import numpy as np
import pandas as pd
from scipy.stats import skew
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.preprocessing import minmax_scale
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import minmax_scale
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_recall_fscore_support
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import PolynomialFeatures
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
from sklearn import preprocessing
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import minmax_scale
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import median_absolute_error
from sklearn.metrics import r2_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn import svm
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
from sklearn.preprocessing import MultiLabelBinarizer, StandardScaler
from sklearn.metrics import confusion_matrix, precision_recall_fscore_support
import seaborn as sns
import warnings
import xgboost as xgb
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeRegressor
from xgboost import XGBRegressor
import pickle
import os
import shutil
warnings.filterwarnings("ignore")
from google.colab import drive
drive.mount('/content/drive')
Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount("/content/drive", force_remount=True).
data = pd.read_csv("/content/drive/MyDrive/jfk_weather_cleaned.csv",na_values="?")
data.head()
DATE | HOURLYVISIBILITY | HOURLYDRYBULBTEMPF | HOURLYWETBULBTEMPF | HOURLYDewPointTempF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYStationPressure | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYAltimeterSetting | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyIncr | HOURLYPressureTendencyDecr | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 01-01-2010 01:00 | 6.0 | 33.0 | 32.0 | 31.0 | 92.0 | 0.0 | 29.97 | 29.99 | 0.01 | 29.99 | 0.0 | 1.0 | 0 | 1 | 0 |
1 | 01-01-2010 02:00 | 6.0 | 33.0 | 33.0 | 32.0 | 96.0 | 0.0 | 29.97 | 29.99 | 0.02 | 29.99 | 0.0 | 1.0 | 0 | 1 | 0 |
2 | 01-01-2010 03:00 | 5.0 | 33.0 | 33.0 | 32.0 | 96.0 | 0.0 | 29.97 | 29.99 | 0.00 | 29.99 | 0.0 | 1.0 | 0 | 1 | 0 |
3 | 01-01-2010 04:00 | 5.0 | 33.0 | 33.0 | 32.0 | 96.0 | 0.0 | 29.95 | 29.97 | 0.00 | 29.97 | 0.0 | 1.0 | 0 | 1 | 0 |
4 | 01-01-2010 05:00 | 5.0 | 33.0 | 32.0 | 31.0 | 92.0 | 0.0 | 29.93 | 29.96 | 0.00 | 29.95 | 0.0 | 1.0 | 0 | 1 | 0 |
data.describe()
HOURLYVISIBILITY | HOURLYDRYBULBTEMPF | HOURLYWETBULBTEMPF | HOURLYDewPointTempF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYStationPressure | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYAltimeterSetting | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyIncr | HOURLYPressureTendencyDecr | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
count | 75119.000000 | 75119.000000 | 75119.000000 | 75119.000000 | 75119.000000 | 75119.000000 | 75119.000000 | 75119.000000 | 75119.000000 | 75119.000000 | 75119.000000 | 7.511900e+04 | 75119.000000 | 75119.000000 | 75119.000000 |
mean | 9.205796 | 55.355509 | 49.333830 | 42.422223 | 64.809942 | 11.252965 | 30.005124 | 30.026116 | 0.004574 | 30.025137 | -0.166465 | 2.867949e-02 | 0.503468 | 0.490835 | 0.005698 |
std | 2.209374 | 17.393210 | 16.178678 | 19.577775 | 19.899904 | 6.099392 | 0.234245 | 0.233949 | 0.033581 | 0.234212 | 0.629050 | 7.587989e-01 | 0.499991 | 0.499919 | 0.075268 |
min | 0.000000 | 1.000000 | -1.000000 | -19.000000 | 8.000000 | 0.000000 | 28.490000 | 28.540000 | 0.000000 | 28.510000 | -1.000000 | -1.000000e+00 | 0.000000 | 0.000000 | 0.000000 |
25% | 10.000000 | 42.000000 | 36.000000 | 27.000000 | 49.000000 | 7.000000 | 29.860000 | 29.880000 | 0.000000 | 29.880000 | -0.766044 | -7.660440e-01 | 0.000000 | 0.000000 | 0.000000 |
50% | 10.000000 | 56.000000 | 50.000000 | 44.000000 | 66.000000 | 10.000000 | 30.000000 | 30.020000 | 0.000000 | 30.020000 | -0.173648 | 6.120000e-17 | 1.000000 | 0.000000 | 0.000000 |
75% | 10.000000 | 70.000000 | 63.000000 | 59.000000 | 82.000000 | 15.000000 | 30.150000 | 30.170000 | 0.000000 | 30.170000 | 0.342020 | 7.660440e-01 | 1.000000 | 1.000000 | 0.000000 |
max | 10.000000 | 102.000000 | 85.000000 | 84.000000 | 100.000000 | 53.000000 | 30.830000 | 30.850000 | 2.410000 | 30.850000 | 1.000000 | 1.000000e+00 | 1.000000 | 1.000000 | 1.000000 |
Check to see and impute any null values
data.isnull().sum()
DATE 0 HOURLYVISIBILITY 0 HOURLYDRYBULBTEMPF 0 HOURLYWETBULBTEMPF 0 HOURLYDewPointTempF 0 HOURLYRelativeHumidity 0 HOURLYWindSpeed 0 HOURLYStationPressure 0 HOURLYSeaLevelPressure 0 HOURLYPrecip 0 HOURLYAltimeterSetting 0 HOURLYWindDirectionSin 0 HOURLYWindDirectionCos 0 HOURLYPressureTendencyIncr 0 HOURLYPressureTendencyDecr 0 HOURLYPressureTendencyCons 0 dtype: int64
EDA Analysis
import pandas_profiling
pandas_profiling.ProfileReport(data, minimal=True)
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Before Analyzing furthur lets scale our data and divide it into Target and Data accordingly
scale = StandardScaler()
X = data.drop(['DATE','HOURLYVISIBILITY'],axis=1)
y = data['HOURLYVISIBILITY']
X_scaled = scale.fit_transform(X)
X_scaled = pd.DataFrame(X_scaled,columns=X.columns)
X_scaled
HOURLYDRYBULBTEMPF | HOURLYWETBULBTEMPF | HOURLYDewPointTempF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYStationPressure | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYAltimeterSetting | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyIncr | HOURLYPressureTendencyDecr | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | -1.285309 | -1.071407 | -0.583432 | 1.366350 | -1.844945 | -0.149949 | -0.154377 | 0.161577 | -0.150024 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
1 | -1.285309 | -1.009597 | -0.532353 | 1.567358 | -1.844945 | -0.149949 | -0.154377 | 0.459363 | -0.150024 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
2 | -1.285309 | -1.009597 | -0.532353 | 1.567358 | -1.844945 | -0.149949 | -0.154377 | -0.136210 | -0.150024 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
3 | -1.285309 | -1.009597 | -0.532353 | 1.567358 | -1.844945 | -0.235330 | -0.239866 | -0.136210 | -0.235417 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
4 | -1.285309 | -1.071407 | -0.583432 | 1.366350 | -1.844945 | -0.320712 | -0.282611 | -0.136210 | -0.320810 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
75114 | 1.186936 | 1.462810 | 1.510793 | 1.165343 | -1.353089 | -0.021877 | -0.026143 | -0.136210 | -0.021933 | -0.953157 | -0.884914 | 0.993088 | -0.981834 | -0.075699 |
75115 | 0.784477 | 1.215569 | 1.357557 | 1.768365 | 0.286430 | -0.064567 | -0.068888 | 34.407033 | -0.064630 | 1.286476 | 0.971759 | 0.993088 | -0.981834 | -0.075699 |
75116 | 0.899466 | 1.277379 | 1.408636 | 1.567358 | -1.844945 | 0.063505 | 0.059346 | 0.161577 | 0.063460 | 0.264630 | 1.280085 | 0.993088 | -0.981834 | -0.075699 |
75117 | 0.956960 | 1.339189 | 1.408636 | 1.466854 | -1.025185 | -0.021877 | -0.026143 | -0.136210 | -0.021933 | 1.482417 | 0.809322 | 0.993088 | -0.981834 | -0.075699 |
75118 | 0.956960 | 1.339189 | 1.459715 | 1.617609 | -1.844945 | 0.020814 | 0.016601 | -0.136210 | 0.020763 | 0.264630 | 1.280085 | 0.993088 | -0.981834 | -0.075699 |
75119 rows × 14 columns
sns.set()
%matplotlib inline
plt.figure(figsize=(20,25), facecolor='white')
plotnumber = 1
for column in data.drop(['DATE'],axis=1):
ax = plt.subplot(5,3,plotnumber)
sns.distplot(data[column])
s = round(skew(data[column]), 2)
plt.xlabel("{}, skewness: {}".format(column, s),fontsize=16)
plotnumber+=1
plt.tight_layout()
plt.show()
Since some of the columns are not following normal distribution curve, We will try to use either square root or log transformations. But since most of the columns have very low skewness, its better to remove columns like precipitation. But before that lets analyze the correlation matrix
plt.figure(figsize=(8,8))
plt_data = data.drop(['DATE'],axis=1)
sns.heatmap(plt_data.corr().abs(), vmin = -0.5,vmax = 1,annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7f4878460610>
From the correlation heatmap, we can see that our target variable has a decent correlation with Hourly Precipitation, so lets leave it like that. But it can also be seen that there is a clear correlation between some other columns, so lets remove them
X_ = X_scaled.drop(['HOURLYWETBULBTEMPF','HOURLYDewPointTempF','HOURLYStationPressure'],axis=1)
X_
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYAltimeterSetting | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyIncr | HOURLYPressureTendencyDecr | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|---|---|---|
0 | -1.285309 | 1.366350 | -1.844945 | -0.154377 | 0.161577 | -0.150024 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
1 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | 0.459363 | -0.150024 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
2 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | -0.136210 | -0.150024 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
3 | -1.285309 | 1.567358 | -1.844945 | -0.239866 | -0.136210 | -0.235417 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
4 | -1.285309 | 1.366350 | -1.844945 | -0.282611 | -0.136210 | -0.320810 | 0.264630 | 1.280085 | -1.006960 | 1.018502 | -0.075699 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
75114 | 1.186936 | 1.165343 | -1.353089 | -0.026143 | -0.136210 | -0.021933 | -0.953157 | -0.884914 | 0.993088 | -0.981834 | -0.075699 |
75115 | 0.784477 | 1.768365 | 0.286430 | -0.068888 | 34.407033 | -0.064630 | 1.286476 | 0.971759 | 0.993088 | -0.981834 | -0.075699 |
75116 | 0.899466 | 1.567358 | -1.844945 | 0.059346 | 0.161577 | 0.063460 | 0.264630 | 1.280085 | 0.993088 | -0.981834 | -0.075699 |
75117 | 0.956960 | 1.466854 | -1.025185 | -0.026143 | -0.136210 | -0.021933 | 1.482417 | 0.809322 | 0.993088 | -0.981834 | -0.075699 |
75118 | 0.956960 | 1.617609 | -1.844945 | 0.016601 | -0.136210 | 0.020763 | 0.264630 | 1.280085 | 0.993088 | -0.981834 | -0.075699 |
75119 rows × 11 columns
plt.figure(figsize=(8,8))
plt_data = X_
sns.heatmap(plt_data.corr().abs(), vmin = -0.5,vmax = 1,annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7f4873c4aa90>
Still some columns have a high correlation, So lets also remove columns in a way that the correlations minimizes
X_ = X_.drop(['HOURLYAltimeterSetting','HOURLYPressureTendencyIncr','HOURLYPressureTendencyDecr'],axis=1)
X_
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|
0 | -1.285309 | 1.366350 | -1.844945 | -0.154377 | 0.161577 | 0.264630 | 1.280085 | -0.075699 |
1 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | 0.459363 | 0.264630 | 1.280085 | -0.075699 |
2 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
3 | -1.285309 | 1.567358 | -1.844945 | -0.239866 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
4 | -1.285309 | 1.366350 | -1.844945 | -0.282611 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
