sleep-prediction-with-ensemble.ipynb

{"cells":[{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# Sleep Prediction - Unlocking the Secrets of Restful Nights\n","## In this problem we have to predict the sleep time. \n","\n","We have 8 years of historic health data taken from various devices including: Fitbit, Apple Watch and iPhone. <br>\n","This valuable data encompassed crucial metrics including step count, heart rate, exercise time, and energy burned.\n","\n","### In this notebook we will implement an Ensemble Model\n","We will train Individual Model with Hyper-parameter tuning using GridSearchCV, after which we will implement esemble model with following learners: <br>\n","1. Decision Tree\n","2. Rnadom Forest\n","2. ADA Boost\n","3. Gradient Boost\n","4. XGBoost\n","    \n","**So Let's get started with our eyes wide open Or..!!**\n","\n","<img src=\"https://media.giphy.com/media/v1.Y2lkPTc5MGI3NjExNTQ3YWZhOTI3ODNkOGE1ZGFjNzRlYjQzMTlhNDc3NzA4ZWQwOGRmYSZlcD12MV9pbnRlcm5hbF9naWZzX2dpZklkJmN0PWc/vMbC8xqhIf9ny/giphy.gif\" alt=\"Cat GIF\" width=\"400\" height=\"400\">\n"]},{"cell_type":"code","execution_count":1,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:45:10.489644Z","iopub.status.busy":"2023-05-26T09:45:10.489081Z","iopub.status.idle":"2023-05-26T09:45:11.561617Z","shell.execute_reply":"2023-05-26T09:45:11.560376Z","shell.execute_reply.started":"2023-05-26T09:45:10.489612Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fri May 26 09:45:11 2023       \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 470.161.03   Driver Version: 470.161.03   CUDA Version: 11.4     |\n","|-------------------------------+----------------------+----------------------+\n","| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n","|                               |                      |               MIG M. |\n","|===============================+======================+======================|\n","|   0  Tesla P100-PCIE...  Off  | 00000000:00:04.0 Off |                    0 |\n","| N/A   37C    P0    26W / 250W |      0MiB / 16280MiB |      0%      Default |\n","|                               |                      |                  N/A |\n","+-------------------------------+----------------------+----------------------+\n","                                                                               \n","+-----------------------------------------------------------------------------+\n","| Processes:                                                                  |\n","|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n","|        ID   ID                                                   Usage      |\n","|=============================================================================|\n","|  No running processes found                                                 |\n","+-----------------------------------------------------------------------------+\n"]}],"source":["# Check CUDA\n","!nvidia-smi"]},{"cell_type":"markdown","metadata":{},"source":["# Import Libs"]},{"cell_type":"code","execution_count":2,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:45:11.624668Z","iopub.status.busy":"2023-05-26T09:45:11.624229Z","iopub.status.idle":"2023-05-26T09:45:22.473861Z","shell.execute_reply":"2023-05-26T09:45:22.472871Z","shell.execute_reply.started":"2023-05-26T09:45:11.624631Z"},"trusted":true},"outputs":[{"name":"stderr","output_type":"stream","text":["/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.23.5\n","  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"]}],"source":["import pandas as pd\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","import math\n","\n","from tqdm.notebook import tqdm\n","\n","import os\n","import re\n","import sys\n","\n","from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error\n","from sklearn.model_selection import train_test_split\n","\n","import keras.backend as K\n","import tensorflow as tf\n","from tensorflow.keras.models import Sequential\n","from tensorflow.keras.optimizers import Adam, RMSprop\n","from tensorflow.keras import layers\n","\n","import torch\n","from torch import nn\n","from torch.utils.data import DataLoader, RandomSampler, TensorDataset\n","\n","%matplotlib inline\n","plt.style.use('ggplot')\n","pd.set_option('display.max_rows', 100)\n","pd.set_option('display.max_columns', 100)"]},{"cell_type":"code","execution_count":3,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:45:22.476480Z","iopub.status.busy":"2023-05-26T09:45:22.475776Z","iopub.status.idle":"2023-05-26T09:45:22.481241Z","shell.execute_reply":"2023-05-26T09:45:22.480377Z","shell.execute_reply.started":"2023-05-26T09:45:22.476453Z"},"trusted":true},"outputs":[],"source":["HOME = '/kaggle/working/'\n","HOMEDATA = '/kaggle/input/kaggle-pog-series-s01e04/'"]},{"cell_type":"markdown","metadata":{},"source":["# Load Training Data"]},{"cell_type":"code","execution_count":4,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:45:22.483560Z","iopub.status.busy":"2023-05-26T09:45:22.482622Z","iopub.status.idle":"2023-05-26T09:45:22.525366Z","shell.execute_reply":"2023-05-26T09:45:22.524320Z","shell.execute_reply.started":"2023-05-26T09:45:22.483523Z"},"trusted":true},"outputs":[{"data":{"text/html":["<div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>sleep_hours</th>\n","    </tr>\n","    <tr>\n","      <th>date</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2015-02-19</th>\n","      <td>6.400000</td>\n","    </tr>\n","    <tr>\n","      <th>2015-02-20</th>\n","      <td>7.583333</td>\n","    </tr>\n","    <tr>\n","      <th>2015-02-21</th>\n","      <td>6.350000</td>\n","    </tr>\n","    <tr>\n","      <th>2015-02-22</th>\n","      <td>6.500000</td>\n","    </tr>\n","    <tr>\n","      <th>2015-02-23</th>\n","      <td>8.916667</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>"],"text/plain":["            sleep_hours\n","date                   \n","2015-02-19     6.400000\n","2015-02-20     7.583333\n","2015-02-21     6.350000\n","2015-02-22     6.500000\n","2015-02-23     8.916667"]},"execution_count":4,"metadata":{},"output_type":"execute_result"}],"source":["train = pd.read_csv(f'{HOMEDATA}FinalTrain.csv', parse_dates=['date'], index_col='date')\n","train.head()"]},{"cell_type":"code","execution_count":6,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T05:49:08.291846Z","iopub.status.busy":"2023-05-26T05:49:08.291245Z","iopub.status.idle":"2023-05-26T05:49:08.299540Z","shell.execute_reply":"2023-05-26T05:49:08.298362Z","shell.execute_reply.started":"2023-05-26T05:49:08.291815Z"},"trusted":true},"outputs":[{"data":{"text/plain":["(2354, 1)"]},"execution_count":6,"metadata":{},"output_type":"execute_result"}],"source":["train.shape"]},{"cell_type":"markdown","metadata":{},"source":["# Include XML files with train"]},{"cell_type":"code","execution_count":9,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:46:57.480822Z","iopub.status.busy":"2023-05-26T09:46:57.480493Z","iopub.status.idle":"2023-05-26T09:46:57.491712Z","shell.execute_reply":"2023-05-26T09:46:57.490967Z","shell.execute_reply.started":"2023-05-26T09:46:57.480791Z"},"trusted":true},"outputs":[],"source":["# Useful files and their aggregating methods\n","useful_files = {'StepCount.csv': 'sum',\n","                'HeartRate.csv': 'max',\n","                'DistanceWalkingRunning.csv': None, #--special treatment\n","                'BasalEnergyBurned.csv': 'sum',\n","                #'FlightsClimbed.csv',         #-- not seem fit\n","                'ActiveEnergyBurned.csv': 'sum',\n","                'Workout.csv': 'sum',\n","                'BodyMass.csv': 'max'}"]},{"cell_type":"code","execution_count":10,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:46:57.493479Z","iopub.status.busy":"2023-05-26T09:46:57.493144Z","iopub.status.idle":"2023-05-26T09:46:57.514252Z","shell.execute_reply":"2023-05-26T09:46:57.513000Z","shell.execute_reply.started":"2023-05-26T09:46:57.493446Z"},"scrolled":true,"trusted":true},"outputs":[],"source":["def xml_data (data):\n","    temp = data.copy()\n","    for file in useful_files.keys():\n","        # Load file\n","        date_columns = ['startDate']\n","        if(file == 'Workout.csv'):\n","            data = pd.read_csv(f'{HOMEXML}{file}',usecols = ['startDate','duration'])\n","            data.rename(columns = {'duration':'value'}, inplace=True)\n","        else: \n","            data = pd.read_csv(f'{HOMEXML}{file}',usecols = ['startDate','value'])\n","        data = data.sort_values('startDate').reset_index(drop=True)\n","        \n","        # Parse & Format the date\n","        for col in date_columns:\n","            data[col] = pd.to_datetime(data[col]).dt.tz_convert(None) #remove the UTC component, -0400\n","            data[col] = pd.to_datetime(data[col].apply(lambda x:x.strftime('%Y-%m-%d')))\n","    \n","        # Pre-process data \n","        if file != 'DistanceWalkingRunning.csv':\n","            data = data.groupby('startDate').aggregate({'value': useful_files[file]})\n","        else:\n","            # Computation method for DistanceWalkingRunning\n","            dates = data['startDate'].unique()\n","            data_dict = {}\n","            for i, date in enumerate(dates):\n","                temp_dist = data[data['startDate'] == date]\n","                if max(temp_dist['value'].values) > 1:\n","                    data_dict[i] = (date, max(temp_dist['value'].values))\n","                else:\n","                    data_dict[i] = (date, sum(temp_dist['value'].values))\n","            data = pd.DataFrame(data_dict).T\n","            data.columns = ['startDate', 'value']\n","            data['value'] = data['value'].astype(float)\n","            data['startDate'] = pd.to_datetime(data['startDate'])\n","            data.set_index('startDate', inplace=True)\n","        \n","        # Merge with df\n","        trail = pd.merge(temp, data, how='inner', left_index = True, right_index = True)\n","        temp[file.split('.')[0]] = trail['value']\n","    return temp"]},{"cell_type":"markdown","metadata":{},"source":["### Impute Nan values of XML file"]},{"cell_type":"code","execution_count":12,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:47:41.675940Z","iopub.status.busy":"2023-05-26T09:47:41.675589Z","iopub.status.idle":"2023-05-26T09:47:41.685907Z","shell.execute_reply":"2023-05-26T09:47:41.684686Z","shell.execute_reply.started":"2023-05-26T09:47:41.675908Z"},"trusted":true},"outputs":[],"source":["def xml_impute (data):\n","    temp = data.copy()\n","    print('Pre-Imputation\\n', temp.isna().sum())\n","    temp.loc[((~pd.isnull(temp['Workout'])) & (pd.isnull(temp['HeartRate']))), 'HeartRate'] = \\\n","            temp.loc[~pd.isnull(temp['Workout']), 'HeartRate'].mean()\n","    temp.loc[pd.isnull(temp['HeartRate']), 'HeartRate'] = 110\n","    temp['BasalEnergyBurned'] = temp['BasalEnergyBurned'].fillna(method='bfill')\n","    temp['BodyMass'] = temp['BodyMass'].fillna(method='ffill')\n","    temp.loc[pd.isnull(temp['Workout']), 'Workout'] = 0\n","    \n","    dates_na = temp[pd.isnull(temp['ActiveEnergyBurned'])].index\n","    # ActiveEnergyBurned - previous 7 days average\n","    for date in dates_na:\n","        impute_val = temp[((temp.index >= date - pd.DateOffset(days=7)) & \\\n","                                    (temp.index < date))]['ActiveEnergyBurned']\n","        temp.loc[(temp.index == date), 'ActiveEnergyBurned'] = impute_val.mean()\n","    print('Post-Imputation\\n', temp.isna().sum())\n","    return temp"]},{"cell_type":"markdown","metadata":{},"source":["# Feature Engineering"]},{"cell_type":"code","execution_count":17,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:47:43.277072Z","iopub.status.busy":"2023-05-26T09:47:43.276211Z","iopub.status.idle":"2023-05-26T09:47:43.287344Z","shell.execute_reply":"2023-05-26T09:47:43.286122Z","shell.execute_reply.started":"2023-05-26T09:47:43.277011Z"},"trusted":true},"outputs":[],"source":["def createDateFeatures(data):\n","    temp = data.copy()\n","    temp['day'] = temp.index.day\n","    temp['month'] = temp.index.month\n","    temp['year'] = temp.index.year\n","    temp['weekofyear'] = temp.index.isocalendar().week.astype('int')\n","    temp['weekday'] = temp.index.weekday\n","    temp['dayofyear'] = temp.index.day_of_year\n","    \n","    temp['date_offset'] = (temp.index.month * 100 + temp.index.day - 320) % 1300\n","    temp['season'] = pd.cut(temp['date_offset'], \n","                            bins=[0, 300, 602, 900, 1300],\n","                            labels=['spring', 'summer', 'fall', 'winter'])\n","    temp.drop('date_offset', axis=1, inplace=True)\n","    \n","    temp2 = pd.get_dummies(temp['season'])\n","    temp.drop('season', axis=1, inplace=True)\n","    temp = temp.merge(temp2, how='left', left_index=True, right_index=True)\n","    \n","    return temp"]},{"cell_type":"code","execution_count":19,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:47:43.331227Z","iopub.status.busy":"2023-05-26T09:47:43.330628Z","iopub.status.idle":"2023-05-26T09:47:43.345845Z","shell.execute_reply":"2023-05-26T09:47:43.344843Z","shell.execute_reply.started":"2023-05-26T09:47:43.331194Z"},"trusted":true},"outputs":[],"source":["'''\n","    Our forecasting horizon is >1 years, \n","    hence for lag of days, we'll compute iteratively from previous predictions.\n","'''\n","# Lag Features\n","def createLagFeatures(data):\n","    temp = data.copy()\n","    n=3 # Num of Lags\n","    # Day lag (3 days)\n","    for i in range(1, n+1):\n","        temp[f'day_lag_{i}'] = temp['sleep_hours'].shift(i)\n","        temp[f'day_lag_{i}'] = temp[f'day_lag_{i}'].bfill()\n","    # Year lag (1 year)\n","    temp[f'year_lag_{1}'] = temp['sleep_hours'].shift(364)\n","    temp[f'year_lag_{1}'] = temp[f'year_lag_{1}'].bfill()\n","    return temp\n","\n","def random_noise(data):\n","    return np.random.normal(scale=1.5, size=(len(data)))\n","# Roll mean Features\n","def createRollMeanFeatures(data):\n","    windows = [3,7,14,28]\n","    temp = data.copy()\n","    for window in windows:\n","        temp['roll_mean_' + str(window)] = \\\n","            temp['sleep_hours'].shift(1).rolling(window=window, win_type=\"triang\").mean() \\\n","                + random_noise(temp)\n","        temp['roll_mean_' + str(window)] = temp['roll_mean_' + str(window)].bfill()\n","    return temp\n","\n","# Exponential Weighted Moving Average\n","def createEWMFeatures(data):\n","    alphas = [0.95,0.8]\n","    lags = [3,7,14,28]\n","    temp = data.copy()\n","    for alpha in alphas:\n","        for lag in lags:\n","            temp['ewm_alpha_' + str(alpha).replace(\".\", \"\") + \"_lag_\" + str(lag)] = \\\n","                temp['sleep_hours'].shift(lag).ewm(alpha=alpha).mean()\n","            temp['ewm_alpha_' + str(alpha).replace(\".\", \"\") + \"_lag_\" + str(lag)] = \\\n","                temp['ewm_alpha_' + str(alpha).replace(\".