\n",
       "        4.18797692e-06, 4.18773869e-06, 4.18794654e-06, 4.18781149e-06,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06]),\n",
       " 'split2_train_MSE': array([7.71095794e-03, 7.62201461e-03, 7.70128698e-03, 7.66112559e-03,\n",
       "        7.82299814e-03, 7.65210090e-03, 7.71482549e-03, 7.67463391e-03,\n",
       "        7.90491401e-03, 7.69832174e-03, 7.70179031e-03, 7.58781099e-03,\n",
       "        8.20763419e-03, 7.99251283e-03, 8.02468202e-03, 8.01991531e-03,\n",
       "        8.22969886e-03, 8.09454762e-03, 8.12304448e-03, 8.07369030e-03,\n",
       "        8.76225600e-03, 8.39290338e-03, 8.41818801e-03, 8.40720691e-03,\n",
       "        1.13006637e-28, 2.28855552e-28, 5.68130489e-28, 9.56810317e-28,\n",
       "        1.13018529e-28, 2.28912371e-28, 5.68208450e-28, 9.56810317e-28,\n",
       "        1.13030422e-28, 2.29026009e-28, 1.36738112e-10, 8.27181173e-11,\n",
       "        1.13129525e-28, 2.29090757e-28, 5.68367015e-28, 9.56877707e-28,\n",
       "        1.13006637e-28, 2.28855552e-28, 5.68130489e-28, 9.56810317e-28,\n",
       "        1.13030422e-28, 2.28912371e-28, 5.68208450e-28, 9.56810317e-28]),\n",
       " 'split3_train_MSE': array([7.89477387e-03, 7.68513022e-03, 7.66141362e-03, 7.65514649e-03,\n",
       "        7.63176154e-03, 7.50055471e-03, 7.59865533e-03, 7.61330035e-03,\n",
       "        7.60994507e-03, 7.45711503e-03, 7.54324379e-03, 7.59295435e-03,\n",
       "        8.29138298e-03, 8.15281626e-03, 8.15690252e-03, 8.11796721e-03,\n",
       "        8.38172543e-03, 8.23941732e-03, 8.19654792e-03, 8.12620144e-03,\n",
       "        8.55639024e-03, 8.35065712e-03, 8.41815680e-03, 8.39249006e-03,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06,\n",
       "        4.19132701e-06, 4.18894472e-06, 4.18828838e-06, 4.18801828e-06,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06,\n",
       "        4.18760469e-06, 4.18760469e-06, 4.18760469e-06, 4.18760469e-06]),\n",
       " 'split4_train_MSE': array([7.59700149e-03, 7.28511893e-03, 7.33082026e-03, 7.39323529e-03,\n",
       "        7.34861213e-03, 7.08755464e-03, 7.21687957e-03, 7.27486252e-03,\n",
       "        7.38389131e-03, 7.15571819e-03, 7.28423430e-03, 7.29513407e-03,\n",
       "        7.78146548e-03, 7.62819424e-03, 7.76232831e-03, 7.77425275e-03,\n",
       "        7.82021910e-03, 7.61570863e-03, 7.76840933e-03, 7.79724305e-03,\n",
       "        7.97063684e-03, 7.87499054e-03, 7.93970707e-03, 8.03381129e-03,\n",
       "        1.14568502e-28, 2.26274906e-28, 5.67480372e-28, 9.60600021e-28,\n",
       "        1.14568502e-28, 2.26274906e-28, 1.36738112e-10, 3.30872469e-10,\n",
       "        6.70016750e-09, 6.03015075e-09, 6.90527467e-09, 4.21862398e-09,\n",
       "        1.14568502e-28, 2.26274906e-28, 5.67480372e-28, 9.60600021e-28,\n",
       "        1.14568502e-28, 2.26274906e-28, 5.67480372e-28, 9.60600021e-28,\n",
       "        1.14568502e-28, 2.26274906e-28, 5.67480372e-28, 9.60600021e-28]),\n",
       " 'mean_train_MSE': array([7.82771300e-03, 7.63338539e-03, 7.62595141e-03, 7.63326399e-03,\n",
       "        7.72259157e-03, 7.56792819e-03, 7.58492360e-03, 7.60159350e-03,\n",
       "        7.73438048e-03, 7.57966266e-03, 7.58885285e-03, 7.57894512e-03,\n",
       "        8.24170197e-03, 8.06717648e-03, 8.07851425e-03, 8.05555935e-03,\n",
       "        8.24573520e-03, 8.09547278e-03, 8.09738164e-03, 8.07828656e-03,\n",
       "        8.51741597e-03, 8.33293559e-03, 8.33903696e-03, 8.34648801e-03,\n",
       "        2.51396805e-06, 2.51396805e-06, 2.51396805e-06, 2.51396805e-06,\n",
       "        2.51396805e-06, 2.51396805e-06, 2.51399540e-06, 2.51403423e-06,\n",
       "        2.51612699e-06, 2.51546889e-06, 2.51562265e-06, 2.51497725e-06,\n",
       "        2.51396805e-06, 2.51396805e-06, 2.51396805e-06, 2.51396805e-06,\n",
       "        2.51396805e-06, 2.51396805e-06, 2.51396805e-06, 2.51396805e-06,\n",
       "        2.51396805e-06, 2.51396805e-06, 2.51396805e-06, 2.51396805e-06]),\n",
       " 'std_train_MSE': array([1.80509743e-04, 2.14627633e-04, 1.78288327e-04, 1.62157968e-04,\n",
       "        2.92228718e-04, 3.19256823e-04, 2.40912462e-04, 2.33098125e-04,\n",
       "        2.95482242e-04, 3.15656357e-04, 2.38584776e-04, 2.48978587e-04,\n",
       "        2.63057965e-04, 2.51803278e-04, 1.94688951e-04, 1.87487241e-04,\n",
       "        2.87953847e-04, 3.22005855e-04, 2.37508206e-04, 2.21540669e-04,\n",
       "        2.95988229e-04, 2.63207778e-04, 2.31931795e-04, 2.07632331e-04,\n",
       "        2.05264792e-06, 2.05264792e-06, 2.05264792e-06, 2.05264792e-06,\n",
       "        2.05264792e-06, 2.05264792e-06, 2.05261443e-06, 2.05256688e-06,\n",
       "        2.05167593e-06, 2.05141215e-06, 2.05112499e-06, 2.05171625e-06,\n",
       "        2.05264792e-06, 2.05264792e-06, 2.05264792e-06, 2.05264792e-06,\n",
       "        2.05264792e-06, 2.05264792e-06, 2.05264792e-06, 2.05264792e-06,\n",
       "        2.05264792e-06, 2.05264792e-06, 2.05264792e-06, 2.05264792e-06]),\n",
       " 'split0_test_MedAE': array([0.1455    , 0.13996667, 0.14292857, 0.14172222, 0.14880556,\n",
       "        0.138     , 0.14257143, 0.143125  , 0.1455    , 0.145     ,\n",
       "        0.14871429, 0.14538889, 0.14433333, 0.1477    , 0.148     ,\n",
       "        0.151     , 0.159     , 0.1528    , 0.162     , 0.15905556,\n",
       "        0.16275   , 0.15895   , 0.15417857, 0.15225   , 0.12366667,\n",
       "        0.1225    , 0.12278571, 0.12433333, 0.13516667, 0.13345   ,\n",
       "        0.13071429, 0.12583333, 0.133     , 0.1295    , 0.12607143,\n",
       "        0.12938889, 0.12575   , 0.1269    , 0.12642857, 0.12555556,\n",
       "        0.13766667, 0.1417    , 0.1405    , 0.14088889, 0.14033333,\n",
       "        0.1393    , 0.14071429, 0.14133333]),\n",
       " 'split1_test_MedAE': array([0.16266667, 0.16      , 0.16157143, 0.15703704, 0.15966667,\n",
       "        0.1566    , 0.15828571, 0.15755556, 0.15777778, 0.1586    ,\n",
       "        0.16378571, 0.15933333, 0.15933333, 0.1598    , 0.16457143,\n",
       "        0.16988889, 0.16366667, 0.1646    , 0.16192857, 0.16577778,\n",
       "        0.16816667, 0.1623    , 0.16535714, 0.166     , 0.164     ,\n",
       "        0.1604    , 0.16114286, 0.16477778, 0.151     , 0.1536    ,\n",
       "        0.14985714, 0.14977778, 0.15433333, 0.1531    , 0.15571429,\n",
       "        0.15127778, 0.15166667, 0.1534    , 0.15771429, 0.15644444,\n",
       "        0.15866667, 0.1602    , 0.156     , 0.15366667, 0.153     ,\n",
       "        0.1465    , 0.14692857, 0.15822222]),\n",
       " 'split2_test_MedAE': array([0.14433333, 0.144     , 0.14671429, 0.14433333, 0.15      ,\n",
       "        0.1488    , 0.14885714, 0.14711111, 0.14      , 0.1388    ,\n",