75114 | 1.186936 | 1.165343 | -1.353089 | -0.026143 | -0.136210 | -0.953157 | -0.884914 | -0.075699 |
75115 | 0.784477 | 1.768365 | 0.286430 | -0.068888 | 34.407033 | 1.286476 | 0.971759 | -0.075699 |
75116 | 0.899466 | 1.567358 | -1.844945 | 0.059346 | 0.161577 | 0.264630 | 1.280085 | -0.075699 |
75117 | 0.956960 | 1.466854 | -1.025185 | -0.026143 | -0.136210 | 1.482417 | 0.809322 | -0.075699 |
75118 | 0.956960 | 1.617609 | -1.844945 | 0.016601 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
75119 rows × 8 columns
plt.figure(figsize=(8,8))
plt_data = X_
sns.heatmap(plt_data.corr().abs(), vmin = -0.5,vmax = 1,annot=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7f4873ef0210>
Now our correlation graph is perfect, lets now move to creating various regression models
x_train, x_test, y_train, y_test = train_test_split(X_, y, train_size=0.8, shuffle=False)
X1 = X_[:]
X1["HOURLYVISIBILITY"] = y
X1
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | HOURLYVISIBILITY | |
---|---|---|---|---|---|---|---|---|---|
0 | -1.285309 | 1.366350 | -1.844945 | -0.154377 | 0.161577 | 0.264630 | 1.280085 | -0.075699 | 6.0 |
1 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | 0.459363 | 0.264630 | 1.280085 | -0.075699 | 6.0 |
2 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | -0.136210 | 0.264630 | 1.280085 | -0.075699 | 5.0 |
3 | -1.285309 | 1.567358 | -1.844945 | -0.239866 | -0.136210 | 0.264630 | 1.280085 | -0.075699 | 5.0 |
4 | -1.285309 | 1.366350 | -1.844945 | -0.282611 | -0.136210 | 0.264630 | 1.280085 | -0.075699 | 5.0 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
75114 | 1.186936 | 1.165343 | -1.353089 | -0.026143 | -0.136210 | -0.953157 | -0.884914 | -0.075699 | 10.0 |
75115 | 0.784477 | 1.768365 | 0.286430 | -0.068888 | 34.407033 | 1.286476 | 0.971759 | -0.075699 | 4.0 |
75116 | 0.899466 | 1.567358 | -1.844945 | 0.059346 | 0.161577 | 0.264630 | 1.280085 | -0.075699 | 10.0 |
75117 | 0.956960 | 1.466854 | -1.025185 | -0.026143 | -0.136210 | 1.482417 | 0.809322 | -0.075699 | 10.0 |
75118 | 0.956960 | 1.617609 | -1.844945 | 0.016601 | -0.136210 | 0.264630 | 1.280085 | -0.075699 | 10.0 |
75119 rows × 9 columns
Linear Regression
regr = LinearRegression()
regr.fit(x_train, y_train)
y_pred = regr.predict(x_test)
mse = mean_squared_error(y_test,y_pred)
sns.set_style('whitegrid')
sns.lmplot(x ='HOURLYVISIBILITY', y ='HOURLYRelativeHumidity', data = X1)
r2 = r2_score(y_test,y_pred)
print(f"\nR2 Score: {r2}")
# sns.pairplot(X1, x_vars=["HOURLYDRYBULBTEMPF", "HOURLYRelativeHumidity", "HOURLYWindSpeed", "HOURLYSeaLevelPressure", "HOURLYPrecip", "HOURLYWindDirectionSin", "HOURLYWindDirectionCos", "HOURLYPressureTendencyCons"], y_vars = "HOURLYVISIBILITY", size = 7, aspect = 0.7, kind = "reg" )
R2 Score: 0.3088916297729465
Polynomial Regression
poly = PolynomialFeatures()
X_poly = poly.fit_transform(x_train)
poly.fit(x_train,y_train)
model = LinearRegression()
model.fit(X_poly, y_train)
y_pred = model.predict(poly.fit_transform(x_test))
# plt.figure(figsize=(10, 6))
# plt.title("Polynomial Regression", size=16)
# plt.scatter(x_test["HOURLYRelativeHumidity"], y_test)
# plt.plot(x_test, y_pred, c="red")
# plt.show()
r2 = r2_score(y_test,y_pred)
print(f"\nR2 Score: {r2}")
R2 Score: 0.41007788739671447
Decision Tree Regressor
Using Grid Search to find best parameters for Decision Tree Regressor
def get_best_params_for_DecisionTreeRegressor(train_x, train_y):
try:
DecisionTreeReg = DecisionTreeRegressor()
param_grid_decisionTree = {"criterion": ["mse", "friedman_mse", "mae"],
"splitter": ["best", "random"],
"max_features": ["auto", "sqrt", "log2"],
'max_depth': range(2, 16, 2),
'min_samples_split': range(2, 16, 2)
}
grid = GridSearchCV(DecisionTreeReg, param_grid_decisionTree, verbose=3,cv=2)
grid.fit(train_x, train_y)
criterion = grid.best_params_['criterion']
splitter = grid.best_params_['splitter']
max_features = grid.best_params_['max_features']
max_depth = grid.best_params_['max_depth']
min_samples_split = grid.best_params_['min_samples_split']
return criterion, splitter, max_features, max_depth, min_samples_split
except Exception as e:
print(e)
criterion, splitter, max_features, max_depth, min_samples_split = get_best_params_for_DecisionTreeRegressor(x_train, y_train)
Fitting 2 folds for each of 882 candidates, totalling 1764 fits [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=2, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=2, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=2, splitter=random;, score=0.102 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=2, splitter=random;, score=0.048 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=4, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=4, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=4, splitter=random;, score=0.346 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=4, splitter=random;, score=0.093 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=6, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=6, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=6, splitter=random;, score=0.318 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=6, splitter=random;, score=0.283 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=8, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=8, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=8, splitter=random;, score=0.198 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=8, splitter=random;, score=0.046 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=10, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=10, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=10, splitter=random;, score=0.153 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=10, splitter=random;, score=0.162 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=12, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=12, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=12, splitter=random;, score=0.251 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=12, splitter=random;, score=0.044 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=14, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=14, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=14, splitter=random;, score=0.062 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=auto, min_samples_split=14, splitter=random;, score=0.341 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.264 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.029 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.030 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.013 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.323 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.450 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.016 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.003 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.053 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.367 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.394 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.010 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.517 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.351 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.004 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.016 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.027 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.355 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.028 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.245 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.535 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.228 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.015 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.001 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.030 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.367 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.452 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.009 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=2, splitter=best;, score=0.093 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=2, splitter=best;, score=0.431 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=2, splitter=random;, score=0.005 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=2, splitter=random;, score=0.032 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=4, splitter=best;, score=0.223 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=4, splitter=best;, score=0.453 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=4, splitter=random;, score=0.025 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=4, splitter=random;, score=0.014 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=6, splitter=best;, score=0.480 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=6, splitter=best;, score=0.396 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=6, splitter=random;, score=0.126 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=6, splitter=random;, score=0.206 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=8, splitter=best;, score=0.488 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=8, splitter=best;, score=0.258 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=8, splitter=random;, score=0.035 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=8, splitter=random;, score=-0.000 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=10, splitter=best;, score=0.483 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=10, splitter=best;, score=0.095 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=10, splitter=random;, score=0.216 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=10, splitter=random;, score=0.124 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=12, splitter=best;, score=0.323 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=12, splitter=best;, score=0.255 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=12, splitter=random;, score=0.471 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=12, splitter=random;, score=0.004 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=14, splitter=best;, score=0.216 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=14, splitter=best;, score=0.040 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=14, splitter=random;, score=0.204 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=2, max_features=log2, min_samples_split=14, splitter=random;, score=0.035 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=2, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=2, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=2, splitter=random;, score=0.438 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=2, splitter=random;, score=0.391 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=4, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=4, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=4, splitter=random;, score=0.439 