\", \"\") + \"_lag_\" + str(lag)].bfill()\n","    return temp"]},{"cell_type":"code","execution_count":21,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:47:43.469035Z","iopub.status.busy":"2023-05-26T09:47:43.468400Z","iopub.status.idle":"2023-05-26T09:47:43.622578Z","shell.execute_reply":"2023-05-26T09:47:43.621621Z","shell.execute_reply.started":"2023-05-26T09:47:43.469000Z"},"trusted":true},"outputs":[],"source":["import holidays\n","\n","def get_holidays(df):\n","    years_list = np.arange(train_new.index.year.min(), train_new.index.year.max()+1)\n","\n","    holiday_US = holidays.CountryHoliday('US', years = years_list)\n","\n","    df['holiday_name'] = df.index.map(holiday_US)\n","    df['is_holiday'] = np.where(df['holiday_name'].notnull(), 1, 0)\n","    df['holiday_name'] = df['holiday_name'].fillna('Not Holiday')\n","    \n","    return df.drop(columns = ['holiday_name'])"]},{"cell_type":"markdown","id":"0da8b7ee","metadata":{},"source":["# Split Feature and Target"]},{"cell_type":"code","execution_count":null,"id":"48fcce7c","metadata":{},"outputs":[],"source":["X = train.drop('sleep_hours', axis=1)\n","y = train['sleep_hours']"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["X.columns"]},{"cell_type":"markdown","id":"762dbc1e","metadata":{},"source":["# Train and Validation"]},{"cell_type":"code","execution_count":null,"metadata":{},"outputs":[],"source":["X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n","X_train.shape, X_val.shape, y_train.shape, y_val.shape"]},{"cell_type":"markdown","metadata":{},"source":["# Ensemble Model"]},{"cell_type":"code","execution_count":29,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:48:09.846968Z","iopub.status.busy":"2023-05-26T09:48:09.846591Z","iopub.status.idle":"2023-05-26T09:48:10.294000Z","shell.execute_reply":"2023-05-26T09:48:10.293063Z","shell.execute_reply.started":"2023-05-26T09:48:09.846938Z"},"trusted":true},"outputs":[],"source":["from sklearn.metrics import r2_score, mean_squared_error\n","from sklearn.ensemble import VotingRegressor\n","from sklearn.tree import DecisionTreeRegressor\n","from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor, AdaBoostRegressor\n","from xgboost import XGBRegressor\n","from sklearn.model_selection import GridSearchCV\n","from sklearn.model_selection import cross_val_score"]},{"cell_type":"markdown","metadata":{},"source":["## Individual Learners"]},{"cell_type":"code","execution_count":30,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T09:48:10.582829Z","iopub.status.busy":"2023-05-26T09:48:10.582474Z","iopub.status.idle":"2023-05-26T09:48:10.592871Z","shell.execute_reply":"2023-05-26T09:48:10.591733Z","shell.execute_reply.started":"2023-05-26T09:48:10.582800Z"},"trusted":true},"outputs":[],"source":["def rmse(actual, predicted):\n","    return np.sqrt(mean_squared_error(actual, predicted))\n","\n","from sklearn.metrics import make_scorer\n","my_scorer = make_scorer(rmse, greater_is_better=False)\n","\n","def grid_search(model, searchSpace, x, y, verbose=4, cv = 5):\n","    np.random.seed(42)\n","    \n","    # Initialize GridSearchCV\n","    gs = GridSearchCV(model, param_grid=searchSpace, scoring=my_scorer, cv=cv, refit=True, verbose=verbose)\n","    \n","#     # Use tqdm to create a loading bar\n","#     total_params = [len(searchSpace[key]) for key in searchSpace.keys()]\n","#     total_params = math.prod(total_params)\n","    \n","#     with tqdm(total=total_params, desc=\"Grid Search Progress\") as pbar:\n","#         # Custom progress bar update function\n","#         def update_progress(estimator, X, y):\n","#             pbar.update(1)  # Increment the progress bar\n","\n","#         # Set the progress callback\n","#         gs.progress_callback = update_progress\n","\n","    # Fit the grid search to the data\n","    gs.fit(x, y)\n","    \n","    # Print the best parameters and score\n","    print()\n","    print(\"Best parameters: \", gs.best_params_)\n","    print(\"Best score: \", gs.best_score_)\n","    \n","    return gs"]},{"cell_type":"markdown","metadata":{},"source":["### Decision Tree"]},{"cell_type":"code","execution_count":75,"metadata":{"collapsed":true,"execution":{"iopub.execute_input":"2023-05-25T11:35:55.162954Z","iopub.status.busy":"2023-05-25T11:35:55.162546Z","iopub.status.idle":"2023-05-25T11:35:59.409578Z","shell.execute_reply":"2023-05-25T11:35:59.408618Z","shell.execute_reply.started":"2023-05-25T11:35:55.162906Z"},"jupyter":{"outputs_hidden":true},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 24 candidates, totalling 120 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2;, score=-0.119 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=4;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=4;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=4;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=4;, score=-0.119 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=4;, score=-0.119 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.116 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=15, min_samples_split=2;, score=-0.153 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=15, min_samples_split=2;, score=-0.148 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=15, min_samples_split=2;, score=-0.146 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=15, min_samples_split=2;, score=-0.159 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=15, min_samples_split=2;, score=-0.146 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=15, min_samples_split=4;, score=-0.150 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=15, min_samples_split=4;, score=-0.147 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=15, min_samples_split=4;, score=-0.151 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=15, min_samples_split=4;, score=-0.158 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=15, min_samples_split=4;, score=-0.150 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=15, min_samples_split=10;, score=-0.143 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=15, min_samples_split=10;, score=-0.143 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=15, min_samples_split=10;, score=-0.146 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=15, min_samples_split=10;, score=-0.150 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=15, min_samples_split=10;, score=-0.140 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=30, min_samples_split=2;, score=-0.154 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=30, min_samples_split=2;, score=-0.144 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=30, min_samples_split=2;, score=-0.152 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=30, min_samples_split=2;, score=-0.163 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=30, min_samples_split=2;, score=-0.154 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=30, min_samples_split=4;, score=-0.156 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=30, min_samples_split=4;, score=-0.145 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=30, min_samples_split=4;, score=-0.152 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=30, min_samples_split=4;, score=-0.159 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=30, min_samples_split=4;, score=-0.152 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=30, min_samples_split=10;, score=-0.145 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=30, min_samples_split=10;, score=-0.141 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=30, min_samples_split=10;, score=-0.149 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=30, min_samples_split=10;, score=-0.150 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=30, min_samples_split=10;, score=-0.143 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=50, min_samples_split=2;, score=-0.154 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=50, min_samples_split=2;, score=-0.144 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=50, min_samples_split=2;, score=-0.152 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=50, min_samples_split=2;, score=-0.163 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=50, min_samples_split=2;, score=-0.154 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=50, min_samples_split=4;, score=-0.156 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=50, min_samples_split=4;, score=-0.145 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=50, min_samples_split=4;, score=-0.152 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=50, min_samples_split=4;, score=-0.159 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=50, min_samples_split=4;, score=-0.152 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=50, min_samples_split=10;, score=-0.145 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=50, min_samples_split=10;, score=-0.141 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=50, min_samples_split=10;, score=-0.149 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=50, min_samples_split=10;, score=-0.150 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=50, min_samples_split=10;, score=-0.143 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2;, score=-0.118 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=4;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=4;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=4;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=4;, score=-0.118 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=4;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=10;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=10;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=10;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=10;, score=-0.116 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=10;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=15, min_samples_split=2;, score=-0.150 total time=   0.1s\n","[CV 2/5] END criterion=friedman_mse, max_depth=15, min_samples_split=2;, score=-0.142 total time=   0.1s\n","[CV 3/5] END criterion=friedman_mse, max_depth=15, min_samples_split=2;, score=-0.148 total time=   0.1s\n","[CV 4/5] END criterion=friedman_mse, max_depth=15, min_samples_split=2;, score=-0.157 total time=   0.1s\n","[CV 5/5] END criterion=friedman_mse, max_depth=15, min_samples_split=2;, score=-0.149 total time=   0.1s\n","[CV 1/5] END criterion=friedman_mse, max_depth=15, min_samples_split=4;, score=-0.151 total time=   0.1s\n","[CV 2/5] END criterion=friedman_mse, max_depth=15, min_samples_split=4;, score=-0.145 total time=   0.1s\n","[CV 3/5] END criterion=friedman_mse, max_depth=15, min_samples_split=4;, score=-0.153 total time=   0.1s\n","[CV 4/5] END criterion=friedman_mse, max_depth=15, min_samples_split=4;, score=-0.156 total time=   0.1s\n","[CV 5/5] END criterion=friedman_mse, max_depth=15, min_samples_split=4;, score=-0.149 total time=   0.1s\n","[CV 1/5] END criterion=friedman_mse, max_depth=15, min_samples_split=10;, score=-0.144 total time=   0.1s\n","[CV 2/5] END criterion=friedman_mse, max_depth=15, min_samples_split=10;, score=-0.143 total time=   0.1s\n","[CV 3/5] END criterion=friedman_mse, max_depth=15, min_samples_split=10;, score=-0.147 total time=   0.1s\n","[CV 4/5] END criterion=friedman_mse, max_depth=15, min_samples_split=10;, score=-0.150 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=15, min_samples_split=10;, score=-0.140 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=30, min_samples_split=2;, score=-0.154 total time=   0.0s\n","[CV 2/5] END criterion=friedman_mse, max_depth=30, min_samples_split=2;, score=-0.147 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=30, min_samples_split=2;, score=-0.157 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=30, min_samples_split=2;, score=-0.162 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=30, min_samples_split=2;, score=-0.155 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=30, min_samples_split=4;, score=-0.156 total time=   0.0s\n","[CV 2/5] END criterion=friedman_mse, max_depth=30, min_samples_split=4;, score=-0.142 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=30, min_samples_split=4;, score=-0.156 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=30, min_samples_split=4;, score=-0.163 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=30, min_samples_split=4;, score=-0.151 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=30, min_samples_split=10;, score=-0.147 total time=   0.0s\n","[CV 2/5] END criterion=friedman_mse, max_depth=30, min_samples_split=10;, score=-0.140 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=30, min_samples_split=10;, score=-0.149 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=30, min_samples_split=10;, score=-0.149 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=30, min_samples_split=10;, score=-0.143 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=50, min_samples_split=2;, score=-0.154 total time=   0.0s\n","[CV 2/5] END criterion=friedman_mse, max_depth=50, min_samples_split=2;, score=-0.147 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=50, min_samples_split=2;, score=-0.157 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=50, min_samples_split=2;, score=-0.162 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=50, min_samples_split=2;, score=-0.155 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=50, min_samples_split=4;, score=-0.156 total time=   0.1s\n","[CV 2/5] END criterion=friedman_mse, max_depth=50, min_samples_split=4;, score=-0.142 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=50, min_samples_split=4;, score=-0.156 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=50, min_samples_split=4;, score=-0.163 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=50, min_samples_split=4;, score=-0.151 total time=   0.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=50, min_samples_split=10;, score=-0.147 total time=   0.0s\n","[CV 2/5] END criterion=friedman_mse, max_depth=50, min_samples_split=10;, score=-0.140 total time=   0.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=50, min_samples_split=10;, score=-0.149 total time=   0.0s\n","[CV 4/5] END criterion=friedman_mse, max_depth=50, min_samples_split=10;, score=-0.149 total time=   0.0s\n","[CV 5/5] END criterion=friedman_mse, max_depth=50, min_samples_split=10;, score=-0.143 total time=   0.0s\n","Best parameters:  {'criterion': 'squared_error', 'max_depth': 5, 'min_samples_split': 10}\n","Best score:  -0.11677814694289994\n"]}],"source":["dec_tree = DecisionTreeRegressor(random_state=42)\n","\n","# SearchSpace\n","searchSpace = {\n","    'criterion': [\"squared_error\", \"friedman_mse\"],\n","    'max_depth': [5, 15, 30, 50],\n","    'min_samples_split': [2, 4, 10]\n","}\n","\n","dec_gs = grid_search(dec_tree, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":78,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:43:25.248864Z","iopub.status.busy":"2023-05-25T11:43:25.248454Z","iopub.status.idle":"2023-05-25T11:43:25.552061Z","shell.execute_reply":"2023-05-25T11:43:25.550955Z","shell.execute_reply.started":"2023-05-25T11:43:25.248831Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.116 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=10;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=15;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=15;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=15;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=15;, score=-0.117 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=15;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","Best parameters:  {'criterion': 'squared_error', 'max_depth': 5, 'min_samples_split': 20}\n","Best score:  -0.11674784779798006\n"]}],"source":["''' By previous GridSearch Run, it looks like we can go higher on min_samples_split '''\n","\n","# SearchSpace\n","searchSpace = {\n","    'criterion': [\"squared_error\"],\n","    'max_depth': [5],\n","    'min_samples_split': [10, 15, 20]\n","}\n","\n","dec_gs = grid_search(dec_tree, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":83,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:51:13.588716Z","iopub.status.busy":"2023-05-25T11:51:13.588309Z","iopub.status.idle":"2023-05-25T11:51:14.094530Z","shell.execute_reply":"2023-05-25T11:51:14.092924Z","shell.execute_reply.started":"2023-05-25T11:51:13.588683Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.116 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.116 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.116 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.115 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.110 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.114 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.111 total time=   0.0s\n","Best parameters:  {'criterion': 'squared_error', 'max_depth': 5, 'min_samples_split': 1000}\n","Best score:  -0.11043056180855884\n"]}],"source":["''' Let's set even higher value for min_samples_split '''\n","\n","# SearchSpace\n","searchSpace = {\n","    'criterion': [\"squared_error\"], \n","    'max_depth': [5],\n","    