       "        0.13942857, 0.14244444, 0.15866667, 0.1388    , 0.13885714,\n",
       "        0.14533333, 0.14266667, 0.1433    , 0.14314286, 0.14044444,\n",
       "        0.14733333, 0.1392    , 0.14592857, 0.14355556, 0.10766667,\n",
       "        0.1142    , 0.10842857, 0.10811111, 0.117     , 0.1214    ,\n",
       "        0.11928571, 0.12155556, 0.13233333, 0.1286    , 0.12785714,\n",
       "        0.12588889, 0.12133333, 0.1232    , 0.12614286, 0.125     ,\n",
       "        0.14533333, 0.1396    , 0.13542857, 0.13822222, 0.134     ,\n",
       "        0.138     , 0.13171429, 0.12844444]),\n",
       " 'split3_test_MedAE': array([0.187     , 0.1812    , 0.17985714, 0.1845    , 0.175     ,\n",
       "        0.1742    , 0.18042857, 0.17483333, 0.176     , 0.1792    ,\n",
       "        0.17814286, 0.17888889, 0.199     , 0.2006    , 0.202     ,\n",
       "        0.20011111, 0.18933333, 0.1974    , 0.19714286, 0.19888889,\n",
       "        0.18366667, 0.1862    , 0.18792857, 0.19      , 0.156     ,\n",
       "        0.1552    , 0.15514286, 0.15477778, 0.16866667, 0.1566    ,\n",
       "        0.16892857, 0.15805556, 0.16966667, 0.1704    , 0.16921429,\n",
       "        0.16744444, 0.17766667, 0.1888    , 0.183     , 0.18188889,\n",
       "        0.18666667, 0.1874    , 0.18828571, 0.18855556, 0.18      ,\n",
       "        0.185     , 0.18671429, 0.18744444]),\n",
       " 'split4_test_MedAE': array([0.14766667, 0.1556    , 0.15385714, 0.16255556, 0.14433333,\n",
       "        0.1484    , 0.15257143, 0.14877778, 0.15433333, 0.1514    ,\n",
       "        0.14285714, 0.144     , 0.16666667, 0.1546    , 0.153     ,\n",
       "        0.15522222, 0.14733333, 0.1529    , 0.15671429, 0.15388889,\n",
       "        0.15833333, 0.1592    , 0.15028571, 0.151     , 0.15      ,\n",
       "        0.15      , 0.15014286, 0.15      , 0.14433333, 0.1472    ,\n",
       "        0.145     , 0.14444444, 0.14033333, 0.1442    , 0.14142857,\n",
       "        0.14211111, 0.138     , 0.138     , 0.139     , 0.14211111,\n",
       "        0.14966667, 0.1478    , 0.14742857, 0.15355556, 0.135     ,\n",
       "        0.1492    , 0.147     , 0.14188889]),\n",
       " 'mean_test_MedAE': array([0.15741734, 0.15613164, 0.15696687, 0.15800777, 0.15555206,\n",
       "        0.15317962, 0.15652413, 0.1542656 , 0.15470986, 0.15458713,\n",
       "        0.15457784, 0.15399955, 0.16557149, 0.16028311, 0.16126791,\n",
       "        0.16429327, 0.16039812, 0.1621874 , 0.16418278, 0.163605  ,\n",
       "        0.16404826, 0.16116702, 0.16072692, 0.16054997, 0.14024441,\n",
       "        0.14043592, 0.13950613, 0.14037846, 0.14322252, 0.14243794,\n",
       "        0.142741  , 0.13991443, 0.145916  , 0.14513901, 0.14403303,\n",
       "        0.14320368, 0.14286037, 0.14603432, 0.14643029, 0.14617233,\n",
       "        0.15557596, 0.15532172, 0.15351111, 0.15495889, 0.14845576,\n",
       "        0.15158351, 0.15060101, 0.15145308]),\n",
       " 'std_test_MedAE': array([0.01618749, 0.01450857, 0.0130935 , 0.01533541, 0.01092572,\n",
       "        0.01205465, 0.01299064, 0.01130704, 0.01235775, 0.01394641,\n",
       "        0.01442349, 0.01381882, 0.01820504, 0.02133471, 0.02197542,\n",
       "        0.01965348, 0.01632912, 0.01884181, 0.01784693, 0.01948731,\n",
       "        0.01195517, 0.01495193, 0.01504014, 0.01639857, 0.02117878,\n",
       "        0.01853585, 0.02034836, 0.02094113, 0.01708871, 0.01319533,\n",
       "        0.01694325, 0.01401603, 0.01426309, 0.01562368, 0.01650422,\n",
       "        0.01512875, 0.02033725, 0.02380709, 0.02159206, 0.02131106,\n",
       "        0.01695277, 0.01755728, 0.01869504, 0.01794226, 0.01714735,\n",
       "        0.01721978, 0.01888566, 0.0203096 ]),\n",
       " 'rank_test_MedAE': array([14, 17, 15, 13, 19, 28, 16, 25, 22, 23, 24, 26,  1, 12,  7,  2, 11,\n",
       "         6,  3,  5,  4,  8,  9, 10, 46, 44, 48, 45, 39, 43, 42, 47, 36, 37,\n",
       "        38, 40, 41, 35, 33, 34, 18, 20, 27, 21, 32, 29, 31, 30]),\n",
       " 'split0_train_MedAE': array([5.60000000e-02, 5.63000000e-02, 5.71785714e-02, 5.72777778e-02,\n",
       "        5.59166667e-02, 5.79000000e-02, 5.72142857e-02, 5.75555556e-02,\n",
       "        5.65000000e-02, 5.76000000e-02, 5.72142857e-02, 5.76666667e-02,\n",
       "        6.11666667e-02, 6.12000000e-02, 6.15714286e-02, 6.11666667e-02,\n",
       "        6.11666667e-02, 6.08000000e-02, 5.88571429e-02, 6.16666667e-02,\n",
       "        6.07500000e-02, 5.96500000e-02, 6.12857143e-02, 5.97500000e-02,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14]),\n",
       " 'split1_train_MedAE': array([5.96666667e-02, 5.98000000e-02, 5.78571429e-02, 5.73333333e-02,\n",
       "        5.60000000e-02, 5.70000000e-02, 5.68571429e-02, 5.56666667e-02,\n",
       "        5.53333333e-02, 5.60000000e-02, 5.60000000e-02, 5.64000000e-02,\n",
       "        6.43333333e-02, 6.16000000e-02, 6.10714286e-02, 6.06666667e-02,\n",
       "        6.13333333e-02, 5.96000000e-02, 6.21428571e-02, 6.33333333e-02,\n",
       "        6.10000000e-02, 5.92000000e-02, 5.95714286e-02, 6.05555556e-02,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14]),\n",
       " 'split2_train_MedAE': array([5.93333333e-02, 6.10000000e-02, 6.04285714e-02, 5.96666667e-02,\n",
       "        5.90000000e-02, 5.96000000e-02, 5.97142857e-02, 6.07777778e-02,\n",
       "        5.86666667e-02, 5.94000000e-02, 5.80000000e-02, 5.83333333e-02,\n",
       "        6.36666667e-02, 6.43000000e-02, 6.34285714e-02, 6.37222222e-02,\n",
       "        6.33333333e-02, 6.40000000e-02, 6.30714286e-02, 6.30000000e-02,\n",
       "        6.30000000e-02, 6.17000000e-02, 6.29285714e-02, 6.25555556e-02,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14]),\n",
       " 'split3_train_MedAE': array([5.36666667e-02, 5.56000000e-02, 5.44285714e-02, 5.35555556e-02,\n",
       "        5.63333333e-02, 5.48000000e-02, 5.69285714e-02, 5.61111111e-02,\n",
       "        5.20000000e-02, 5.24000000e-02, 5.32857143e-02, 5.32222222e-02,\n",
       "        5.96666667e-02, 5.86000000e-02, 5.80000000e-02, 5.87777778e-02,\n",
       "        6.13333333e-02, 5.90000000e-02, 6.08571429e-02, 6.03333333e-02,\n",
       "        5.78333333e-02, 5.94000000e-02, 5.74285714e-02, 5.85555556e-02,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14]),\n",
       " 'split4_train_MedAE': array([5.76666667e-02, 5.70000000e-02, 5.61428571e-02, 5.67777778e-02,\n",
       "        5.76666667e-02, 5.90000000e-02, 5.81428571e-02, 5.81111111e-02,\n",
       "        5.73333333e-02, 5.56000000e-02, 5.61428571e-02, 5.51111111e-02,\n",
       "        5.90000000e-02, 6.18000000e-02, 6.17142857e-02, 6.24444444e-02,\n",
       "        6.23333333e-02, 6.29000000e-02, 6.11428571e-02, 6.30000000e-02,\n",