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=4, splitter=random;, score=0.278 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=6, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=6, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=6, splitter=random;, score=0.523 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=6, splitter=random;, score=0.440 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=8, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=8, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=8, splitter=random;, score=0.518 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=8, splitter=random;, score=0.293 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=10, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=10, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=10, splitter=random;, score=0.532 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=10, splitter=random;, score=0.292 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=12, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=12, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=12, splitter=random;, score=0.373 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=12, splitter=random;, score=0.378 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=14, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=14, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=14, splitter=random;, score=0.503 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=auto, min_samples_split=14, splitter=random;, score=0.476 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.305 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.279 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.171 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.165 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.508 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.485 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.527 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.374 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.504 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.462 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.080 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.040 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.370 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.492 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.090 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.234 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.256 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.466 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.015 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.162 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.347 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.501 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.304 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.049 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.547 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.279 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.028 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.153 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=2, splitter=best;, score=0.496 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=2, splitter=best;, score=0.513 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=2, splitter=random;, score=0.477 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=2, splitter=random;, score=0.025 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=4, splitter=best;, score=0.538 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=4, splitter=best;, score=0.498 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=4, splitter=random;, score=0.162 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=4, splitter=random;, score=0.158 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=6, splitter=best;, score=0.548 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=6, splitter=best;, score=0.569 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=6, splitter=random;, score=0.158 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=6, splitter=random;, score=0.420 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=8, splitter=best;, score=0.498 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=8, splitter=best;, score=0.472 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=8, splitter=random;, score=0.099 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=8, splitter=random;, score=0.229 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=10, splitter=best;, score=0.479 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=10, splitter=best;, score=0.406 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=10, splitter=random;, score=0.107 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=10, splitter=random;, score=0.146 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=12, splitter=best;, score=0.531 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=12, splitter=best;, score=0.524 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=12, splitter=random;, score=0.022 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=12, splitter=random;, score=0.340 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=14, splitter=best;, score=0.332 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=14, splitter=best;, score=0.509 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=14, splitter=random;, score=0.440 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=4, max_features=log2, min_samples_split=14, splitter=random;, score=0.127 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=2, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=2, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=2, splitter=random;, score=0.460 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=2, splitter=random;, score=0.538 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=4, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=4, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=4, splitter=random;, score=0.564 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=4, splitter=random;, score=0.493 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=6, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=6, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=6, splitter=random;, score=0.562 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=6, splitter=random;, score=0.447 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=8, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=8, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=8, splitter=random;, score=0.568 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=8, splitter=random;, score=0.484 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=10, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=10, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=10, splitter=random;, score=0.520 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=10, splitter=random;, score=0.492 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=12, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=12, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=12, splitter=random;, score=0.461 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=12, splitter=random;, score=0.419 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=14, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=14, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=14, splitter=random;, score=0.537 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=auto, min_samples_split=14, splitter=random;, score=0.442 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.507 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.502 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.207 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.442 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.390 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.565 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.107 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.365 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.373 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.254 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.298 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.113 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.417 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.356 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.133 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.337 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.537 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.549 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.408 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.281 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.568 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.476 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.209 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.105 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.524 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.479 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.144 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.408 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=2, splitter=best;, score=0.550 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=2, splitter=best;, score=0.477 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=2, splitter=random;, score=0.248 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=2, splitter=random;, score=0.110 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=4, splitter=best;, score=0.559 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=4, splitter=best;, score=0.551 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=4, splitter=random;, score=0.156 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=4, splitter=random;, score=0.184 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=6, splitter=best;, score=0.569 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=6, splitter=best;, score=0.516 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=6, splitter=random;, score=0.254 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=6, splitter=random;, score=0.275 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=8, splitter=best;, score=0.588 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=8, splitter=best;, score=0.558 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=8, splitter=random;, score=0.262 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=8, splitter=random;, score=0.308 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=10, splitter=best;, score=0.538 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=10, splitter=best;, score=0.587 