'min_samples_split': [20, 50, 100, 150, 500, 1000, 2000]\n","}\n","\n","dec_gs = grid_search(dec_tree, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":84,"metadata":{"collapsed":true,"execution":{"iopub.execute_input":"2023-05-25T11:51:27.018803Z","iopub.status.busy":"2023-05-25T11:51:27.018429Z","iopub.status.idle":"2023-05-25T11:51:31.848948Z","shell.execute_reply":"2023-05-25T11:51:31.847688Z","shell.execute_reply.started":"2023-05-25T11:51:27.018774Z"},"jupyter":{"outputs_hidden":true},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.114 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.111 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.121 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.121 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.119 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.112 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.108 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.115 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.105 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.108 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.106 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.109 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.116 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.115 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.119 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.115 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.117 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.111 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.112 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.115 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.108 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.111 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.105 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.115 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.108 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.106 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.116 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.109 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.115 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.116 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.115 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.116 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.110 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.116 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.110 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.113 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.109 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.106 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.116 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.114 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.117 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.112 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.117 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.116 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.109 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.105 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.113 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.106 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.110 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.111 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.119 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.111 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.121 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.121 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.116 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.111 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.120 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.121 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.112 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.121 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.115 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.111 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.106 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.106 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.111 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.119 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.116 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.115 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.116 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.113 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.116 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.107 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.106 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.111 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.121 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.119 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.117 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.117 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.115 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.117 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.116 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.117 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.110 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.115 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.110 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.106 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.113 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.110 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.105 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.110 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.107 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.119 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.116 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.121 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.118 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.118 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.114 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.117 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.120 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.117 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.116 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.117 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.116 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.115 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.111 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.111 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.112 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.112 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.122 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.116 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.110 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.115 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.116 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.111 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.110 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.117 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.112 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.110 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.114 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.111 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.109 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.115 total time=   0.0s\n","Fitting 5 folds for each of 7 candidates, totalling 35 fits\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.123 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.116 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.118 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.113 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=20;, score=-0.117 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.121 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.115 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.115 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.111 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=50;, score=-0.113 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.120 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.114 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.115 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.110 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=100;, score=-0.112 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.117 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.111 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.108 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=150;, score=-0.111 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.115 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.112 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.109 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.107 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=500;, score=-0.105 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.111 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=1000;, score=-0.104 total time=   0.0s\n","[CV 1/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.114 total time=   0.0s\n","[CV 2/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.113 total time=   0.0s\n","[CV 3/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.110 total time=   0.0s\n","[CV 4/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.109 total time=   0.0s\n","[CV 5/5] END criterion=squared_error, max_depth=5, min_samples_split=2000;, score=-0.106 total time=   0.0s\n"]}],"source":["score = cross_val_score(dec_gs, X_train, y_train, cv=10)"]},{"cell_type":"code","execution_count":85,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:51:31.852158Z","iopub.status.busy":"2023-05-25T11:51:31.851398Z","iopub.status.idle":"2023-05-25T11:51:31.860759Z","shell.execute_reply":"2023-05-25T11:51:31.859432Z","shell.execute_reply.started":"2023-05-25T11:51:31.852112Z"},"trusted":true},"outputs":[{"data":{"text/plain":["array([-0.11681739, -0.11050197, -0.10987398, -0.11138552, -0.10992735,\n","       -0.11027842, -0.11288172, -0.10206279, -0.10795301, -0.1180055 ])"]},"execution_count":85,"metadata":{},"output_type":"execute_result"}],"source":["score"]},{"cell_type":"markdown","metadata":{},"source":["**Scores are pretty descent**"]},{"cell_type":"markdown","metadata":{},"source":["### Random Forest"]},{"cell_type":"code","execution_count":89,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T11:57:10.549462Z","iopub.status.busy":"2023-05-25T11:57:10.549016Z","iopub.status.idle":"2023-05-25T12:00:38.371059Z","shell.execute_reply":"2023-05-25T12:00:38.369942Z","shell.execute_reply.started":"2023-05-25T11:57:10.549425Z"},"scrolled":true,"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 72 candidates, totalling 360 fits\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.107 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.111 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   0.7s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.107 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.107 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.110 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.107 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.107 total time=   0.4s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.4s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   1.0s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   0.7s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.107 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.110 total time=   0.7s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.114 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.107 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.107 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.114 total time=   1.4s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.107 total time=   1.1s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   1.0s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.2s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.112 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.2s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   1.1s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.107 total time=   0.9s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   1.0s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.2s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.107 total time=   1.5s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.2s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.6s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   1.1s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.0s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   0.9s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   0.9s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.6s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.2s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   1.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.1s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   1.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.0s\n","[CV 2/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 3/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 4/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 5/5] END bootstrap=True, criterion=squared_error, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   0.9s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.107 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.111 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.107 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.107 total time=   0.7s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.110 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.107 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   0.9s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   0.8s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.107 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.110 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.114 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.114 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.107 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.1s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   1.1s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   1.0s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.0s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.112 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   0.9s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   0.9s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.107 total time=   0.9s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   0.9s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.114 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.114 total time=   0.8s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.111 total time=   0.6s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.2s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.6s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   1.1s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.0s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.1s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.8s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.7s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.9s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.9s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   1.1s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.1s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.1s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.5s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.5s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.5s\n","[CV 