       "        6.16666667e-02, 5.88000000e-02, 6.00000000e-02, 5.98888889e-02,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14]),\n",
       " 'mean_train_MedAE': array([5.72666667e-02, 5.79400000e-02, 5.72071429e-02, 5.69222222e-02,\n",
       "        5.69833333e-02, 5.76600000e-02, 5.77714286e-02, 5.76444444e-02,\n",
       "        5.59666667e-02, 5.62000000e-02, 5.61285714e-02, 5.61466667e-02,\n",
       "        6.15666667e-02, 6.15000000e-02, 6.11571429e-02, 6.13555556e-02,\n",
       "        6.19000000e-02, 6.12600000e-02, 6.12142857e-02, 6.22666667e-02,\n",
       "        6.08500000e-02, 5.97500000e-02, 6.02428571e-02, 6.02611111e-02,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14,\n",
       "        9.76996262e-15, 1.24344979e-14, 2.48689958e-14, 3.19744231e-14]),\n",
       " 'std_train_MedAE': array([0.00222511, 0.00209151, 0.00198263, 0.00195871, 0.00118837,\n",
       "        0.00168713, 0.00107419, 0.00180589, 0.00226176, 0.00232551,\n",
       "        0.00159872, 0.00183198, 0.00211765, 0.00181328, 0.00176728,\n",
       "        0.00166985, 0.00082731, 0.00191165, 0.00141421, 0.00112349,\n",
       "        0.00169837, 0.0010139 , 0.00182924, 0.00131605, 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "        0.        , 0.        , 0.        ]),\n",
       " 'split0_test_R2': array([ 0.28868768,  0.2949519 ,  0.29935919,  0.3000851 ,  0.30116977,\n",
       "         0.31030601,  0.30278607,  0.29945061,  0.31512489,  0.31323462,\n",
       "         0.3132185 ,  0.30810776,  0.25865063,  0.25205207,  0.25086182,\n",
       "         0.24964606,  0.25862111,  0.26753444,  0.26062374,  0.2653445 ,\n",
       "         0.2839772 ,  0.29612862,  0.29465675,  0.29982497, -0.31880387,\n",
       "        -0.31830949, -0.31658167, -0.31698741,  0.28371528,  0.28720851,\n",
       "         0.29908805,  0.29869136,  0.34405782,  0.34478788,  0.34258318,\n",
       "         0.34393259, -0.06687052, -0.06713905, -0.06347111, -0.06368254,\n",
       "         0.24815555,  0.25992144,  0.26820357,  0.27538021,  0.35470896,\n",
       "         0.33749285,  0.32804377,  0.33399952]),\n",
       " 'split1_test_R2': array([ 0.22145159,  0.22173918,  0.22206005,  0.23011917,  0.24474398,\n",
       "         0.24952174,  0.24587541,  0.24038882,  0.22335965,  0.21180421,\n",
       "         0.21246424,  0.21373298,  0.17888161,  0.19489513,  0.19404957,\n",
       "         0.19418628,  0.20162883,  0.21680643,  0.21275647,  0.2057334 ,\n",
       "         0.20180107,  0.19764669,  0.19973378,  0.20232788, -0.25990413,\n",
       "        -0.25828084, -0.25910868, -0.25863694,  0.26313404,  0.25367846,\n",
       "         0.26167524,  0.26245225,  0.24808572,  0.25353165,  0.2510665 ,\n",
       "         0.25010057, -0.40160455, -0.39740615, -0.40484718, -0.40114667,\n",
       "         0.22525027,  0.22510986,  0.22687628,  0.23082661,  0.24019368,\n",
       "         0.23545467,  0.23553904,  0.23415007]),\n",
       " 'split2_test_R2': array([ 0.26372998,  0.26134328,  0.2665068 ,  0.26604209,  0.23652801,\n",
       "         0.25296598,  0.25660907,  0.25637641,  0.25476404,  0.2468125 ,\n",
       "         0.23886577,  0.23989091,  0.23176344,  0.23726664,  0.23886828,\n",
       "         0.24423536,  0.23329545,  0.23222243,  0.22947238,  0.23118773,\n",
       "         0.21044724,  0.21777079,  0.22690533,  0.23029838, -0.40250305,\n",
       "        -0.40448207, -0.40433316, -0.40580243,  0.2690672 ,  0.26373464,\n",
       "         0.27024417,  0.26800175,  0.24053372,  0.25442263,  0.25705001,\n",
       "         0.26166123, -0.1123965 , -0.11406745, -0.11914721, -0.11722936,\n",
       "         0.18553564,  0.20961736,  0.21453632,  0.21282703,  0.23242797,\n",
       "         0.23527336,  0.24157005,  0.23776108]),\n",
       " 'split3_test_R2': array([ 0.39543296,  0.40468395,  0.40796307,  0.40580896,  0.40912783,\n",
       "         0.40836284,  0.40764349,  0.41215774,  0.37120717,  0.37865195,\n",
       "         0.38317482,  0.38156085,  0.35963839,  0.37027281,  0.37215913,\n",
       "         0.36948231,  0.37385569,  0.36980687,  0.37136566,  0.37092679,\n",
       "         0.34730235,  0.36290307,  0.36066128,  0.36228741, -0.04162295,\n",
       "        -0.04085595, -0.04031183, -0.04128749,  0.43051964,  0.43480854,\n",
       "         0.43385139,  0.43802667,  0.4386102 ,  0.4291905 ,  0.42664975,\n",
       "         0.42165067,  0.21256457,  0.20195138,  0.20811031,  0.2064885 ,\n",
       "         0.4084782 ,  0.40405662,  0.40127129,  0.39981536,  0.3976138 ,\n",
       "         0.40749413,  0.40622871,  0.40621045]),\n",
       " 'split4_test_R2': array([ 0.22247386,  0.22594971,  0.23657025,  0.24163069,  0.24618721,\n",
       "         0.25003019,  0.25655716,  0.25552707,  0.23494257,  0.23432702,\n",
       "         0.23907702,  0.24594433,  0.18839726,  0.20280117,  0.20429321,\n",
       "         0.2079767 ,  0.20617907,  0.20752143,  0.20564993,  0.20122671,\n",
       "         0.23412636,  0.22557244,  0.22665064,  0.23428446, -0.09770821,\n",
       "        -0.09626082, -0.09800321, -0.09591115,  0.25605766,  0.25115245,\n",
       "         0.25882916,  0.25873435,  0.28542277,  0.28152665,  0.28012937,\n",
       "         0.27570567, -0.02748824, -0.04054691, -0.03652434, -0.02967308,\n",
       "         0.22476728,  0.22385916,  0.22382251,  0.22371283,  0.24974665,\n",
       "         0.24526489,  0.24803906,  0.24886613]),\n",
       " 'mean_test_R2': array([ 0.27836906,  0.28175132,  0.28650912,  0.28875242,  0.28756962,\n",
       "         0.29425889,  0.29390616,  0.29278907,  0.27992691,  0.27701468,\n",
       "         0.27740814,  0.27788793,  0.24348662,  0.25145836,  0.25204481,\n",
       "         0.2531007 ,  0.25472126,  0.25879006,  0.25597987,  0.25489785,\n",
       "         0.25556897,  0.26005275,  0.2617657 ,  0.26585022, -0.22423538,\n",
       "        -0.22376474, -0.22379226, -0.2238501 ,  0.30047627,  0.2981019 ,\n",
       "         0.30473003,  0.30517258,  0.3113859 ,  0.31273489,  0.31153744,\n",
       "         0.31065481, -0.07914258, -0.08341978, -0.08314949, -0.08102535,\n",
       "         0.2584236 ,  0.26450673,  0.26694368,  0.26852162,  0.29501833,\n",
       "         0.2922567 ,  0.2919326 ,  0.29225349]),\n",
       " 'std_test_R2': array([0.06381534, 0.06691721, 0.06623521, 0.063212  , 0.0649401 ,\n",
       "        0.06150813, 0.06014027, 0.06281274, 0.05552517, 0.06101254,\n",
       "        0.06264781, 0.06037053, 0.06486996, 0.06302205, 0.06359089,\n",
       "        0.06183318, 0.06295386, 0.05911884, 0.06068753, 0.06229998,\n",
       "        0.05402705, 0.06121179, 0.0585537 , 0.05785347, 0.13512123,\n",
       "        0.13596883, 0.13555409, 0.13610489, 0.06560365, 0.069476  ,\n",
       "        0.06606808, 0.06785985, 0.07338328, 0.06700055, 0.0660363 ,\n",