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=10, splitter=random;, score=0.407 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=10, splitter=random;, score=0.207 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=12, splitter=best;, score=0.577 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=12, splitter=best;, score=0.526 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=12, splitter=random;, score=0.287 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=12, splitter=random;, score=0.411 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=14, splitter=best;, score=0.436 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=14, splitter=best;, score=0.531 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=14, splitter=random;, score=0.231 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=6, max_features=log2, min_samples_split=14, splitter=random;, score=0.225 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=2, splitter=best;, score=0.554 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=2, splitter=best;, score=0.568 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=2, splitter=random;, score=0.586 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=2, splitter=random;, score=0.465 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=4, splitter=best;, score=0.556 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=4, splitter=best;, score=0.571 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=4, splitter=random;, score=0.571 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=4, splitter=random;, score=0.541 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=6, splitter=best;, score=0.556 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=6, splitter=best;, score=0.569 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=6, splitter=random;, score=0.543 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=6, splitter=random;, score=0.516 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=8, splitter=best;, score=0.557 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=8, splitter=best;, score=0.574 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=8, splitter=random;, score=0.548 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=8, splitter=random;, score=0.492 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=10, splitter=best;, score=0.556 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=10, splitter=best;, score=0.576 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=10, splitter=random;, score=0.575 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=10, splitter=random;, score=0.502 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=12, splitter=best;, score=0.562 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=12, splitter=best;, score=0.576 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=12, splitter=random;, score=0.584 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=12, splitter=random;, score=0.576 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=14, splitter=best;, score=0.564 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=14, splitter=best;, score=0.577 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=14, splitter=random;, score=0.522 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=auto, min_samples_split=14, splitter=random;, score=0.541 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.464 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.553 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.094 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.530 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.549 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.546 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.148 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.148 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.523 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.501 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.234 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.293 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.446 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.513 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.478 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.173 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.564 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.513 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.324 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.396 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.400 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.507 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.192 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.261 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.504 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.479 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.218 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.307 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=2, splitter=best;, score=0.546 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=2, splitter=best;, score=0.555 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=2, splitter=random;, score=0.347 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=2, splitter=random;, score=0.131 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=4, splitter=best;, score=0.568 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=4, splitter=best;, score=0.527 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=4, splitter=random;, score=0.298 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=4, splitter=random;, score=0.483 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=6, splitter=best;, score=0.564 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=6, splitter=best;, score=0.574 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=6, splitter=random;, score=0.347 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=6, splitter=random;, score=0.285 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=8, splitter=best;, score=0.530 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=8, splitter=best;, score=0.570 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=8, splitter=random;, score=0.341 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=8, splitter=random;, score=0.295 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=10, splitter=best;, score=0.475 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=10, splitter=best;, score=0.553 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=10, splitter=random;, score=0.502 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=10, splitter=random;, score=0.305 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=12, splitter=best;, score=0.552 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=12, splitter=best;, score=0.581 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=12, splitter=random;, score=0.414 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=12, splitter=random;, score=0.430 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=14, splitter=best;, score=0.554 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=14, splitter=best;, score=0.539 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=14, splitter=random;, score=0.316 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=8, max_features=log2, min_samples_split=14, splitter=random;, score=0.236 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=2, splitter=best;, score=0.469 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=2, splitter=best;, score=0.510 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=2, splitter=random;, score=0.542 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=2, splitter=random;, score=0.512 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=4, splitter=best;, score=0.471 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=4, splitter=best;, score=0.510 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=4, splitter=random;, score=0.516 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=4, splitter=random;, score=0.562 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=6, splitter=best;, score=0.479 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=6, splitter=best;, score=0.513 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=6, splitter=random;, score=0.566 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=6, splitter=random;, score=0.514 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=8, splitter=best;, score=0.484 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=8, splitter=best;, score=0.522 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=8, splitter=random;, score=0.562 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=8, splitter=random;, score=0.539 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=10, splitter=best;, score=0.489 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=10, splitter=best;, score=0.527 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=10, splitter=random;, score=0.571 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=10, splitter=random;, score=0.545 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=12, splitter=best;, score=0.510 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=12, splitter=best;, score=0.534 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=12, splitter=random;, score=0.584 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=12, splitter=random;, score=0.543 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=14, splitter=best;, score=0.512 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=14, splitter=best;, score=0.535 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=14, splitter=random;, score=0.574 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=auto, min_samples_split=14, splitter=random;, score=0.509 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.519 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.456 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.464 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.240 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.521 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.476 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.400 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.292 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.524 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.476 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.342 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.422 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.528 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.509 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.356 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.261 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.507 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.479 