1/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.0s\n","[CV 2/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 3/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.0s\n","[CV 4/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   1.0s\n","[CV 5/5] END bootstrap=True, criterion=friedman_mse, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.0s\n","Best parameters:  {'bootstrap': True, 'criterion': 'friedman_mse', 'max_depth': 5, 'min_samples_split': 2, 'n_estimators': 200}\n","Best score:  -0.10926676204348817\n"]}],"source":["ran_forest = RandomForestRegressor(random_state=42, max_features='sqrt', bootstrap=True)\n","\n","# SearchSpace\n","searchSpace = {\n","    'n_estimators': [50, 100, 200],\n","    'criterion': [\"squared_error\", \"friedman_mse\"], \n","    'max_depth': [5, 15, 30, 50],\n","    'min_samples_split': [2, 4, 10]\n","}\n","\n","ran_gs = grid_search(ran_forest, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":90,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T12:03:20.974166Z","iopub.status.busy":"2023-05-25T12:03:20.973355Z","iopub.status.idle":"2023-05-25T12:03:36.509260Z","shell.execute_reply":"2023-05-25T12:03:36.508183Z","shell.execute_reply.started":"2023-05-25T12:03:20.974128Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.107 total time=   0.6s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   0.6s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=300;, score=-0.112 total time=   0.9s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=300;, score=-0.108 total time=   0.9s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=300;, score=-0.108 total time=   0.9s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=300;, score=-0.107 total time=   0.9s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=300;, score=-0.110 total time=   1.0s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.112 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.108 total time=   1.3s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.108 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.107 total time=   1.3s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.3s\n","Best parameters:  {'criterion': 'friedman_mse', 'max_depth': 5, 'min_samples_split': 2, 'n_estimators': 400}\n","Best score:  -0.10926486461156323\n"]}],"source":["ran_forest = RandomForestRegressor(random_state=42, max_features='sqrt', bootstrap=True)\n","\n","# SearchSpace\n","searchSpace = {\n","    'n_estimators': [200, 300, 400],\n","    'criterion': [\"friedman_mse\"],\n","    'max_depth': [5],\n","    'min_samples_split': [2]\n","}\n","\n","ran_gs = grid_search(ran_forest, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":91,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T12:04:59.333703Z","iopub.status.busy":"2023-05-25T12:04:59.333101Z","iopub.status.idle":"2023-05-25T12:05:29.134917Z","shell.execute_reply":"2023-05-25T12:05:29.133941Z","shell.execute_reply.started":"2023-05-25T12:04:59.333667Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.112 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.108 total time=   1.2s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.108 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.107 total time=   1.2s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.112 total time=   1.9s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.108 total time=   1.9s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.108 total time=   1.8s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.107 total time=   1.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.112 total time=   2.5s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.108 total time=   2.5s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.108 total time=   2.6s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.107 total time=   2.9s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","Best parameters:  {'criterion': 'friedman_mse', 'max_depth': 5, 'min_samples_split': 2, 'n_estimators': 400}\n","Best score:  -0.10926486461156323\n"]}],"source":["ran_forest = RandomForestRegressor(random_state=42, max_features='sqrt', bootstrap=True)\n","\n","# SearchSpace\n","searchSpace = {\n","    'n_estimators': [400, 600, 800],\n","    'criterion': [\"friedman_mse\"],\n","    'max_depth': [5],\n","    'min_samples_split': [2]\n","}\n","\n","ran_gs = grid_search(ran_forest, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":93,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T12:10:45.139530Z","iopub.status.busy":"2023-05-25T12:10:45.138735Z","iopub.status.idle":"2023-05-25T12:15:44.532792Z","shell.execute_reply":"2023-05-25T12:15:44.531843Z","shell.execute_reply.started":"2023-05-25T12:10:45.139494Z"},"scrolled":true,"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.111 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.106 total time=   1.2s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.3s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.106 total time=   1.2s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.106 total time=   1.8s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.9s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.106 total time=   1.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.106 total time=   2.4s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.8s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.106 total time=   2.5s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.113 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.106 total time=   1.2s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.106 total time=   1.2s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.3s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.113 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.106 total time=   1.8s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.9s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.107 total time=   1.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.113 total time=   2.5s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.106 total time=   2.4s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.107 total time=   2.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.113 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.107 total time=   1.2s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.107 total time=   1.2s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.109 total time=   1.2s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.113 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.108 total time=   1.9s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.111 total time=   1.9s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.107 total time=   1.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.109 total time=   1.9s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.113 total time=   2.4s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.108 total time=   2.4s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.5s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.107 total time=   2.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.109 total time=   2.9s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.112 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.108 total time=   1.3s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.111 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.107 total time=   1.2s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.112 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.108 total time=   2.0s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.111 total time=   1.8s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.107 total time=   2.1s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.112 total time=   2.5s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.108 total time=   2.6s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.111 total time=   2.4s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.107 total time=   2.5s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.7s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.113 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.109 total time=   1.2s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.109 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.107 total time=   1.3s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.113 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.109 total time=   1.8s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.109 total time=   1.8s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.106 total time=   1.9s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.113 total time=   2.5s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.109 total time=   2.5s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.109 total time=   2.4s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.106 total time=   2.5s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.113 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.109 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.107 total time=   1.2s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.113 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.109 total time=   1.8s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.107 total time=   1.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.9s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.113 total time=   2.4s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.109 total time=   2.4s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.107 total time=   2.4s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.113 total time=   1.5s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.3s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.109 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.105 total time=   1.2s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.109 total time=   1.2s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.113 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.109 total time=   1.9s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.105 total time=   1.9s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.9s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.113 total time=   2.5s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.109 total time=   2.5s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.109 total time=   2.5s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.105 total time=   2.4s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.113 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.7s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.108 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.3s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.113 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.108 total time=   1.8s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.9s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.113 total time=   2.4s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.108 total time=   2.4s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.5s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.4s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.113 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.109 total time=   1.2s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.108 total time=   1.6s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.106 total time=   1.4s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.113 total time=   1.5s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.112 total time=   1.9s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.109 total time=   2.1s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.108 total time=   1.8s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.106 total time=   1.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.113 total time=   1.9s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.112 total time=   2.4s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.109 total time=   2.4s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.108 total time=   2.4s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.106 total time=   2.4s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.113 total time=   2.5s\n","Fitting 5 folds for each of 3 candidates, totalling 15 fits\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.112 total time=   1.2s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.108 total time=   1.2s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.106 total time=   1.4s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=400;, score=-0.105 total time=   1.4s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.112 total time=   1.8s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.110 total time=   1.8s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.108 total time=   1.8s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.106 total time=   1.8s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=600;, score=-0.105 total time=   1.8s\n","[CV 1/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.112 total time=   2.5s\n","[CV 2/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.110 total time=   2.5s\n","[CV 3/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.108 total time=   2.4s\n","[CV 4/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.106 total time=   2.4s\n","[CV 5/5] END criterion=friedman_mse, max_depth=5, min_samples_split=2, n_estimators=800;, score=-0.105 total time=   2.4s\n"]}],"source":["score = cross_val_score(ran_gs, X_train, y_train, cv=10)"]},{"cell_type":"code","execution_count":94,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T12:15:44.535082Z","iopub.status.busy":"2023-05-25T12:15:44.534727Z","iopub.status.idle":"2023-05-25T12:15:44.545265Z","shell.execute_reply":"2023-05-25T12:15:44.544319Z","shell.execute_reply.started":"2023-05-25T12:15:44.535047Z"},"trusted":true},"outputs":[{"data":{"text/plain":["array([-0.11480452, -0.1103527 , -0.10898201, -0.10733326, -0.10842364,\n","       -0.10791492, -0.11135623, -0.10268021, -0.10291638, -0.11710867])"]},"execution_count":94,"metadata":{},"output_type":"execute_result"}],"source":["score"]},{"cell_type":"markdown","metadata":{},"source":["### ADA Boost"]},{"cell_type":"code","execution_count":32,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T14:00:39.914926Z","iopub.status.busy":"2023-05-25T14:00:39.914607Z","iopub.status.idle":"2023-05-25T14:01:47.999824Z","shell.execute_reply":"2023-05-25T14:01:47.998090Z","shell.execute_reply.started":"2023-05-25T14:00:39.914903Z"},"scrolled":true,"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 12 candidates, totalling 60 fits\n","[CV 1/5] END learning_rate=0.01, n_estimators=50;, score=-0.113 total time=   0.5s\n","[CV 2/5] END learning_rate=0.01, n_estimators=50;, score=-0.109 total time=   0.5s\n","[CV 3/5] END learning_rate=0.01, n_estimators=50;, score=-0.108 total time=   0.5s\n","[CV 4/5] END learning_rate=0.01, n_estimators=50;, score=-0.107 total time=   0.5s\n","[CV 5/5] END learning_rate=0.01, n_estimators=50;, score=-0.111 total time=   0.5s\n","[CV 1/5] END learning_rate=0.01, n_estimators=100;, score=-0.112 total time=   1.1s\n","[CV 2/5] END learning_rate=0.01, n_estimators=100;, score=-0.109 total time=   1.1s\n","[CV 3/5] END learning_rate=0.01, n_estimators=100;, score=-0.108 total time=   1.1s\n","[CV 4/5] END learning_rate=0.01, n_estimators=100;, score=-0.106 total time=   1.1s\n","[CV 5/5] END learning_rate=0.01, n_estimators=100;, score=-0.111 total time=   1.1s\n","[CV 1/5] END learning_rate=0.01, n_estimators=200;, score=-0.112 total time=   2.1s\n","[CV 2/5] END learning_rate=0.01, n_estimators=200;, score=-0.109 total time=   2.1s\n","[CV 3/5] END learning_rate=0.01, n_estimators=200;, score=-0.108 total time=   2.1s\n","[CV 4/5] END learning_rate=0.01, n_estimators=200;, score=-0.106 total time=   2.2s\n","[CV 5/5] END learning_rate=0.01, n_estimators=200;, score=-0.111 