       "        0.06433626, 0.1963373 , 0.19121869, 0.19592021, 0.19449349,\n",
       "        0.07762468, 0.07166317, 0.06960511, 0.06896971, 0.06790374,\n",
       "        0.0692666 , 0.06631759, 0.06773141]),\n",
       " 'rank_test_R2': array([21, 19, 18, 16, 17, 10, 11, 12, 20, 24, 23, 22, 40, 39, 38, 37, 36,\n",
       "        31, 33, 35, 34, 30, 29, 27, 48, 45, 46, 47,  7,  8,  6,  5,  3,  1,\n",
       "         2,  4, 41, 44, 43, 42, 32, 28, 26, 25,  9, 13, 15, 14]),\n",
       " 'split0_train_R2': array([0.89967971, 0.90178626, 0.90284689, 0.90259273, 0.89852753,\n",
       "        0.90026182, 0.90208916, 0.90155347, 0.89855913, 0.89994435,\n",
       "        0.90152154, 0.90101017, 0.89399856, 0.8965802 , 0.89680955,\n",
       "        0.89690153, 0.89278308, 0.89401569, 0.89552312, 0.8956294 ,\n",
       "        0.89146058, 0.89267786, 0.89333313, 0.89310279, 0.99994824,\n",
       "        0.99994824, 0.99994824, 0.99994824, 0.99994824, 0.99994824,\n",
       "        0.99994824, 0.99994824, 0.99994824, 0.99994824, 0.99994823,\n",
       "        0.99994824, 0.99994824, 0.99994824, 0.99994824, 0.99994824,\n",
       "        0.99994824, 0.99994824, 0.99994824, 0.99994824, 0.99994824,\n",
       "        0.99994824, 0.99994824, 0.99994824]),\n",
       " 'split1_train_R2': array([0.90164165, 0.90404146, 0.90470267, 0.90470292, 0.90440876,\n",
       "        0.9052859 , 0.90600528, 0.90591073, 0.90483555, 0.90576469,\n",
       "        0.90632354, 0.90679741, 0.8949408 , 0.89691221, 0.89811064,\n",
       "        0.89906248, 0.89783237, 0.8999675 , 0.90005102, 0.90000057,\n",
       "        0.89287313, 0.89480124, 0.89573229, 0.89622202, 0.99994724,\n",
       "        0.99994724, 0.99994724, 0.99994724, 0.99994724, 0.99994724,\n",
       "        0.99994724, 0.99994724, 0.99994723, 0.99994723, 0.99994723,\n",
       "        0.99994723, 0.99994724, 0.99994724, 0.99994724, 0.99994724,\n",
       "        0.99994724, 0.99994724, 0.99994724, 0.99994724, 0.99994724,\n",
       "        0.99994724, 0.99994724, 0.99994724]),\n",
       " 'split2_train_R2': array([0.90335552, 0.90447029, 0.90347673, 0.90398009, 0.90195128,\n",
       "        0.9040932 , 0.90330705, 0.90381079, 0.9009246 , 0.9035139 ,\n",
       "        0.90347043, 0.90489897, 0.89713048, 0.89982669, 0.8994235 ,\n",
       "        0.89948324, 0.89685394, 0.89854784, 0.89819068, 0.89880926,\n",
       "        0.89017919, 0.89480843, 0.89449153, 0.89462916, 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        ]),\n",
       " 'split3_train_R2': array([0.89187289, 0.89474418, 0.895069  , 0.89515484, 0.89547512,\n",
       "        0.89727213, 0.89592854, 0.89572796, 0.89577392, 0.89786708,\n",
       "        0.89668746, 0.89600662, 0.88644092, 0.88833873, 0.88828277,\n",
       "        0.88881603, 0.88520359, 0.88715264, 0.88773978, 0.88870325,\n",
       "        0.88281137, 0.8856291 , 0.88470462, 0.88505615, 0.99994265,\n",
       "        0.99994265, 0.99994265, 0.99994265, 0.99994265, 0.99994265,\n",
       "        0.99994265, 0.99994265, 0.9999426 , 0.99994263, 0.99994264,\n",
       "        0.99994264, 0.99994265, 0.99994265, 0.99994265, 0.99994265,\n",
       "        0.99994265, 0.99994265, 0.99994265, 0.99994265, 0.99994265,\n",
       "        0.99994265, 0.99994265, 0.99994265]),\n",
       " 'split4_train_R2': array([0.90178188, 0.90581407, 0.90522322, 0.90441628, 0.90499319,\n",
       "        0.90836829, 0.9066963 , 0.90594667, 0.90453709, 0.90748703,\n",
       "        0.90582551, 0.90568459, 0.89939703, 0.90137861, 0.89964445,\n",
       "        0.89949028, 0.89889601, 0.90154003, 0.89956583, 0.89919305,\n",
       "        0.89695133, 0.89818789, 0.8973512 , 0.89613457, 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 0.99999991, 0.99999992, 0.99999991,\n",
       "        0.99999995, 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        , 1.        , 1.        ,\n",
       "        1.        , 1.        , 1.        ]),\n",
       " 'mean_train_R2': array([0.89966633, 0.90217125, 0.9022637 , 0.90216937, 0.90107118,\n",
       "        0.90305627, 0.90280527, 0.90258993, 0.90092606, 0.90291541,\n",
       "        0.90276569, 0.90287955, 0.89438156, 0.89660729, 0.89645418,\n",
       "        0.89675071, 0.8943138 , 0.89624474, 0.89621409, 0.89646711,\n",
       "        0.89085512, 0.89322091, 0.89312256, 0.89302894, 0.99996762,\n",
       "        0.99996762, 0.99996762, 0.99996762, 0.99996762, 0.99996762,\n",
       "        0.99996762, 0.99996762, 0.9999676 , 0.9999676 , 0.9999676 ,\n",
       "        0.99996761, 0.99996762, 0.99996762, 0.99996762, 0.99996762,\n",
       "        0.99996762, 0.99996762, 0.99996762, 0.99996762, 0.99996762,\n",
       "        0.99996762, 0.99996762, 0.99996762]),\n",
       " 'std_train_R2': array([4.06756595e-03, 3.93352150e-03, 3.69547147e-03, 3.58131571e-03,\n",
       "        3.60783186e-03, 3.88662256e-03, 3.83234040e-03, 3.79332947e-03,\n",
       "        3.47765882e-03, 3.56900927e-03, 3.49314218e-03, 3.95038236e-03,\n",
       "        4.38690690e-03, 4.50760888e-03, 4.21038660e-03, 4.08162193e-03,\n",
       "        5.00336636e-03, 5.19303723e-03, 4.52005284e-03, 4.15646422e-03,\n",
       "        4.62085342e-03, 4.18614563e-03, 4.41525151e-03, 4.14661694e-03,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65019848e-05, 2.65019848e-05,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65015529e-05, 2.65009397e-05,\n",
       "        2.64911723e-05, 2.64866686e-05, 2.64825671e-05, 2.64901042e-05,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65019848e-05, 2.65019848e-05,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65019848e-05, 2.65019848e-05,\n",
       "        2.65019848e-05, 2.65019848e-05, 2.65019848e-05, 2.65019848e-05])}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.cv_results_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rf_opt = pd.DataFrame(gs.cv_results_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>split0_test_Explained Variance</th>\n",
       "      <th>split1_test_Explained Variance</th>\n",
       "      <th>split2_test_Explained Variance</th>\n",
       "      <th>split3_test_Explained Variance</th>\n",
       "      <th>split4_test_Explained Variance</th>\n",
       "      <th>mean_test_Explained Variance</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_R2</th>\n",
       "      <th>std_test_R2</th>\n",
       "      <th>rank_test_R2</th>\n",
       "      <th>split0_train_R2</th>\n",
       "      <th>split1_train_R2</th>\n",
       "      <th>split2_train_R2</th>\n",
       "      <th>split3_train_R2</th>\n",
       "      <th>split4_train_R2</th>\n",
       "      <th>mean_train_R2</th>\n",