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.318 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.123 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.534 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.543 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.484 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.267 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.504 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.424 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.287 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.320 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=2, splitter=best;, score=0.522 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=2, splitter=best;, score=0.516 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=2, splitter=random;, score=0.350 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=2, splitter=random;, score=0.462 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=4, splitter=best;, score=0.506 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=4, splitter=best;, score=0.457 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=4, splitter=random;, score=0.505 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=4, splitter=random;, score=0.520 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=6, splitter=best;, score=0.489 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=6, splitter=best;, score=0.526 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=6, splitter=random;, score=0.492 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=6, splitter=random;, score=0.509 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=8, splitter=best;, score=0.538 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=8, splitter=best;, score=0.533 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=8, splitter=random;, score=0.516 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=8, splitter=random;, score=0.395 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=10, splitter=best;, score=0.513 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=10, splitter=best;, score=0.520 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=10, splitter=random;, score=0.424 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=10, splitter=random;, score=0.461 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=12, splitter=best;, score=0.524 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=12, splitter=best;, score=0.536 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=12, splitter=random;, score=0.300 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=12, splitter=random;, score=0.477 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=14, splitter=best;, score=0.541 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=14, splitter=best;, score=0.515 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=14, splitter=random;, score=0.328 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=10, max_features=log2, min_samples_split=14, splitter=random;, score=0.323 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=2, splitter=best;, score=0.404 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=2, splitter=best;, score=0.426 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=2, splitter=random;, score=0.530 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=2, splitter=random;, score=0.530 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=4, splitter=best;, score=0.414 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=4, splitter=best;, score=0.441 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=4, splitter=random;, score=0.485 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=4, splitter=random;, score=0.541 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=6, splitter=best;, score=0.426 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=6, splitter=best;, score=0.449 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=6, splitter=random;, score=0.559 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=6, splitter=random;, score=0.503 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=8, splitter=best;, score=0.432 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=8, splitter=best;, score=0.462 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=8, splitter=random;, score=0.515 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=8, splitter=random;, score=0.554 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=10, splitter=best;, score=0.446 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=10, splitter=best;, score=0.475 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=10, splitter=random;, score=0.528 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=10, splitter=random;, score=0.548 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=12, splitter=best;, score=0.472 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=12, splitter=best;, score=0.484 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=12, splitter=random;, score=0.551 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=12, splitter=random;, score=0.557 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=14, splitter=best;, score=0.479 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=14, splitter=best;, score=0.486 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=14, splitter=random;, score=0.581 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=auto, min_samples_split=14, splitter=random;, score=0.520 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.410 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.458 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.437 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.332 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.388 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.466 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.269 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.277 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.458 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.399 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.403 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.344 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.433 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.517 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.319 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.304 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.466 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.462 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.547 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.480 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.489 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.445 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.520 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.269 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.507 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.516 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.371 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.312 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=2, splitter=best;, score=0.428 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=2, splitter=best;, score=0.442 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=2, splitter=random;, score=0.513 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=2, splitter=random;, score=0.466 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=4, splitter=best;, score=0.422 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=4, splitter=best;, score=0.467 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=4, splitter=random;, score=0.469 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=4, splitter=random;, score=0.463 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=6, splitter=best;, score=0.448 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=6, splitter=best;, score=0.477 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=6, splitter=random;, score=0.405 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=6, splitter=random;, score=0.456 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=8, splitter=best;, score=0.425 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=8, splitter=best;, score=0.492 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=8, splitter=random;, score=0.543 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=8, splitter=random;, score=0.479 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=10, splitter=best;, score=0.492 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=10, splitter=best;, score=0.493 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=10, splitter=random;, score=0.482 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=10, splitter=random;, score=0.527 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=12, splitter=best;, score=0.457 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=12, splitter=best;, score=0.510 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=12, splitter=random;, score=0.461 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=12, splitter=random;, score=0.464 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=14, splitter=best;, score=0.487 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=14, splitter=best;, score=0.507 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=14, splitter=random;, score=0.338 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=12, max_features=log2, min_samples_split=14, splitter=random;, score=0.541 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=2, splitter=best;, score=0.344 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=2, splitter=best;, score=0.315 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=2, splitter=random;, score=0.495 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=2, splitter=random;, score=0.438 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=4, splitter=best;, score=0.356 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=4, splitter=best;, score=0.347 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=4, splitter=random;, score=0.497 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=4, splitter=random;, score=0.482 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=6, splitter=best;, score=0.380 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=6, splitter=best;, score=0.370 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=6, splitter=random;, score=0.495 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=6, splitter=random;, score=0.491 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=8, splitter=best;, score=0.401 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=8, splitter=best;, score=0.402 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=8, splitter=random;, score=0.474 