total time=   2.1s\n","[CV 1/5] END learning_rate=0.05, n_estimators=50;, score=-0.112 total time=   0.5s\n","[CV 2/5] END learning_rate=0.05, n_estimators=50;, score=-0.109 total time=   0.5s\n","[CV 3/5] END learning_rate=0.05, n_estimators=50;, score=-0.108 total time=   0.5s\n","[CV 4/5] END learning_rate=0.05, n_estimators=50;, score=-0.107 total time=   0.5s\n","[CV 5/5] END learning_rate=0.05, n_estimators=50;, score=-0.112 total time=   0.5s\n","[CV 1/5] END learning_rate=0.05, n_estimators=100;, score=-0.112 total time=   1.0s\n","[CV 2/5] END learning_rate=0.05, n_estimators=100;, score=-0.108 total time=   1.1s\n","[CV 3/5] END learning_rate=0.05, n_estimators=100;, score=-0.108 total time=   1.1s\n","[CV 4/5] END learning_rate=0.05, n_estimators=100;, score=-0.107 total time=   1.1s\n","[CV 5/5] END learning_rate=0.05, n_estimators=100;, score=-0.112 total time=   1.1s\n","[CV 1/5] END learning_rate=0.05, n_estimators=200;, score=-0.112 total time=   2.0s\n","[CV 2/5] END learning_rate=0.05, n_estimators=200;, score=-0.108 total time=   2.0s\n","[CV 3/5] END learning_rate=0.05, n_estimators=200;, score=-0.109 total time=   2.1s\n","[CV 4/5] END learning_rate=0.05, n_estimators=200;, score=-0.107 total time=   2.1s\n","[CV 5/5] END learning_rate=0.05, n_estimators=200;, score=-0.112 total time=   2.0s\n","[CV 1/5] END learning_rate=0.1, n_estimators=50;, score=-0.112 total time=   0.5s\n","[CV 2/5] END learning_rate=0.1, n_estimators=50;, score=-0.108 total time=   0.5s\n","[CV 3/5] END learning_rate=0.1, n_estimators=50;, score=-0.109 total time=   0.5s\n","[CV 4/5] END learning_rate=0.1, n_estimators=50;, score=-0.107 total time=   0.5s\n","[CV 5/5] END learning_rate=0.1, n_estimators=50;, score=-0.111 total time=   0.5s\n","[CV 1/5] END learning_rate=0.1, n_estimators=100;, score=-0.111 total time=   1.0s\n","[CV 2/5] END learning_rate=0.1, n_estimators=100;, score=-0.108 total time=   1.0s\n","[CV 3/5] END learning_rate=0.1, n_estimators=100;, score=-0.109 total time=   1.0s\n","[CV 4/5] END learning_rate=0.1, n_estimators=100;, score=-0.107 total time=   1.0s\n","[CV 5/5] END learning_rate=0.1, n_estimators=100;, score=-0.111 total time=   1.0s\n","[CV 1/5] END learning_rate=0.1, n_estimators=200;, score=-0.111 total time=   1.9s\n","[CV 2/5] END learning_rate=0.1, n_estimators=200;, score=-0.108 total time=   1.9s\n","[CV 3/5] END learning_rate=0.1, n_estimators=200;, score=-0.110 total time=   1.9s\n","[CV 4/5] END learning_rate=0.1, n_estimators=200;, score=-0.107 total time=   1.9s\n","[CV 5/5] END learning_rate=0.1, n_estimators=200;, score=-0.112 total time=   1.9s\n","[CV 1/5] END learning_rate=0.5, n_estimators=50;, score=-0.112 total time=   0.5s\n","[CV 2/5] END learning_rate=0.5, n_estimators=50;, score=-0.108 total time=   0.5s\n","[CV 3/5] END learning_rate=0.5, n_estimators=50;, score=-0.111 total time=   0.5s\n","[CV 4/5] END learning_rate=0.5, n_estimators=50;, score=-0.108 total time=   0.5s\n","[CV 5/5] END learning_rate=0.5, n_estimators=50;, score=-0.112 total time=   0.5s\n","[CV 1/5] END learning_rate=0.5, n_estimators=100;, score=-0.111 total time=   0.8s\n","[CV 2/5] END learning_rate=0.5, n_estimators=100;, score=-0.108 total time=   0.8s\n","[CV 3/5] END learning_rate=0.5, n_estimators=100;, score=-0.111 total time=   0.8s\n","[CV 4/5] END learning_rate=0.5, n_estimators=100;, score=-0.108 total time=   0.8s\n","[CV 5/5] END learning_rate=0.5, n_estimators=100;, score=-0.112 total time=   0.6s\n","[CV 1/5] END learning_rate=0.5, n_estimators=200;, score=-0.112 total time=   1.4s\n","[CV 2/5] END learning_rate=0.5, n_estimators=200;, score=-0.108 total time=   1.4s\n","[CV 3/5] END learning_rate=0.5, n_estimators=200;, score=-0.113 total time=   1.4s\n","[CV 4/5] END learning_rate=0.5, n_estimators=200;, score=-0.108 total time=   1.4s\n","[CV 5/5] END learning_rate=0.5, n_estimators=200;, score=-0.112 total time=   0.5s\n","Best parameters:  {'learning_rate': 0.1, 'n_estimators': 100}\n","Best score:  -0.1093742861239341\n"]}],"source":["ada_boost = AdaBoostRegressor(random_state=42)\n","\n","# SearchSpace\n","searchSpace = {\n","    'n_estimators': [50, 100, 200],\n","    'learning_rate': [0.01, 0.05, 0.1, 0.5]\n","}\n","\n","ada_gs = grid_search(ada_boost, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":35,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T14:03:24.507074Z","iopub.status.busy":"2023-05-25T14:03:24.506696Z","iopub.status.idle":"2023-05-25T14:03:28.580560Z","shell.execute_reply":"2023-05-25T14:03:28.579308Z","shell.execute_reply.started":"2023-05-25T14:03:24.507043Z"},"trusted":true},"outputs":[],"source":["score = cross_val_score(ada_boost, X_train, y_train, cv=10)"]},{"cell_type":"code","execution_count":36,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T14:03:37.738788Z","iopub.status.busy":"2023-05-25T14:03:37.738410Z","iopub.status.idle":"2023-05-25T14:03:37.745841Z","shell.execute_reply":"2023-05-25T14:03:37.744657Z","shell.execute_reply.started":"2023-05-25T14:03:37.738758Z"},"trusted":true},"outputs":[{"data":{"text/plain":["array([ 0.0296562 ,  0.03034349,  0.06617951, -0.00294066,  0.0251807 ,\n","        0.02260289,  0.02708428, -0.02191224, -0.06104863, -0.04501389])"]},"execution_count":36,"metadata":{},"output_type":"execute_result"}],"source":["score"]},{"cell_type":"markdown","metadata":{},"source":["### Gradient Boost"]},{"cell_type":"code","execution_count":41,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T14:09:59.056381Z","iopub.status.busy":"2023-05-25T14:09:59.055993Z","iopub.status.idle":"2023-05-25T14:17:22.707463Z","shell.execute_reply":"2023-05-25T14:17:22.706076Z","shell.execute_reply.started":"2023-05-25T14:09:59.056356Z"},"scrolled":true,"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 144 candidates, totalling 720 fits\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.4s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.107 total time=   0.4s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.4s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   0.7s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   0.7s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   0.7s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.107 total time=   0.7s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   0.7s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   0.7s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   0.6s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   0.6s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.107 total time=   0.6s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   0.6s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   0.7s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   0.6s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   0.6s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.107 total time=   0.7s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   0.6s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.7s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.7s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.5s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   1.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   1.4s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   1.4s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   1.4s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.7s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.114 total time=   1.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.4s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   1.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.6s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.6s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.6s\n","[CV 1/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.2s\n","[CV 2/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.110 total time=   1.2s\n","[CV 3/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.2s\n","[CV 4/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.2s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.4s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.8s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.8s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.8s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.8s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.8s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.114 total time=   1.6s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.6s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.5s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.5s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.6s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.4s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   1.5s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.5s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.4s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.110 total time=   1.4s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.6s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.6s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.7s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.6s\n","[CV 1/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.114 total time=   1.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   1.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   1.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.108 total time=   0.4s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.8s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.8s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.8s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.8s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.114 total time=   1.5s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.5s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.5s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.108 total time=   1.5s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.5s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.114 total time=   0.4s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.4s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.7s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.109 total time=   0.7s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.7s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.115 total time=   1.4s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.4s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   1.4s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   1.4s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.4s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.6s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.6s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.6s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.6s\n","[CV 1/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.3s\n","[CV 2/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 3/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   1.3s\n","[CV 4/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.108 total time=   1.3s\n","[CV 5/5] END learning_rate=0.01, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.114 total time=   0.2s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.115 total time=   0.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.108 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.116 total time=   0.6s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   0.6s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   0.6s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.114 total time=   0.6s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.106 total time=   0.2s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.2s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.114 total time=   0.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.116 total time=   0.6s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   0.6s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   0.6s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.114 total time=   0.6s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.114 total time=   0.1s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.107 total time=   0.2s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.111 total time=   0.2s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.114 total time=   0.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.107 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   0.6s\n","[CV 2/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 3/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.110 total time=   0.6s\n","[CV 4/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   0.6s\n","[CV 5/5] END learning_rate=0.05, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   0.6s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.116 total time=   0.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.116 total time=   0.7s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.116 total time=   1.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.4s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   1.4s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   1.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.114 total time=   0.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.114 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.115 total time=   0.6s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.6s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.108 total time=   0.6s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.6s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.115 total time=   1.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   1.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.108 total time=   1.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.114 total time=   1.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.116 total time=   0.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.116 total time=   0.6s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.112 total time=   0.6s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.6s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.6s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.6s\n","[CV 1/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.116 total time=   1.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   1.2s\n","[CV 3/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.116 total time=   0.4s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.4s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.116 total time=   0.8s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.8s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.8s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.111 total time=   0.8s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.116 total time=   1.5s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.5s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   1.5s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   1.5s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.5s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.117 total time=   0.4s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.4s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.117 total time=   0.7s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.117 total time=   1.5s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.4s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.110 total time=   1.5s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.110 total time=   1.4s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.5s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.115 total time=   0.