       "      <th>std_train_R2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.00</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>29.006633</td>\n",
       "      <td>0.473384</td>\n",
       "      <td>0.520930</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>0.241464</td>\n",
       "      <td>0.147872</td>\n",
       "      <td>0.161230</td>\n",
       "      <td>0.345928</td>\n",
       "      <td>0.196991</td>\n",
       "      <td>0.218727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.207211</td>\n",
       "      <td>0.081223</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.948791</td>\n",
       "      <td>0.950535</td>\n",
       "      <td>0.950076</td>\n",
       "      <td>0.945523</td>\n",
       "      <td>0.951136</td>\n",
       "      <td>0.949212</td>\n",
       "      <td>0.002062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>48.169482</td>\n",
       "      <td>0.791739</td>\n",
       "      <td>0.004781</td>\n",
       "      <td>0.009593</td>\n",
       "      <td>0.195287</td>\n",
       "      <td>0.206666</td>\n",
       "      <td>0.198278</td>\n",
       "      <td>0.129948</td>\n",
       "      <td>0.115541</td>\n",
       "      <td>0.165789</td>\n",
       "      <td>...</td>\n",
       "      <td>0.166348</td>\n",
       "      <td>0.040031</td>\n",
       "      <td>14.00</td>\n",
       "      <td>0.051763</td>\n",
       "      <td>0.050031</td>\n",
       "      <td>0.050529</td>\n",
       "      <td>0.055104</td>\n",
       "      <td>0.049450</td>\n",
       "      <td>0.051370</td>\n",
       "      <td>0.002081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.278843</td>\n",
       "      <td>0.009263</td>\n",
       "      <td>0.519184</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>-0.303583</td>\n",
       "      <td>-0.355876</td>\n",
       "      <td>-0.399690</td>\n",
       "      <td>-0.041605</td>\n",
       "      <td>-0.087307</td>\n",
       "      <td>-0.217758</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.224235</td>\n",
       "      <td>0.054027</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.891461</td>\n",
       "      <td>0.892873</td>\n",
       "      <td>0.890179</td>\n",
       "      <td>0.882811</td>\n",
       "      <td>0.896135</td>\n",
       "      <td>0.890855</td>\n",
       "      <td>0.000026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.127368</td>\n",
       "      <td>0.045110</td>\n",
       "      <td>0.519770</td>\n",
       "      <td>0.000486</td>\n",
       "      <td>0.299301</td>\n",
       "      <td>0.215228</td>\n",
       "      <td>0.218683</td>\n",
       "      <td>0.362909</td>\n",
       "      <td>0.223896</td>\n",
       "      <td>0.272255</td>\n",
       "      <td>...</td>\n",
       "      <td>0.254316</td>\n",
       "      <td>0.062183</td>\n",
       "      <td>12.75</td>\n",
       "      <td>0.898121</td>\n",
       "      <td>0.901244</td>\n",
       "      <td>0.900650</td>\n",
       "      <td>0.891109</td>\n",
       "      <td>0.901721</td>\n",
       "      <td>0.898937</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.984434</td>\n",
       "      <td>0.108583</td>\n",
       "      <td>0.520282</td>\n",
       "      <td>0.000631</td>\n",
       "      <td>0.314108</td>\n",
       "      <td>0.232818</td>\n",
       "      <td>0.240072</td>\n",
       "      <td>0.389442</td>\n",
       "      <td>0.239722</td>\n",
       "      <td>0.283847</td>\n",
       "      <td>...</td>\n",
       "      <td>0.272768</td>\n",
       "      <td>0.065820</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.951398</td>\n",
       "      <td>0.953372</td>\n",
       "      <td>0.952449</td>\n",
       "      <td>0.948905</td>\n",
       "      <td>0.954184</td>\n",
       "      <td>0.951512</td>\n",
       "      <td>0.001752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>35.386309</td>\n",
       "      <td>0.515254</td>\n",
       "      <td>0.520665</td>\n",
       "      <td>0.000896</td>\n",
       "      <td>0.330120</td>\n",
       "      <td>0.249929</td>\n",
       "      <td>0.256468</td>\n",
       "      <td>0.408600</td>\n",
       "      <td>0.257161</td>\n",
       "      <td>0.301091</td>\n",
       "      <td>...</td>\n",
       "      <td>0.292390</td>\n",
       "      <td>0.069508</td>\n",
       "      <td>36.25</td>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.999947</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999943</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.004071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>220.712829</td>\n",
       "      <td>3.571031</td>\n",
       "      <td>0.553179</td>\n",
       "      <td>0.067141</td>\n",
       "      <td>0.376280</td>\n",
       "      <td>0.269817</td>\n",
       "      <td>0.270642</td>\n",
       "      <td>0.438677</td>\n",
       "      <td>0.289995</td>\n",
       "      <td>0.318230</td>\n",
       "      <td>...</td>\n",
       "      <td>0.312735</td>\n",
       "      <td>0.196337</td>\n",
       "      <td>48.00</td>\n",
       "      <td>0.999948</td>\n",
       "      <td>0.999947</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999943</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999968</td>\n",
       "      <td>0.005193</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "count      48.000000     48.000000        48.000000       48.000000   \n",
       "mean       29.006633      0.473384         0.520930        0.002095   \n",
       "std        48.169482      0.791739         0.004781        0.009593   \n",
       "min         0.278843      0.009263         0.519184        0.000273   \n",
       "25%         2.127368      0.045110         0.519770        0.000486   \n",
       "50%         5.984434      0.108583         0.520282        0.000631   \n",
       "75%        35.386309      0.515254         0.520665        0.000896   \n",
       "max       220.712829      3.571031         0.553179        0.067141   \n",
       "\n",
       "       split0_test_Explained Variance  split1_test_Explained Variance  \\\n",
       "count                       48.000000                       48.000000   \n",
       "mean                         0.241464                        0.147872   \n",
       "std                          0.195287                        0.206666   \n",
       "min                         -0.303583                       -0.355876   \n",
       "25%                          0.299301                        0.215228   \n",
       "50%                          0.314108                        0.232818   \n",
       "75%                          0.330120                        0.249929   \n",
       "max                          0.376280                        0.269817   \n",
       "\n",
       "       split2_test_Explained Variance  split3_test_Explained Variance  \\\n",