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=8, splitter=random;, score=0.472 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=10, splitter=best;, score=0.418 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=10, splitter=best;, score=0.415 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=10, splitter=random;, score=0.559 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=10, splitter=random;, score=0.511 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=12, splitter=best;, score=0.443 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=12, splitter=best;, score=0.427 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=12, splitter=random;, score=0.544 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=12, splitter=random;, score=0.516 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=14, splitter=best;, score=0.455 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=14, splitter=best;, score=0.433 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=14, splitter=random;, score=0.526 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=auto, min_samples_split=14, splitter=random;, score=0.538 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.333 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.389 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.340 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.371 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.383 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.396 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.339 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.488 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.438 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.401 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.483 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.383 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.463 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.449 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.384 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.326 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.423 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.458 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.516 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.530 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.412 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.455 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.378 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.457 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.476 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.468 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.443 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.410 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=2, splitter=best;, score=0.367 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=2, splitter=best;, score=0.369 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=2, splitter=random;, score=0.310 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=2, splitter=random;, score=0.385 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=4, splitter=best;, score=0.385 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=4, splitter=best;, score=0.375 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=4, splitter=random;, score=0.412 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=4, splitter=random;, score=0.437 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=6, splitter=best;, score=0.393 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=6, splitter=best;, score=0.408 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=6, splitter=random;, score=0.471 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=6, splitter=random;, score=0.369 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=8, splitter=best;, score=0.424 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=8, splitter=best;, score=0.423 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=8, splitter=random;, score=0.503 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=8, splitter=random;, score=0.373 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=10, splitter=best;, score=0.420 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=10, splitter=best;, score=0.428 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=10, splitter=random;, score=0.381 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=10, splitter=random;, score=0.423 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=12, splitter=best;, score=0.432 total time= 0.1s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=12, splitter=best;, score=0.470 total time= 0.1s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=12, splitter=random;, score=0.468 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=12, splitter=random;, score=0.493 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=14, splitter=best;, score=0.462 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=14, splitter=best;, score=0.489 total time= 0.0s [CV 1/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=14, splitter=random;, score=0.536 total time= 0.0s [CV 2/2] END criterion=mse, max_depth=14, max_features=log2, min_samples_split=14, splitter=random;, score=0.494 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=2, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=2, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=2, splitter=random;, score=0.313 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=2, splitter=random;, score=0.173 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=4, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=4, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=4, splitter=random;, score=0.416 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=4, splitter=random;, score=0.054 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=6, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=6, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=6, splitter=random;, score=0.193 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=6, splitter=random;, score=0.315 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=8, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=8, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=8, splitter=random;, score=0.119 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=8, splitter=random;, score=0.147 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=10, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=10, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=10, splitter=random;, score=0.224 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=10, splitter=random;, score=0.039 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=12, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=12, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=12, splitter=random;, score=0.194 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=12, splitter=random;, score=0.280 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=14, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=14, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=14, splitter=random;, score=0.164 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=auto, min_samples_split=14, splitter=random;, score=0.122 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.039 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.431 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.008 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.143 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.221 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.238 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.022 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.090 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.471 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.378 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.001 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.005 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.479 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.395 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.030 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.011 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.129 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.020 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.017 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.120 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.204 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.234 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.001 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.006 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.264 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.230 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.164 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.009 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=2, splitter=best;, score=0.448 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=2, splitter=best;, score=0.258 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=2, splitter=random;, score=0.003 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=2, splitter=random;, score=0.006 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=4, splitter=best;, score=0.264 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=4, splitter=best;, score=0.261 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=4, splitter=random;, score=0.394 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=4, splitter=random;, score=-0.004 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=6, splitter=best;, score=0.452 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=6, splitter=best;, score=0.255 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=6, splitter=random;, score=0.045 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=6, splitter=random;, score=0.105 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=8, splitter=best;, score=0.038 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=8, splitter=best;, score=0.251 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=8, splitter=random;, score=0.019 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=8, splitter=random;, score=0.007 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=10, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=10, splitter=best;, score=0.473 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=10, splitter=random;, score=0.225 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=10, splitter=random;, score=0.025 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=12, splitter=best;, score=0.483 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=12, splitter=best;, score=0.022 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=12, splitter=random;, score=0.049 