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.111 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.115 total time=   0.7s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.7s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.109 total time=   0.7s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 1/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   1.4s\n","[CV 2/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.4s\n","[CV 3/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.110 total time=   1.4s\n","[CV 4/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.109 total time=   1.4s\n","[CV 5/5] END learning_rate=0.05, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.114 total time=   1.4s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.116 total time=   0.4s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.4s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.4s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.4s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.116 total time=   0.8s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.109 total time=   0.7s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.116 total time=   2.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.5s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.109 total time=   1.5s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   1.5s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.5s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.115 total time=   0.4s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.115 total time=   0.7s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.7s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.115 total time=   1.5s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   1.5s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.5s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.110 total time=   1.5s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.4s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.115 total time=   0.3s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.3s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.3s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.115 total time=   0.7s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 1/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   1.4s\n","[CV 2/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   1.4s\n","[CV 3/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.110 total time=   1.4s\n","[CV 4/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.110 total time=   1.4s\n","[CV 5/5] END learning_rate=0.05, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.114 total time=   1.4s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.115 total time=   0.2s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.114 total time=   0.2s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.117 total time=   0.3s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.3s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.3s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.116 total time=   0.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.120 total time=   0.6s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.114 total time=   0.6s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.115 total time=   0.6s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   0.6s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.118 total time=   0.6s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.116 total time=   0.2s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.2s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.108 total time=   0.2s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.115 total time=   0.2s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.119 total time=   0.3s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.115 total time=   0.3s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.3s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.117 total time=   0.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.121 total time=   0.6s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.116 total time=   0.6s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.114 total time=   0.6s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.118 total time=   0.6s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.115 total time=   0.2s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.111 total time=   0.2s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.110 total time=   0.2s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.109 total time=   0.2s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.114 total time=   0.2s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.118 total time=   0.3s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.3s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.112 total time=   0.3s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.110 total time=   0.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.116 total time=   0.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.120 total time=   0.6s\n","[CV 2/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   0.6s\n","[CV 3/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   0.6s\n","[CV 4/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   0.6s\n","[CV 5/5] END learning_rate=0.1, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.117 total time=   0.6s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.116 total time=   0.3s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.114 total time=   0.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.116 total time=   0.7s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.8s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.116 total time=   1.3s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.4s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.114 total time=   1.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.117 total time=   0.3s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.115 total time=   0.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.117 total time=   0.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.117 total time=   0.7s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.115 total time=   0.6s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.117 total time=   0.7s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.117 total time=   1.4s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.3s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   1.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.115 total time=   1.4s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.117 total time=   1.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.118 total time=   0.3s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.3s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.114 total time=   0.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.115 total time=   0.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.118 total time=   0.6s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.114 total time=   0.6s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.115 total time=   0.6s\n","[CV 1/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.118 total time=   1.2s\n","[CV 2/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.114 total time=   1.2s\n","[CV 3/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.3s\n","[CV 4/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.114 total time=   1.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   1.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.118 total time=   0.4s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.114 total time=   0.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.112 total time=   0.4s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.118 total time=   0.8s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.114 total time=   0.8s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.110 total time=   0.8s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.112 total time=   0.8s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.8s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.118 total time=   1.2s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.114 total time=   1.2s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.110 total time=   1.2s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.112 total time=   1.2s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.1s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.117 total time=   0.4s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.110 total time=   0.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.117 total time=   0.7s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.8s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.110 total time=   0.7s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.117 total time=   1.5s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.110 total time=   1.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   1.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   1.4s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.115 total time=   0.4s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.114 total time=   0.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.114 total time=   0.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.3s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.115 total time=   0.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.116 total time=   0.7s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.115 total time=   0.7s\n","[CV 1/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.116 total time=   1.4s\n","[CV 2/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.114 total time=   1.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.114 total time=   1.5s\n","[CV 4/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   1.5s\n","[CV 5/5] END learning_rate=0.1, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   1.4s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.118 total time=   0.4s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.114 total time=   0.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.118 total time=   0.8s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.114 total time=   0.8s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.111 total time=   0.8s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.111 total time=   0.8s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.113 total time=   0.8s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.118 total time=   1.2s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.114 total time=   1.2s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.2s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.111 total time=   1.2s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.113 total time=   1.2s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.116 total time=   0.4s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.114 total time=   0.4s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.112 total time=   0.4s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.116 total time=   0.7s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.7s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.111 total time=   0.7s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.114 total time=   0.7s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.112 total time=   0.8s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.116 total time=   1.6s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.5s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   1.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.114 total time=   1.5s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.112 total time=   1.4s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.117 total time=   0.4s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.113 total time=   0.4s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.111 total time=   0.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.112 total time=   0.4s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.115 total time=   0.3s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.117 total time=   0.7s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.113 total time=   0.7s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.111 total time=   0.7s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.112 total time=   0.7s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.115 total time=   0.7s\n","[CV 1/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.117 total time=   1.5s\n","[CV 2/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.113 total time=   1.5s\n","[CV 3/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.111 total time=   1.4s\n","[CV 4/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.112 total time=   1.6s\n","[CV 5/5] END learning_rate=0.1, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.115 total time=   1.5s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.131 total time=   0.2s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.126 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.132 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.125 total time=   0.2s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=50;, score=-0.132 total time=   0.2s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.135 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.128 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.133 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.128 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=100;, score=-0.134 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.135 total time=   0.6s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.128 total time=   0.6s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.134 total time=   0.7s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.129 total time=   0.6s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=2, n_estimators=200;, score=-0.135 total time=   0.6s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.131 total time=   0.2s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.126 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.123 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.125 total time=   0.2s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=50;, score=-0.129 total time=   0.2s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.133 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.129 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.125 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.127 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=100;, score=-0.133 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.134 total time=   0.6s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.129 total time=   0.6s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.126 total time=   0.6s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.128 total time=   0.6s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.133 total time=   0.7s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.129 total time=   0.2s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.128 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.125 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.129 total time=   0.2s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=50;, score=-0.129 total time=   0.2s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.130 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.132 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.128 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.129 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=100;, score=-0.131 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.131 total time=   0.6s\n","[CV 2/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.132 total time=   0.6s\n","[CV 3/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.129 total time=   0.6s\n","[CV 