       "count                       48.000000                       48.000000   \n",
       "mean                         0.161230                        0.345928   \n",
       "std                          0.198278                        0.129948   \n",
       "min                         -0.399690                       -0.041605   \n",
       "25%                          0.218683                        0.362909   \n",
       "50%                          0.240072                        0.389442   \n",
       "75%                          0.256468                        0.408600   \n",
       "max                          0.270642                        0.438677   \n",
       "\n",
       "       split4_test_Explained Variance  mean_test_Explained Variance  ...  \\\n",
       "count                       48.000000                     48.000000  ...   \n",
       "mean                         0.196991                      0.218727  ...   \n",
       "std                          0.115541                      0.165789  ...   \n",
       "min                         -0.087307                     -0.217758  ...   \n",
       "25%                          0.223896                      0.272255  ...   \n",
       "50%                          0.239722                      0.283847  ...   \n",
       "75%                          0.257161                      0.301091  ...   \n",
       "max                          0.289995                      0.318230  ...   \n",
       "\n",
       "       mean_test_R2  std_test_R2  rank_test_R2  split0_train_R2  \\\n",
       "count     48.000000    48.000000         48.00        48.000000   \n",
       "mean       0.207211     0.081223         24.50         0.948791   \n",
       "std        0.166348     0.040031         14.00         0.051763   \n",
       "min       -0.224235     0.054027          1.00         0.891461   \n",
       "25%        0.254316     0.062183         12.75         0.898121   \n",
       "50%        0.272768     0.065820         24.50         0.951398   \n",
       "75%        0.292390     0.069508         36.25         0.999948   \n",
       "max        0.312735     0.196337         48.00         0.999948   \n",
       "\n",
       "       split1_train_R2  split2_train_R2  split3_train_R2  split4_train_R2  \\\n",
       "count        48.000000        48.000000        48.000000        48.000000   \n",
       "mean          0.950535         0.950076         0.945523         0.951136   \n",
       "std           0.050031         0.050529         0.055104         0.049450   \n",
       "min           0.892873         0.890179         0.882811         0.896135   \n",
       "25%           0.901244         0.900650         0.891109         0.901721   \n",
       "50%           0.953372         0.952449         0.948905         0.954184   \n",
       "75%           0.999947         1.000000         0.999943         1.000000   \n",
       "max           0.999947         1.000000         0.999943         1.000000   \n",
       "\n",
       "       mean_train_R2  std_train_R2  \n",
       "count      48.000000     48.000000  \n",
       "mean        0.949212      0.002062  \n",
       "std         0.051370      0.002081  \n",
       "min         0.890855      0.000026  \n",
       "25%         0.898937      0.000027  \n",
       "50%         0.951512      0.001752  \n",
       "75%         0.999968      0.004071  \n",
       "max         0.999968      0.005193  \n",
       "\n",
       "[8 rows x 79 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rf_opt.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=False, criterion='mse', max_depth=None,\n",
       "           max_features='sqrt', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=250, n_jobs=4,\n",
       "           oob_score=False, random_state=42, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'bootstrap': False,\n",
       " 'criterion': 'mse',\n",
       " 'max_features': 'sqrt',\n",
       " 'n_estimators': 250}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs.best_index_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time',\n",
       "       'param_bootstrap', 'param_criterion', 'param_max_features',\n",
       "       'param_n_estimators', 'params', 'split0_test_Explained Variance',\n",
       "       'split1_test_Explained Variance', 'split2_test_Explained Variance',\n",
       "       'split3_test_Explained Variance', 'split4_test_Explained Variance',\n",
       "       'mean_test_Explained Variance', 'std_test_Explained Variance',\n",
       "       'rank_test_Explained Variance', 'split0_train_Explained Variance',\n",
       "       'split1_train_Explained Variance', 'split2_train_Explained Variance',\n",
       "       'split3_train_Explained Variance', 'split4_train_Explained Variance',\n",
       "       'mean_train_Explained Variance', 'std_train_Explained Variance',\n",
       "       'split0_test_MAE', 'split1_test_MAE', 'split2_test_MAE',\n",
       "       'split3_test_MAE', 'split4_test_MAE', 'mean_test_MAE', 'std_test_MAE',\n",
       "       'rank_test_MAE', 'split0_train_MAE', 'split1_train_MAE',\n",
       "       'split2_train_MAE', 'split3_train_MAE', 'split4_train_MAE',\n",
       "       'mean_train_MAE', 'std_train_MAE', 'split0_test_MSE', 'split1_test_MSE',\n",
       "       'split2_test_MSE', 'split3_test_MSE', 'split4_test_MSE',\n",
       "       'mean_test_MSE', 'std_test_MSE', 'rank_test_MSE', 'split0_train_MSE',\n",
       "       'split1_train_MSE', 'split2_train_MSE', 'split3_train_MSE',\n",
       "       'split4_train_MSE', 'mean_train_MSE', 'std_train_MSE',\n",
       "       'split0_test_MedAE', 'split1_test_MedAE', 'split2_test_MedAE',\n",
       "       'split3_test_MedAE', 'split4_test_MedAE', 'mean_test_MedAE',\n",
       "       'std_test_MedAE', 'rank_test_MedAE', 'split0_train_MedAE',\n",
       "       'split1_train_MedAE', 'split2_train_MedAE', 'split3_train_MedAE',\n",
       "       'split4_train_MedAE', 'mean_train_MedAE', 'std_train_MedAE',\n",
       "       'split0_test_R2', 'split1_test_R2', 'split2_test_R2', 'split3_test_R2',\n",
       "       'split4_test_R2', 'mean_test_R2', 'std_test_R2', 'rank_test_R2',\n",
       "       'split0_train_R2', 'split1_train_R2', 'split2_train_R2',\n",