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=12, splitter=random;, score=0.016 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=14, splitter=best;, score=0.471 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=14, splitter=best;, score=0.396 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=14, splitter=random;, score=0.116 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=2, max_features=log2, min_samples_split=14, splitter=random;, score=0.034 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=2, splitter=best;, score=0.595 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=2, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=2, splitter=random;, score=0.538 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=2, splitter=random;, score=0.283 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=4, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=4, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=4, splitter=random;, score=0.465 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=4, splitter=random;, score=0.372 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=6, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=6, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=6, splitter=random;, score=0.150 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=6, splitter=random;, score=0.390 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=8, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=8, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=8, splitter=random;, score=0.496 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=8, splitter=random;, score=0.401 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=10, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=10, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=10, splitter=random;, score=0.536 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=10, splitter=random;, score=0.116 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=12, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=12, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=12, splitter=random;, score=0.358 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=12, splitter=random;, score=0.242 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=14, splitter=best;, score=0.595 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=14, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=14, splitter=random;, score=0.405 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=auto, min_samples_split=14, splitter=random;, score=0.390 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.507 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.495 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.076 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.071 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.255 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.450 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.456 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.220 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.521 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.158 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.022 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.027 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.395 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.462 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.065 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.024 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.502 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.394 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.164 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.307 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.560 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.473 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.063 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.026 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.244 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.205 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.084 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.210 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=2, splitter=best;, score=0.578 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=2, splitter=best;, score=0.397 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=2, splitter=random;, score=0.066 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=2, splitter=random;, score=0.144 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=4, splitter=best;, score=0.569 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=4, splitter=best;, score=0.501 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=4, splitter=random;, score=0.181 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=4, splitter=random;, score=0.268 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=6, splitter=best;, score=0.520 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=6, splitter=best;, score=0.320 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=6, splitter=random;, score=0.262 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=6, splitter=random;, score=0.305 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=8, splitter=best;, score=0.480 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=8, splitter=best;, score=0.538 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=8, splitter=random;, score=0.164 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=8, splitter=random;, score=0.195 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=10, splitter=best;, score=0.549 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=10, splitter=best;, score=0.504 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=10, splitter=random;, score=0.191 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=10, splitter=random;, score=0.248 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=12, splitter=best;, score=0.500 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=12, splitter=best;, score=0.283 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=12, splitter=random;, score=0.158 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=12, splitter=random;, score=0.102 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=14, splitter=best;, score=0.423 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=14, splitter=best;, score=0.216 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=14, splitter=random;, score=0.384 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=4, max_features=log2, min_samples_split=14, splitter=random;, score=0.081 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=2, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=2, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=2, splitter=random;, score=0.371 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=2, splitter=random;, score=0.520 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=4, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=4, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=4, splitter=random;, score=0.452 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=4, splitter=random;, score=0.417 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=6, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=6, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=6, splitter=random;, score=0.569 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=6, splitter=random;, score=0.498 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=8, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=8, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=8, splitter=random;, score=0.528 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=8, splitter=random;, score=0.377 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=10, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=10, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=10, splitter=random;, score=0.576 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=10, splitter=random;, score=0.537 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=12, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=12, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=12, splitter=random;, score=0.447 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=12, splitter=random;, score=0.545 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=14, splitter=best;, score=0.590 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=14, splitter=best;, score=0.591 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=14, splitter=random;, score=0.541 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=auto, min_samples_split=14, splitter=random;, score=0.563 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.550 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.320 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.112 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.260 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.575 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.492 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.140 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.138 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.361 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.554 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.138 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.110 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.492 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.485 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.049 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.130 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.574 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.402 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.202 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.113 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.563 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.516 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.521 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.089 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.567 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.485 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.096 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.361 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=2, splitter=best;, score=0.572 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=2, splitter=best;, score=0.566 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=2, splitter=random;, score=0.548 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=2, splitter=random;, score=0.185 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=4, splitter=best;, score=0.571 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=4, splitter=best;, score=0.549 