4/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.129 total time=   0.6s\n","[CV 5/5] END learning_rate=0.5, max_depth=5, min_samples_split=10, n_estimators=200;, score=-0.131 total time=   0.6s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.131 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.130 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.128 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.130 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=50;, score=-0.130 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.131 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.130 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.128 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.130 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=100;, score=-0.130 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.131 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.130 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.128 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.130 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=2, n_estimators=200;, score=-0.130 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.131 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.129 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.127 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.127 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=50;, score=-0.132 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.131 total time=   0.4s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.129 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.127 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.127 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=100;, score=-0.132 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.131 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.129 total time=   0.4s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.127 total time=   0.4s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.127 total time=   0.4s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=4, n_estimators=200;, score=-0.132 total time=   0.4s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.132 total time=   0.4s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.123 total time=   0.4s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.126 total time=   0.4s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.123 total time=   0.4s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=50;, score=-0.130 total time=   0.4s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.132 total time=   0.5s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.123 total time=   0.5s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.126 total time=   0.5s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.123 total time=   0.5s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=100;, score=-0.130 total time=   0.5s\n","[CV 1/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.132 total time=   0.5s\n","[CV 2/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.123 total time=   0.4s\n","[CV 3/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.126 total time=   0.5s\n","[CV 4/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.123 total time=   0.5s\n","[CV 5/5] END learning_rate=0.5, max_depth=15, min_samples_split=10, n_estimators=200;, score=-0.130 total time=   0.5s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.132 total time=   0.2s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.130 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.128 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.128 total time=   0.2s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=50;, score=-0.131 total time=   0.2s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.132 total time=   0.2s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.130 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.128 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.128 total time=   0.2s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=100;, score=-0.131 total time=   0.2s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.132 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.130 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.128 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.128 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=2, n_estimators=200;, score=-0.131 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.134 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.131 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.123 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.127 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=50;, score=-0.126 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.134 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.131 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.123 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.127 total time=   0.4s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=100;, score=-0.126 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.134 total time=   0.4s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.131 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.123 total time=   0.4s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.127 total time=   0.4s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=4, n_estimators=200;, score=-0.126 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.127 total time=   0.4s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.129 total time=   0.5s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.127 total time=   0.4s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.121 total time=   0.4s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=50;, score=-0.127 total time=   0.4s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.127 total time=   0.5s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.129 total time=   0.5s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.127 total time=   0.5s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.121 total time=   0.5s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=100;, score=-0.127 total time=   0.5s\n","[CV 1/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.127 total time=   0.5s\n","[CV 2/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.129 total time=   0.6s\n","[CV 3/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.127 total time=   0.5s\n","[CV 4/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.121 total time=   0.6s\n","[CV 5/5] END learning_rate=0.5, max_depth=30, min_samples_split=10, n_estimators=200;, score=-0.127 total time=   0.5s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.132 total time=   0.2s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.127 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.130 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.127 total time=   0.2s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=50;, score=-0.131 total time=   0.2s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.132 total time=   0.2s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.127 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.130 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.127 total time=   0.2s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=100;, score=-0.131 total time=   0.2s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.132 total time=   0.2s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.127 total time=   0.2s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.130 total time=   0.2s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.127 total time=   0.2s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=2, n_estimators=200;, score=-0.131 total time=   0.2s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.134 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.131 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.123 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.129 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=50;, score=-0.126 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.134 total time=   0.3s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.131 total time=   0.3s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.123 total time=   0.3s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.129 total time=   0.3s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=100;, score=-0.126 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.134 total time=   0.4s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.131 total time=   0.4s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.123 total time=   0.4s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.129 total time=   0.4s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=4, n_estimators=200;, score=-0.126 total time=   0.3s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.126 total time=   0.5s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.129 total time=   0.4s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.126 total time=   0.4s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.120 total time=   0.5s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=50;, score=-0.127 total time=   0.5s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.126 total time=   0.6s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.129 total time=   0.5s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.126 total time=   0.5s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.120 total time=   0.6s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=100;, score=-0.127 total time=   0.5s\n","[CV 1/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.126 total time=   0.6s\n","[CV 2/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.129 total time=   0.5s\n","[CV 3/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.126 total time=   0.5s\n","[CV 4/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.120 total time=   0.6s\n","[CV 5/5] END learning_rate=0.5, max_depth=50, min_samples_split=10, n_estimators=200;, score=-0.127 total time=   0.6s\n","Best parameters:  {'learning_rate': 0.01, 'max_depth': 5, 'min_samples_split': 4, 'n_estimators': 200}\n","Best score:  -0.10954294100812154\n"]}],"source":["grad_boost = GradientBoostingRegressor(random_state=42, max_features='sqrt')\n","\n","# SearchSpace\n","searchSpace = {\n","    'n_estimators': [50, 100, 200],\n","    'learning_rate': [0.01, 0.05, 0.1, 0.5],\n","    'max_depth': [5, 15, 30, 50],\n","    'min_samples_split': [2, 4, 10]\n","}\n","\n","grad_gs = grid_search(grad_boost, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":42,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T14:18:30.342409Z","iopub.status.busy":"2023-05-25T14:18:30.341058Z","iopub.status.idle":"2023-05-25T14:18:51.979814Z","shell.execute_reply":"2023-05-25T14:18:51.978296Z","shell.execute_reply.started":"2023-05-25T14:18:30.342352Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Fitting 5 folds for each of 4 candidates, totalling 20 fits\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.113 total time=   0.6s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   0.6s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.109 total time=   0.6s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.107 total time=   0.6s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=200;, score=-0.111 total time=   0.6s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=300;, score=-0.113 total time=   0.9s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=300;, score=-0.109 total time=   0.9s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=300;, score=-0.109 total time=   0.9s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=300;, score=-0.107 total time=   0.9s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=300;, score=-0.111 total time=   0.9s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=400;, score=-0.113 total time=   1.2s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=400;, score=-0.110 total time=   1.2s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=400;, score=-0.109 total time=   1.2s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=400;, score=-0.107 total time=   1.2s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=400;, score=-0.112 total time=   1.2s\n","[CV 1/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=500;, score=-0.114 total time=   1.6s\n","[CV 2/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=500;, score=-0.110 total time=   1.5s\n","[CV 3/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=500;, score=-0.109 total time=   1.5s\n","[CV 4/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=500;, score=-0.107 total time=   1.5s\n","[CV 5/5] END learning_rate=0.01, max_depth=5, min_samples_split=4, n_estimators=500;, score=-0.112 total time=   1.5s\n","Best parameters:  {'learning_rate': 0.01, 'max_depth': 5, 'min_samples_split': 4, 'n_estimators': 200}\n","Best score:  -0.10954294100812154\n"]}],"source":["grad_boost = GradientBoostingRegressor(random_state=42, max_features='sqrt')\n","\n","# SearchSpace\n","searchSpace = {\n","    'n_estimators': [200, 300, 400, 500],\n","    'learning_rate': [0.01],\n","    'max_depth': [5],\n","    'min_samples_split': [4]\n","}\n","\n","grad_gs = grid_search(grad_boost, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":45,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T14:19:41.721297Z","iopub.status.busy":"2023-05-25T14:19:41.720947Z","iopub.status.idle":"2023-05-25T14:19:43.899830Z","shell.execute_reply":"2023-05-25T14:19:43.898791Z","shell.execute_reply.started":"2023-05-25T14:19:41.721275Z"},"trusted":true},"outputs":[],"source":["score = cross_val_score(grad_boost, X_train, y_train, cv=10)"]},{"cell_type":"code","execution_count":46,"metadata":{"execution":{"iopub.execute_input":"2023-05-25T14:19:43.901791Z","iopub.status.busy":"2023-05-25T14:19:43.901505Z","iopub.status.idle":"2023-05-25T14:19:43.909816Z","shell.execute_reply":"2023-05-25T14:19:43.908682Z","shell.execute_reply.started":"2023-05-25T14:19:43.901769Z"},"trusted":true},"outputs":[{"data":{"text/plain":["array([ 0.00616488, -0.02473145, -0.01165732, -0.04140249,  0.03578469,\n","        0.0325899 ,  0.00890336, -0.02515419, -0.07727098, -0.04414416])"]},"execution_count":46,"metadata":{},"output_type":"execute_result"}],"source":["score"]},{"cell_type":"markdown","metadata":{},"source":["### XGBoost"]},{"cell_type":"code","execution_count":null,"metadata":{"scrolled":true,"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.111 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=3, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=50;, score=-0.111 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=15, min_child_weight=5, n_estimators=200;, score=-0.110 total time=   0.3s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=50;, score=-0.111 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=1, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=50;, score=-0.111 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=3, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=50;, score=-0.111 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=30, min_child_weight=5, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=50;, score=-0.111 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=1, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=50;, score=-0.111 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=3, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=50;, score=-0.111 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.1, max_depth=50, min_child_weight=5, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=200;, score=-0.108 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=200;, score=-0.108 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=1, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200;, score=-0.108 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200;, score=-0.108 