       "       'split3_train_R2', 'split4_train_R2', 'mean_train_R2', 'std_train_R2'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rf_opt.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a1d59c780>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kSco5g50kSZIk5ZzBTpIkSZJyzmAnSZIkSTlnsJMkSZKknDPYSZIkSVLOGewkSZIkKecMdpIkSZKUcwY7SZIkSco5g50kSZIk5ZzBTpIkSZJyzmAnSZIkSTlnsJMkSZKknMsk2EXEkRHxXEQsjYgL6pi+bUTcXpz+54joksVyJUmSJEkZBLuIaAVcBxwF9AQmRETPWrN9Hvh7SukTwNXAZY1driRJkiSpIIszdkOApSmlF1JKHwIzgHG15hkH3FzsvxMYExGRwbIlSZIkaasXKaXGFYgYDxyZUjqjOPxZ4KCU0jnV5nmmOM+y4vBfi/O8VavWFGAKwD777HPgSy+91Ki2SZKUpS4X/GaDcZU/OLrRdbKo0ZRtkcqxOfZ//w+ppYqIeSmlQaXMm8UZu7rOvNVOi6XMQ0rp+pTSoJTSoI4dO2bQNEmSJElq+bIIdsuAvasNdwZeq2+eiGgNtAPezmDZkiRJkrTVyyLYPQl0i4iuEbENcAowq9Y8s4DTiv3jgT+kxl4DKkmSJEkCoHVjC6SUPoqIc4D7gFbAjSmlZyPiEmBuSmkW8FPglohYSuFM3SmNXa4kSZIkqaDRwQ4gpXQvcG+tcRdV6/8AOCmLZUmSJEmSasrkB8olSZIkSU3HYCdJkiRJOWewkyRJkqScM9hJkiRJUs4Z7CRJkiQp5wx2kiRJkpRzBjtJkiRJyjmDnSRJkiTlnMFOkiRJknKudVM3QJIkSVuPyh8c3dRNyFRLWx/ll8FOkiRJuZJFmDKQqaXxUkxJkiRJyjmDnSRJkiTlnMFOkiRJknLOYCdJkiRJOWewkyRJkqScM9hJkiRJUs4Z7CRJkiQp5wx2kiRJkpRzBjtJkiRJyjmDnSRJkiTlnMFOkiRJknKudVM3oBxr1qxh2bJlfPDBB03dFKlJtG3bls6dO9OmTZumbookSZKakVwFu2XLlrHTTjvRpUsXIqKpmyNtUSklVqxYwbJly+jatWtTN0eSJEnNSK4uxfzggw9o3769oU5bpYigffv2nrGWJEnSBnIV7ABDnbZq7v+SJEmqS+6CnSRJkiSpplzdY1dblwt+k2m9yh8cnWk9SZIkSdoSPGOXQz/60Y9YvXp1g147c+ZMFi5cuNF5Jk2aRNeuXenfvz/9+/fn4IMPbtCy5syZwzHHHLPReebOnct5553XoPq1TZ8+nXPOOafGuMrKSjp37sy6detqjO/fvz9PPPFEybWzbKckSZKUNYNdDm3uYAdwxRVXsGDBAhYsWMCjjz7aoGWVYtCgQVxzzTWbrX6XLl3Ye++9+dOf/lQ1bvHixaxatYohQ4aUVOOjjz7a7O2UJEmSGsNgV4bKykp69OjBGWecQe/evZk4cSIPPPAAw4cPp1u3blVngN577z1OP/10Bg8ezIABA7jnnnuqXj9ixAgGDhzIwIEDqwLTnDlzGDVqFOPHj6dHjx5MnDiRlFKdbbjmmmt47bXXGD16NKNHjwbg/vvvZ9iwYQwcOJCTTjqJd999F4ALLriAnj170rdvX84//3weffRRZs2axde//nX69+/PX//617LW/7zzzuOSSy4B4L777uPQQw9l3bp1TJo0iS9+8YuMGDGC/fffn1//+tcbvPaJJ57g4IMPZsCAARx88ME899xzVeu+/qze1KlTOf300xk1ahQf//jHawSpn//85wwZMoT+/fvzhS98gbVr1wJw0003sf/++zNy5EgeeeSROts9YcIEZsyYUTU8Y8YMJkyYAMD//M//cNBBBzFgwADGjh3Lm2++WdWWKVOmcPjhh/O5z32uRjvrW5fp06dzwgkncOSRR9KtWze+8Y1vVC3zd7/7HQMHDqRfv36MGTMGqH8/kSRJksqV63vsmsLSpUu54447uP766xk8eDC33XYbDz/8MLNmzeJ73/seM2fO5Lvf/S6f/OQnufHGG3nnnXcYMmQIY8eOpVOnTvz+97+nbdu2PP/880yYMIG5c+cCMH/+fJ599ln23HNPhg8fziOPPMIhhxyywfLPO+88rrrqKmbPnk2HDh146623uPTSS3nggQfYYYcduOyyy7jqqqs455xzuPvuu1m8eDERwTvvvMMuu+zCscceyzHHHMP48eM3up5f//rXufTSSwHo1asXt956Kz/4wQ8YPHgwI0aM4LzzzuPee++loqLw3UBlZSV//OMf+etf/8ro0aNZunRpjXo9evTgoYceonXr1jzwwAN8+9vf5q677tpguYsXL2b27NmsWrWK7t2786UvfYmlS5dy++2388gjj9CmTRvOOussbr31Vg477DAuvvhi5s2bR7t27Rg9ejQDBgzYoOanP/1pBgwYwLRp02jdujW33347d9xxBwCHHHIIjz/+OBHBDTfcwOWXX84Pf/hDAObNm8fDDz/Mdtttx5w5c0palwULFjB//ny23XZbunfvzrnnnkvbtm0588wzeeihh+jatStvv/02QL37yQ477LDR90aSJEmqzWBXpq5du9KnTx+gEHjGjBlDRNCnTx8qKyuBwhm0WbNmceWVVwKF3997+eWX2XPPPTnnnHNYsGABrVq1YsmSJVV1hwwZQufOnYHC/V+VlZV1BrvaHn/8cRYuXMjw4cMB+PDDDxk2bBg777wzbdu25YwzzuDoo4/e5L1utV1xxRUbhL/tt9+en/zkJxx66KFcffXV7LffflXTPv3pT1NRUUG3bt34+Mc/zuLFi2u8duXKlZx22mk8//zzRARr1qypc7lHH3002267Ldtuuy2dOnXizTff5MEHH2TevHkMHjwYgPfff59OnTrx5z//mVGjRtGxY0cATj755BrbdL3dd9+dXr168eCDD7LbbrvRpk0bevfuDRR+9P7kk0/m9ddf58MPP6zxw9/HHnss22233Qb1NrYuY8aMoV27dgD07NmTl156ib///e8ceuihVbV33XVXoP795IADDqhz20iSJEn1MdiVadttt63qr6ioqBquqKjgo48+AiClxF133UX37t1rvHbq1KnstttuPPXUU6xbt462bdvWWbdVq1ZVtTYlpcRhhx3GL37xiw2mPfHEEzz44IPMmDGDa6+9lj/84Q+lr2g9/vKXv9C+fXtee+21GuNr/75a7eELL7yQ0aNHc/fdd1NZWcmoUaPqrF/Xdkgpcdppp/H973+/xrwzZ84s+Xfd1l+Oudtuu1Vdhglw7rnn8rWvfY1jjz2WOXPmMHXq1Kpp9Z0529i61Nf+utpZ334iSZIklSvXwa65/jzBEUccwbRp05g2bRoRwfz58xkwYAArV66kc+fOVFRUcPPNN1fdJ1aunXbaiVWrVtGhQweGDh3K2WefzdKlS/nEJz7B6tWrWbZsGXvuuSerV6/mU5/6FEOHDuUTn/hEjdc2xEsvvcQPf/hD5s+fz6c+9SmOO+44DjroIADuuOMOTjvtNF588UVeeOEFunfvzuOPP1712pUrV7LXXnsBhXvRyjFmzBjGjRvHV7/6VTp16sTbb7/NqlWrOOigg/jyl7/MihUr2Hnnnbnjjjvo169fnTVOPPFEvv3tb7P99tvXCLjV23XzzTeX1J5y12XYsGGcffbZvPjii1WXYu6666717ieSJElSuXx4ymZw4YUXsmbNGvr27Uvv3r258MILATjrrLO4+eabGTp0KEuWLGnwvVRTpkzhqKOOYvTo0XTs2JHp06czYcIE+vbty9ChQ6ue+njMMcfQt29fRo4cydVXXw3AKaecwhVXXMGAAQM2+vCU9Q9YWd/94x//4POf/zxXXnkle+65Jz/96U8544wz+OCDDwDo3r07I0eO5KijjuLHP/5xjbORAN/4xjf41re+xfDhw8sOtD179uTSSy/l8MMPp2/fvhx22GG8/vrr7LHHHkydOpVhw4YxduxYBg4cWG+NXXbZhaFDh7LbbrvVuNxy6tSpnHTSSYwYMYIOHTqU1J5y16Vjx45cf/31nHDCCfTr14+TTz4ZqH8/kSRJksoV9T19sakNGjQorX+wyHqLFi3y/qNmaNKkSSU9kEXZ8P+B1HS6XPCbDcY15OqR2nWyqNGUbZEkbR4RMS+lNKiUeT1jJ0mSJEk5l+t77Fq6448/nhdffLHGuMsuu4wjjjgik/pnn332Br/99uUvf5nJkyeXVafce+YkSZIkZctg14zdfffdm7X+ddddt1nrS5IkSdoyGnUpZkTsGhG/j4jni/9+rI55+kfEYxHxbEQ8HREnN2aZkiRJkqSaGnuP3QXAgymlbsCDxeHaVgOfSyn1Ao4EfhQRuzRyuZIkSZKkosYGu3HA+h//uhk4rvYMKaUlKaXni/2vAX8DOjZyuZIkSZKkosbeY7dbSul1gJTS6xHRaWMzR8QQYBugzh9Qi4gpwBSAffbZZ9NLn9quzOZuqt7KbOtJkiRJ0hawyTN2EfFARDxTRzeunAVFxB7ALcDklNK6uuZJKV2fUhqUUhrUsaMn9erzox/9iNWrV5f9uosuuogHHnig7NdNnz6djh071vjB8oULF5ZdB2DHHXfc5DwHH3xwg2rXVllZSe/evTcY37VrV5577rka477yla9w+eWXl1U/q3ZKkiRJjbXJM3YppbH1TYuINyNij+LZuj0oXGZZ13w7A78BvpNSerzBrRVQCHannnoq22+//QbT1q5dS6tWrep83SWXXNLgZZ588slce+21DX59OR599NHNWv+UU05hxowZXHzxxQCsW7eOO++8c4OffqjP+m28udspSZIklaqx99jNAk4r9p8G3FN7hojYBrgb+FlK6Y5GLq9JVVZW0qNHD8444wx69+7NxIkTeeCBBxg+fDjdunXjiSeeAOC9997j9NNPZ/DgwQwYMIB77rmn6vUjRoxg4MCBDBw4sCoYzJkzh1GjRjF+/Hh69OjBxIkTSSnV2YZrrrmG1157jdGjRzN69GigcBbsoosu4qCDDuKxxx7jkksuYfDgwfTu3ZspU6ZU1Zo0aRJ33nknAF26dOHiiy9m4MCB9OnTh8WLF5e9Pe6++27Gjh1LSonXX3+d/fffnzfeeIPp06czbtw4jjzySLp3786///u/b/Dad999lzFjxlQtf/02Wr8+m9ou8+bNY+TIkRx44IEcccQRvP7661Xj+/Xrx7Bhw+r9OYcJEyYwY8aMquGHHnqILl26sO+++270PRo9ejSf+cxn6NOnT4121rculZWVHHDAAZx55pn06tWLww8/nPfffx+ApUuXMnbsWPr168fAgQP5618LVydfccUVDB48mL59+1YFT0mSJGmTUkoN7oD2FJ6G+Xzx312L4wcBNxT7TwXWAAuqdf03VfvAAw9MtS1cuLDmiIt3zrbbhBdffDG1atUqPf3002nt2rVp4MCBafLkyWndunVp5syZady4cSmllL71rW+lW265JaWU0t///vfUrVu39O6776b33nsvvf/++ymllJYsWZLWr+Ps2bPTzjvvnF555ZW0du3aNHTo0PSnP/2p3nbsu+++afny5VXDQLr99turhlesWFHVf+qpp6ZZs2allFI67bTT0h133FFV45prrkkppXTdddelz3/+8/Uu76abbkodOnRI/fr1q+pWr16dUkpp4sSJadq0aenoo49Ot912W9X8u+++e3rrrbfS6tWrU69evdKTTz6ZUkpphx12SCmltGbNmrRy5cqUUkrLly9P++23X1q3bl2NeerbLh9++GEaNmxY+tvf/pZSSmnGjBlp8uTJKaWU+vTpk+bMmZNSSun8889PvXr1qnOdevbsmRYsWJBSSukLX/hCuvbaa1NKaaPv0fbbb59eeOGFqhqbWpf1+8v8+fNTSimddNJJVfvFkCFD0q9+9auUUkrvv/9+eu+999J9992XzjzzzLRu3bq0du3adPTRR6c//vGPG7R9g/8HkiRJapGAuanEbNaoh6eklFYAY+oYPxc4o9j/c+DnjVlOc9K1a9eqMza9evVizJgxRAR9+vShsrISgPvvv59Zs2Zx5ZVXAvDBBx/w8ssvs+eee3LOOeewYMECWrVqxZIlS6rqDhkyhM6dOwPQv39/KisrOeSQQ0pqU6tWrTjxxBOrhmfPns3ll1/O6tWrefvtt+nVqxf/8i//ssHrTjjhBAAOPPBAfvWrX210GfVdijlt2jQwbv26AAATKUlEQVR69+7N0KFDmTBhQtX4ww47jPbt21ct5+GHH2bQoEFV01NKfPvb3+ahhx6ioqKCV199lTfffJPdd9+9Rv26tssuu+zCM888w2GHHQYULo3cY489WLlyJe+88w4jR44E4LOf/Sy//e1v61yf9WftevXqxT333FN1meqaNWs2+h517dp1g1r1rQsU9pf+/fsDhe1cWVnJqlWrePXVVzn++OMBaNu2LVDYb+6//34GDBgAFM4EPv/88xx66KF1roMkSZK0XmOfirnV2Xbbbav6KyoqqoYrKir46KOPgMKB/l133UX37t1rvHbq1KnstttuPPXUU6xbt67qgL523VatWlXVKkXbtm2r7qv74IMPOOuss5g7dy577703U6dO5YMPPtjoupS7vOpeffVVKioqePPNN1m3bh0VFYWreyOixny1h2+99VaWL1/OvHnzaNOmDV26dKmznXVtl5QSvXr14rHHHqsx7zvvvLPBcuozYcIEDj/8cEaOHEnfvn3p1KnwQNerr7663vdohx12qLPWxtaldvvff//9ei+zTSnxrW99iy984QslrYMkSZK0Xr6DXTP9eYIjjjiCadOmMW3aNCKC+fPnM2DAAFauXEnnzp2pqKjg5ptvZu3atQ2qv9NOO7Fq1So6dOiwwbT1gaJDhw68++673HnnnYwfP75R61Ofjz76iMmTJ3Pbbbfxs5/9jKuuuorzzz8fgN///ve8/fbbbLfddsycOZMbb7yxxmtXrlxJp06daNOmDbNnz+all14qebndu3dn+fLlPPbYYwwbNow1a9awZMkSevXqRbt27Xj44Yc55JBDuPXWW+utsd9++9G+fXsuuOACvvKVr9RoV7nvUbnrsvPOO9O5c2dmzpzJcccdxz/+8Q/Wrl3LEUccwYUXXsjEiRPZcccdefXVV2nTpk1V6JQkSZLq09iHp6gOF154IWvWrKFv37707t2bCy+8EICzzjqLm2++maFDh7JkyZJ6zwBtypQpUzjqqKOqHp5S3S677MKZZ55Jnz59OO644xg8eHCj1mW922+/vcbPHTz66KN873vfY8SIEYwYMYKrrrqKG264gUWLFgFwyCGH8NnPfpb+/ftz4okn1rgME2DixInMnTuXQYMGceutt9KjR4+S27LNNttw55138s1vfpN+/fpVtQfgpptu4uyzz2bYsGFst912G60zYcIEFi9eXHVJJDTsPWrIutxyyy1cc8019O3bl4MPPpg33niDww8/nM985jMMGzaMPn36MH78eFatWrXJWpIkSVLUd1lYUxs0aFCaO3dujXGLFi3igAMOaKIWqVTTp09n7ty5W+znEbY2/j+QJEnaOkTEvJTSoE3P6Rk7SZIkScq9fN9j18Idf/zxvPjiizXGXXbZZRxxxBGbZXk33XQT//mf/1lj3PDhw+v9Pbj6TJo0iUmTJmXYMkmSJEkbk7tgl1Iq+cmHeXf33Xdv0eVNnjyZyZMnb9FlqjzN9dJpSZIkNa1cXYrZtm1bVqxY4cGttkopJVasWFHjJxgkSZIkyNkZu86dO7Ns2TKWL1/e1E2RmkTbtm2rfrBdkiRJWi9Xwa5NmzZ07dq1qZshSZIkSc1Kri7FlCRJkiRtyGAnSZIkSTlnsJMkSZKknIvm+oTJiFgOvLSJ2ToAb2WwuCzqNJcatsW2bOkatqX5t6WlrY9taf5taWnrY1s2Xw3bYlu2dI28tWXflFLHkiqllHLbAXObS53mUsO22JateX1sy9axPral+belpa2Pbdk61se2NP+2tLT1ybJOSslLMSVJkiQp7wx2kiRJkpRzeQ921zejOs2lRlZ1bMvmq5FVneZSI6s6tqV518iqjm3ZfDWyqtNcamRVx7Y07xpZ1bEtm69GVnWaS42s6jSntgDN+OEpkiRJkqTS5P2MnSRJkiRt9Qx2kiRJkpRzBjtJkiRJyrnWTd2AckRED2AcsBeQgNeAWSmlRU3Ulr2AP6eU3q02/siU0u9KrDEESCmlJyOiJ3AksDildG8j2vWzlNLnGvr6Yo1DgCHAMyml+8t43UHAopTS/0XEdsAFwEBgIfC9lNLKEmqcB9ydUnqlYa2HiNgGOAV4LaX0QER8BjgYWARcn1JaU0at/YDjgb2Bj4DngV+Usi6SWr6I6JRS+ltTt0OSpNycsYuIbwIzgACeAJ4s9v8iIi7IaBmTS5zvPOAe4FzgmYgYV23y90qscTFwDfDfEfF94FpgR+CCiPi3EmvMqtX9D3DC+uFSahTrPFGt/8xiW3YCLi5z294IrC72/yfQDrisOO6mEmv8B/DniPhTRJwVER3LWP56NwFHA1+OiFuAk4A/A4OBG0otUnyffwy0Lb52OwoB77GIGNWAdmkjIqJTU7dB/xQR7SLiBxGxOCJWFLtFxXG7NHX7yhURu0fEf0fEdRHRPiKmRsRfIuKXEbFHiTV2rdW1B56IiI9FxK6beRVKaV/7JlruoIiYHRE/j4i9I+L3EbEyIp6MiAFbuC2tI+ILEfG7iHg6Ip6KiN9GxBcjok0G9Ut+el1EtCq25T8iYnitad8pscb2EfGNiPh6RLSNiEnFv/GXR8SO5ba/Vu0lZc7ft1p/m4j4TrEt34uI7cuoc05EdCj2fyIiHoqIdyLizxHRp8Qav4qIUzPYBh+PiBsj4tKI2DEifhIRz0TEHRHRpcQaFRFxekT8pri/zYuIGeUcJ2zu/ba4jJL2Xffbeus0m/12o7L6pfPN3QFLgDZ1jN8GeD6jZbxc4nx/AXYs9ncB5gJfLg7PL6NGK2B74P+AnYvjtwOeLrHG/wI/B0YBI4v/vl7sH1nGes+v1v8k0LHYvwPwlzLqLKretlrTFpTaFgpfOBwO/BRYDvwOOA3YqcQaTxf/bQ28CbQqDkep27b6e1Ts3x6YU+zfp9T3uTh/O+AHwGJgRbFbVBy3Sxb77pbsgN2B/wauA9oDU4vb6pfAHiXW2LVW1x6oBD4G7NoM1rF9Ey13EDC7+P96b+D3wMri/8sBW7gt9wHfBHav9d5/E/h9BvV/W8a8OwPfB24BPlNr2n+VWON3FL6MuwB4urge+xTH3VNijXXAi7W6NcV/XyhjfY6s1t+u+Fn3NHAbsFuJNX4AdKi237wALAVeorzP//8FvgPs14j38gngKGAC8Aowvjh+DPBYGXV2BC4Bni3u98uBx4FJZdT4RfHzaSjQudgNLY67vcQatT+fqn9OLSujLTcU39OvAPOAq6pv9xJr/BL4IfBfwIMUvng9FLgCuKWMtqyicKzxf8X+VcDa9eNL3Veq9f8QmE7heONq4GdltOXZav2/AY4v9o8CHimxxqvAncDbxW10PLBNA/bdh4AvFT8XngH+lcJn7+eBP5RY4yYKfwcPAX5U3IcPAx4Azt1S+21W+677bfPfbze6jCyLbc6OwkHxvnWM3xd4row6T9fT/QX4R4k1FtYa3pHCQcNVlBFg6uovDpdaowL4KoWDv/7FcSUfXFSr8xSFA+r2wNz62llCnTuAycX+m4BBxf79gSdLrFE7ELYBji1+6C0vscYzFAL/x4r/+Xctjm9LtfBZQp2/ANsW+z8GzKu+jDLqeIC8YQ0PkOuu0ZwOkOv9XN3YtFrzDaynOxB4vYy23FV8n44DZhWH1//fLPVAo/pn7su1ppX6mXt+cf/vU23ciw14n6sfaNwAXErhb9lXgZkl1vhLtf7ZwOBi//7U+hzfRJ0XgSuBl4v731eBPctcn41t23L+htwDTKJwUPs14EKgG3Azhcv5G7vfLimxxtri50D1z6f1wx+WsT5PV+tvTeG3qn4FbFvqdlm/b1L4YvIN/vkzVeV+UTkN+BnVPhfL3Xdrvc8LKH7Z3oC2PFet/8la00r9Ynt+8d+dgM8C91L4nLsJOHxL7ru12ww8Xvx3W0o85shiv81q33W/bf777UaXkUWRLdFRuP9sKfDb4k52PYU/sEupdnBXQp03gf4U/ohW77pQuCerlBp/oBikqo1rXdz51pZY48/A9sX+imrj21HigUq113SmEKqurf3BVOLrK6v9x3+BYgChcHBY0gFPtbZPB/5aXL81xXp/BPqVWKPeDw1guxJrfLW43JeA8yh8W/QTCkHt4jLW58sUQsL1FL5YWB9aOwIPlVHHA+QNa3iAXP623dIHyPcD36DmH9TdKAT5B0qssZbC5+XsOrr3y1ifBbWG/w14hMKXUaXut09V67+0vn2ghDrrP2+vovDHuSFfplXfb2uvW6n/hxYDrYv9jzdifaq3ZQSFb9jfKL5HU0qs8RiFqyxOovC5e1xx/Mgy/w89VWv4yeK/FRTuPy+lxuPFdlT/u1oBnEzhnvhSajwP7FPPtFfKWJ8N2gxcXNx3S7rSqPr+ANy4se1VQq0Di/8fzytuk7L2XQp/V08ATqRWYCmnLcB3KRwrfBz4NoUzQ/sAk4Ffl7vfVhu3K/BFSjzTVnzNPAqf9UOAt/jnF9KfoPSD9XkUv9Cj8Hf5oWrTFpZYo9H7bVb7bgvdb49vSfvtRpeRRZEt1RXf0KHFN2d8sb9VmTV+ChxSz7TbSqzRmWpnX2pNG15ijW3rGd+Bage7Za7b0ZR40FZive2Brg143U5Av+J/xpLOmlR77f4ZtX1PigfVwC7F/WVIA+r0Kr62RyPa4gFy3XU8QN6wRnM6QP4YhXtkFwN/p3DpyKLiuJIul6Vw9rxbPdPKOUBeRLUDnuK40yickXypxBqXULyEvtb4TwB3NmDf+xcKB2NvNOC1yygE7n+lcNAR1aaVejB5bvGz5ZMULgP7EYVLnf6d8i51qutAoxWFL1NvKrFGPwpXJvwW6EHhHut3iu/PwWW05VGKf5+L2/e+atNK/RKsC3A78DcKt3AsKfbfTol/z4CzqefLSEq8tK4478+p44tn4AxgTYk1bqhnv90PeLgB+14FhQPkP1Hil9nVXntTrW634vjdgQfLrDWJwhfAb1G4smYhhWcUtCvx9SV/ubqJOmOA54qfMYdQ+LLz+eI+M67EGp+k8IXeEgpf8B1UHN8RuLzM/XZ5sc76NpS832a177bA/XZ6hvvt5Oaw3250GZt7AXZ2W3tHzQPkt6l5gPyxEmt4gFz3az1ArrtOow+Qi/P2AMbWfr/r+qNfz+vHA93rmXZcGe24HBhbx/gjKeMe6+L6jGno+tSuQeGe6N4NqHFxrW79fc27U949H6MoHPjNp3BFwr3AFOq4H30jNWaUOu8m6hyQwbbtR+Fs9zvAw+v3HQoHyOeVUecgCmdg2lM4WD8f+FSZ6zOEf56971n8nCmrRlZ16qlxNNU+7xpQZwRwUQPactBm2C69KHyGN9V7dFCttjRkfxmWRVuKr29P4Uv+nzfk9XXUK/kzZUvUKHe/rVVjD2BFc1ifYp2SjxE2d1vWd+uveZXUBCJickrpphLmG0/h7NFzdUw7LqU0s8TlXQ7cn1J6oNb4I4FpKaVuJTa9vvolrU8dr9uOwqUsz5RTIwpPl63uv1JKyyNidwrflJb00x/Fp5d9icIlOa0p3N82k8IlJB+VWGNGSumUUuZtiDK3S18K35juT+FLgdNTSkui8JTZCSmla0qocR6Fb38XUbh8/csppXuK0/43pTSwxLY0+qdhNlHnqJTSb0t4/bnAOY1Zn6y2ySbWp5yfzNnc27akOsXtchaFL68au10OKLbl8Qa25WIK96m2pnD/+RAKtwOMpfAFx3cbUOMgYE45NbKqsxnbksV2KbvGZmxLrrdL1P0k809SuFKHlNKxJa5P7ToBjC6nTnOpUU8dKHO7bMZtm0VbGrRdNirLlGhnZ1deRwPuiayjxuSM2tLoOhmtT6NrZLg+LWrbltMWsnn673kULnOaSeFe3nHVppV8LzGFs6qNqpPR+jS6Robr06K2bbV1WpxBWxr7xOlG17AttqXMGpk95byxdZpLjay2S0tsy0aXkUUROzu7+jsyeBLrJupndcBf6s99ZPFk2c26TbLaLnncthm2JYun/2Z1wJ9FKMtifRpdI8P1aVHbNsO2ZPHE6UbXsC22pcwaWT3lvNF1mksN29KwrjWSNrfdgCMoPICiuqBwL9QmRcTT9U0q1i9JRnUavT4Z1chkfVrgts2qLW9ERP+U0gKAlNK7EXEMcCNQ0o+xUni41bvF11cWL3m9MyL2LbalVFnUyWJ9sqiR1fq0tG2bVVs+jIjtU0qrKTzAC4CIaEfhZ1a2VA3bYltKrpFSWgdcHRF3FP99E8o/Rs+iTnOpYVsaxmAnbX6/pvAt9ILaEyJiTok1Mjngz6hOFuuTRQ1oRiEzozrNabt8Dqhxf2Eq3G/4uYj4fyXWyOqAP4s6WaxPFjWgeYXM5rJts2rLoSmlfxRfX/2gug2FB0VtqRq2xbaU2xZSSsuAkyLiaAqXdTZIFnWaSw3bUh4fniLlQET8lMKTFR+uY9ptKaXPbMk6zUUW69MSt21zaUtEdAY+Sim9Uce04SmlR7ZkneYii/Vpidu2ObVFkvLIYCdJkiRJOVfR1A2QJEmSJDWOwU6SJEmScs5gJ0mSJEk5Z7CTJEmSpJz7/1wjXxbeXh5EAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rf_opt.plot.bar(y=[\"mean_test_Explained Variance\",\"mean_train_Explained Variance\"], figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a1ce29898>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rf_opt.plot.bar(y=[\"mean_test_MAE\",\"mean_train_MAE\"], figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a1cb79630>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rf_opt.plot.bar(y=[\"mean_test_MSE\",\"mean_train_MSE\"], figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a1d60e240>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rf_opt.plot.bar(y=[\"mean_test_MedAE\",\"mean_train_MedAE\"], figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x25a1d59cbe0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_rf_opt.plot.bar(y=[\"mean_test_R2\",\"mean_train_R2\"], figsize=(15,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean_fit_time         0.614939\n",
       "std_fit_time        0.00956473\n",
       "mean_score_time       0.520042\n",
       "std_score_time     0.000632062\n",
       "param_bootstrap          False\n",
       "Name: 33, dtype: object"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rf_opt.iloc[33].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "UNC-ML (Python 3.7.1)",
   "language": "python",
   "name": "unc"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}