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=3, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=50;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=50;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=100;, score=-0.109 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=100;, score=-0.107 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=200;, score=-0.109 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=200;, score=-0.108 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=200;, score=-0.107 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=5, min_child_weight=5, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.108 total time=   0.2s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.108 total time=   0.2s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=1, n_estimators=200;, score=-0.110 total time=   0.2s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.108 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=50;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.113 total time=   0.1s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 3/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 4/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.108 total time=   0.1s\n","[CV 5/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=100;, score=-0.110 total time=   0.1s\n","[CV 1/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=200;, score=-0.113 total time=   0.2s\n","[CV 2/5] END colsample_bytree=1, gamma=0.1, learning_rate=0.2, max_depth=15, min_child_weight=3, n_estimators=200;, score=-0.110 total time=   0.2s\n"]}],"source":["xg_boost = XGBRegressor(random_state=42, tree_method='gpu_hist', gpu_id=0)\n","\n","# SearchSpace\n","searchSpace = {\n","    'n_estimators': [50, 100, 200],\n","    'learning_rate': [0.05, 0.1, 0.2, 0.5],\n","    'max_depth': [5, 15, 30, 50],\n","    'gamma': [0, 0.1, 0.2, 0.3, 0.4, 0.5],\n","    'min_child_weight': [1, 3, 5],\n","    'colsample_bytree': [0.3, 0.5, 0.7, 1]\n","}\n","\n","xg_gs = grid_search(xg_boost, searchSpace, X_train, y_train)"]},{"cell_type":"code","execution_count":49,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T07:43:10.643575Z","iopub.status.busy":"2023-05-26T07:43:10.643210Z","iopub.status.idle":"2023-05-26T07:43:10.649889Z","shell.execute_reply":"2023-05-26T07:43:10.648866Z","shell.execute_reply.started":"2023-05-26T07:43:10.643548Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Best parameters:  {'colsample_bytree': 0.5, 'gamma': 0.1, 'learning_rate': 0.2, 'max_depth': 5, 'min_child_weight': 5, 'n_estimators': 50}\n","Best score:  -0.1093562521141489\n"]}],"source":["print(\"Best parameters: \", xg_gs.best_params_)\n","print(\"Best score: \", xg_gs.best_score_)"]},{"cell_type":"code","execution_count":106,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T11:37:31.854264Z","iopub.status.busy":"2023-05-26T11:37:31.853863Z","iopub.status.idle":"2023-05-26T11:37:31.866578Z","shell.execute_reply":"2023-05-26T11:37:31.865684Z","shell.execute_reply.started":"2023-05-26T11:37:31.854230Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["Individual Learners Performance\n","+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|       Field        |   Decision Tree    |   Random Forest    |     ADA Boost      |   Gradient Boost   |      XGBoost       |\n","+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n","|     criterion      |   squared_error    |    friedman_mse    |         -          |         -          |         -          |\n","|     max_depth      |         5          |         5          |         -          |         5          |         5          |\n","| min_samples_split  |        1000        |         2          |         -          |         4          |         -          |\n","|    n_estimators    |         -          |        400         |        100         |         -          |         50         |\n","|   Learning Rate    |         -          |         -          |        0.1         |        0.01        |        0.2         |\n","|       gamma        |         -          |         -          |         -          |         -          |        0.1         |\n","|  min_child_weight  |         -          |         -          |         -          |         -          |         5          |\n","|  colsample_bytree  |         -          |         -          |         -          |         -          |        0.5         |\n","| ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |\n","|     Neg. RMSE      |      -0.1104       |      -0.1092       |      -0.1093       |      -0.1095       |      -0.1093       |\n","| ------------------ | ------------------ | ------------------ | ------------------ | ------------------ | ------------------ |\n","+--------------------+--------------------+--------------------+--------------------+--------------------+--------------------+\n"]}],"source":["from prettytable import PrettyTable\n","\n","print(\"Individual Learners Performance\")\n","\n","table = PrettyTable()\n","table.field_names = [\"Field\",                   \"Decision Tree\", \"Random Forest\", \"ADA Boost\", \"Gradient Boost\", \"XGBoost\"]\n","table.add_row(      [\"criterion\",               \"squared_error\", \"friedman_mse\",  \"-\",         \"-\",               \"-\"     ])\n","table.add_row(      [\"max_depth\",                5,               5,              \"-\",         5,                 5       ])\n","table.add_row(      [\"min_samples_split\",        1000,            2,              \"-\",         4,                 \"-\"     ])\n","table.add_row(      [\"n_estimators\",             \"-\",             400,             100,        \"-\",               50      ])\n","table.add_row(      [\"Learning Rate\",            \"-\",             \"-\",             0.1,        0.01,              0.2     ])\n","table.add_row(      [\"gamma\",                    \"-\",             \"-\",            \"-\",         \"-\",               0.1     ])\n","table.add_row(      [\"min_child_weight\",         \"-\",             \"-\",            \"-\",         \"-\",               5       ])\n","table.add_row(      [\"colsample_bytree\",         \"-\",             \"-\",            \"-\",         \"-\",               0.5     ])\n","table.add_row(      ['------------------', '------------------', '------------------', '------------------', '------------------','------------------'])\n","table.add_row(      [\"Neg. RMSE\",                -0.1104,         -0.1092,         -0.1093,    -0.1095,           -0.1093 ])\n","table.add_row(      ['------------------', '------------------', '------------------', '------------------', '------------------','------------------'])\n","print(table)"]},{"cell_type":"markdown","metadata":{},"source":["## Ensemble Training"]},{"cell_type":"code","execution_count":60,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T10:20:23.261541Z","iopub.status.busy":"2023-05-26T10:20:23.261122Z","iopub.status.idle":"2023-05-26T10:20:47.378384Z","shell.execute_reply":"2023-05-26T10:20:47.377388Z","shell.execute_reply.started":"2023-05-26T10:20:23.261512Z"},"scrolled":true,"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.5s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.5s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.5s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.5s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.4s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.4s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.6s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.3s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.4s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.5s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.3s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.4s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.5s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.3s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.4s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.5s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.3s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.4s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.7s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.5s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.6s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.5s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.3s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.4s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.5s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.3s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.4s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n","[Voting] ....................... (1 of 5) Processing dt, total=   0.0s\n","[Voting] ....................... (2 of 5) Processing rf, total=   1.5s\n","[Voting] ....................... (3 of 5) Processing ab, total=   0.3s\n","[Voting] ....................... (4 of 5) Processing gb, total=   0.4s\n","[Voting] ...................... (5 of 5) Processing xgb, total=   0.1s\n"]}],"source":["# Define the number of models to train\n","n_models = 10\n","\n","# Define the base learners\n","dt = DecisionTreeRegressor(criterion='squared_error', max_depth=5, min_samples_split=1000, random_state=42)\n","rf = RandomForestRegressor(criterion='friedman_mse', max_depth=5, min_samples_split=2, n_estimators=400, random_state=42)\n","ab = AdaBoostRegressor(learning_rate=0.1, n_estimators=100, random_state=42)\n","gb = GradientBoostingRegressor(learning_rate=0.01, max_depth=5, min_samples_split=4, random_state=42)\n","xgb = XGBRegressor( n_estimators=50, learning_rate=0.2, max_depth=5, colsample_bytree=0.5, gamma=0.1, min_child_weight=5, random_state=42)\n","\n","# Define an empty list to store the models\n","models = []\n","\n","np.random.seed(42)\n","\n","# Train n models with different resampled datasets\n","for i in range(n_models):\n","    x_train_resampled = X_train.sample(frac=0.2).to_numpy() #here we converting to numpy array as we don't want to fit the model with feature names, as we will fit on single sample later (iteratively), which will raise feature importance error.\n","    selected_indices = X_train.sample(frac=0.2).index\n","    y_train_resampled = (y_train[selected_indices]).to_numpy()\n","\n","    # Define the voting regressor\n","    voting_reg = VotingRegressor(\n","        estimators=[('dt', dt), ('rf', rf), ('ab', ab), ('gb', gb), ('xgb', xgb)], \n","        verbose=True\n","    )\n","    \n","    # Fit the voting classifier on the resampled training data\n","    voting_reg.fit(x_train_resampled, y_train_resampled)\n","    \n","    # Append the trained model to the list of models\n","    models.append(voting_reg)"]},{"cell_type":"code","execution_count":103,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T11:32:35.980770Z","iopub.status.busy":"2023-05-26T11:32:35.980040Z","iopub.status.idle":"2023-05-26T11:32:36.920925Z","shell.execute_reply":"2023-05-26T11:32:36.920020Z","shell.execute_reply.started":"2023-05-26T11:32:35.980735Z"},"trusted":true},"outputs":[],"source":["y_preds = []\n","x = X_train.to_numpy()\n","for model in models:\n","    y_pred = model.predict(x)\n","    y_preds.append(y_pred)\n","y_pred_avg = np.mean(y_preds, axis=0)\n","y_pred_final = np.round(y_pred_avg, 2)"]},{"cell_type":"code","execution_count":104,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T11:32:39.923611Z","iopub.status.busy":"2023-05-26T11:32:39.923263Z","iopub.status.idle":"2023-05-26T11:32:39.933385Z","shell.execute_reply":"2023-05-26T11:32:39.932323Z","shell.execute_reply.started":"2023-05-26T11:32:39.923585Z"},"trusted":true},"outputs":[{"name":"stdout","output_type":"stream","text":["MSE: 0.012446967949336685\n","RMSE: 0.11156598025086628\n","MAPE: 4.76 %\n"]}],"source":["mse = mean_squared_error(y_train, y_pred_final)\n","rmse = np.sqrt(mse)\n","mape = np.round(mean_absolute_percentage_error(y_train, y_pred_final) * 100, 2)\n","print(\"MSE:\", mse)\n","print(\"RMSE:\", rmse)\n","print(\"MAPE: {} %\".format(mape))"]},{"cell_type":"markdown","metadata":{},"source":["# Predict on Test set"]},{"cell_type":"code","execution_count":94,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T11:24:52.864633Z","iopub.status.busy":"2023-05-26T11:24:52.863902Z","iopub.status.idle":"2023-05-26T11:27:45.425195Z","shell.execute_reply":"2023-05-26T11:27:45.424190Z","shell.execute_reply.started":"2023-05-26T11:24:52.864598Z"},"scrolled":true,"trusted":true},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"861736b14e2e49289278856f226a8267","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/419 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"}],"source":["test_iter_target = test_merge.copy()\n","for i in tqdm(range(len(test))):\n","    data_val = test_iter_target.iloc[train_part_len+i,1:].to_numpy().reshape(1, -1)\n","    y_preds = []\n","    for model in models:\n","        y_pred = model.predict(data_val)\n","        y_preds.append(y_pred)\n","    y_pred_avg = np.mean(y_preds, axis=0)\n","    y_pred_final = np.round(y_pred_avg, 2)\n","    \n","    # update target iteratively with prediction\n","    test_iter_target.iloc[train_part_len+i,0] = y_pred_final\n","    # update roll_mean iteratively with prediction\n","    test_iter_target = createLagFeatures(test_iter_target)\n","    test_iter_target = createRollMeanFeatures(test_iter_target)\n","    test_iter_target = createEWMFeatures(test_iter_target)"]},{"cell_type":"code","execution_count":95,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T11:27:45.428041Z","iopub.status.busy":"2023-05-26T11:27:45.427062Z","iopub.status.idle":"2023-05-26T11:27:45.434616Z","shell.execute_reply":"2023-05-26T11:27:45.433442Z","shell.execute_reply.started":"2023-05-26T11:27:45.428004Z"},"trusted":true},"outputs":[],"source":["test_final = test_iter_target[test_iter_target.index.year>2021]"]},{"cell_type":"markdown","metadata":{},"source":["### Inverse log transformation"]},{"cell_type":"code","execution_count":97,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T11:27:45.477522Z","iopub.status.busy":"2023-05-26T11:27:45.477184Z","iopub.status.idle":"2023-05-26T11:27:45.482577Z","shell.execute_reply":"2023-05-26T11:27:45.481382Z","shell.execute_reply.started":"2023-05-26T11:27:45.477491Z"},"trusted":true},"outputs":[],"source":["test_y = np.exp(test_final['sleep_hours'])"]},{"cell_type":"code","execution_count":98,"metadata":{"execution":{"iopub.execute_input":"2023-05-26T11:27:56.814975Z","iopub.status.busy":"2023-05-26T11:27:56.814601Z","iopub.status.idle":"2023-05-26T11:27:57.231882Z","shell.execute_reply":"2023-05-26T11:27:57.230993Z","shell.execute_reply.started":"2023-05-26T11:27:56.814945Z"},"trusted":true},"outputs":[{"data":{"image/png":"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","text/plain":["<Figure size 1000x500 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.figure(figsize=(10,5))\n","test_y.plot()\n","plt.title('Forcasted Sleep')\n","plt.ylabel('Sleep time')\n","plt.xlabel('Date')\n","plt.show()"]},{"attachments":{},"cell_type":"markdown","metadata":{},"source":["# Conclusion\n","\n","We have successfully implemented an Emsemble model with hyper-parameter tuning.\n","\n","### Ensemble Model (Tree-Based):\n","Recognizing the potential for enhanced performance through model fusion, I crafted an ensemble model comprising various tree-based algorithms. Leveraging the collective strength of decision trees, random forests, ADA Boost, gradient boost, and XGBoost. By harnessing the individual strengths of each technique, we achieved superior predictive accuracy and generalizability.\n","\n","MSE : 0.01244 <br>\n","RMSE : 0.11156 <br>\n","MAPE : 4.76 % <br>\n","\n","#### <font color='Brown'> In this model we are on an average 7 minutes off of the ground truth, which is pretty good! </font>\n"]},{"cell_type":"markdown","metadata":{},"source":["# END"]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.10"}},"nbformat":4,"nbformat_minor":5}