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=4, splitter=random;, score=0.309 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=4, splitter=random;, score=0.141 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=6, splitter=best;, score=0.572 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=6, splitter=best;, score=0.371 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=6, splitter=random;, score=0.331 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=6, splitter=random;, score=0.149 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=8, splitter=best;, score=0.585 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=8, splitter=best;, score=0.486 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=8, splitter=random;, score=0.158 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=8, splitter=random;, score=0.396 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=10, splitter=best;, score=0.570 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=10, splitter=best;, score=0.555 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=10, splitter=random;, score=0.369 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=10, splitter=random;, score=0.294 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=12, splitter=best;, score=0.442 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=12, splitter=best;, score=0.578 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=12, splitter=random;, score=0.501 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=12, splitter=random;, score=0.067 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=14, splitter=best;, score=0.449 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=14, splitter=best;, score=0.544 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=14, splitter=random;, score=0.170 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=6, max_features=log2, min_samples_split=14, splitter=random;, score=0.500 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=2, splitter=best;, score=0.556 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=2, splitter=best;, score=0.570 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=2, splitter=random;, score=0.558 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=2, splitter=random;, score=0.534 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=4, splitter=best;, score=0.556 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=4, splitter=best;, score=0.570 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=4, splitter=random;, score=0.580 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=4, splitter=random;, score=0.456 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=6, splitter=best;, score=0.554 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=6, splitter=best;, score=0.572 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=6, splitter=random;, score=0.546 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=6, splitter=random;, score=0.419 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=8, splitter=best;, score=0.558 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=8, splitter=best;, score=0.574 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=8, splitter=random;, score=0.568 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=8, splitter=random;, score=0.565 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=10, splitter=best;, score=0.557 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=10, splitter=best;, score=0.576 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=10, splitter=random;, score=0.452 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=10, splitter=random;, score=0.565 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=12, splitter=best;, score=0.562 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=12, splitter=best;, score=0.576 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=12, splitter=random;, score=0.573 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=12, splitter=random;, score=0.494 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=14, splitter=best;, score=0.564 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=14, splitter=best;, score=0.577 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=14, splitter=random;, score=0.475 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=auto, min_samples_split=14, splitter=random;, score=0.577 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.565 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.558 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.232 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.343 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.424 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.531 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.180 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.159 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.554 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.534 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.190 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.458 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.511 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.451 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.454 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.299 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.512 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.524 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.172 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.241 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.389 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.469 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.281 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.245 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.539 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.491 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.250 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.163 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=2, splitter=best;, score=0.565 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=2, splitter=best;, score=0.559 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=2, splitter=random;, score=0.276 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=2, splitter=random;, score=0.434 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=4, splitter=best;, score=0.567 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=4, splitter=best;, score=0.548 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=4, splitter=random;, score=0.432 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=4, splitter=random;, score=0.386 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=6, splitter=best;, score=0.497 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=6, splitter=best;, score=0.531 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=6, splitter=random;, score=0.496 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=6, splitter=random;, score=0.380 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=8, splitter=best;, score=0.506 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=8, splitter=best;, score=0.514 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=8, splitter=random;, score=0.381 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=8, splitter=random;, score=0.280 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=10, splitter=best;, score=0.527 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=10, splitter=best;, score=0.581 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=10, splitter=random;, score=0.424 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=10, splitter=random;, score=0.413 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=12, splitter=best;, score=0.562 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=12, splitter=best;, score=0.570 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=12, splitter=random;, score=0.293 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=12, splitter=random;, score=0.273 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=14, splitter=best;, score=0.544 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=14, splitter=best;, score=0.486 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=14, splitter=random;, score=0.193 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=8, max_features=log2, min_samples_split=14, splitter=random;, score=0.308 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=2, splitter=best;, score=0.473 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=2, splitter=best;, score=0.511 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=2, splitter=random;, score=0.531 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=2, splitter=random;, score=0.576 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=4, splitter=best;, score=0.468 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=4, splitter=best;, score=0.511 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=4, splitter=random;, score=0.573 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=4, splitter=random;, score=0.575 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=6, splitter=best;, score=0.479 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=6, splitter=best;, score=0.516 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=6, splitter=random;, score=0.560 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=6, splitter=random;, score=0.544 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=8, splitter=best;, score=0.486 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=8, splitter=best;, score=0.525 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=8, splitter=random;, score=0.552 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=8, splitter=random;, score=0.553 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=10, splitter=best;, score=0.489 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=10, splitter=best;, score=0.531 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=10, splitter=random;, score=0.563 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=10, splitter=random;, score=0.550 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=12, splitter=best;, score=0.509 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=12, splitter=best;, score=0.534 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=12, splitter=random;, score=0.564 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=12, splitter=random;, score=0.554 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=14, splitter=best;, score=0.512 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=14, splitter=best;, score=0.535 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=14, splitter=random;, score=0.564 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=auto, min_samples_split=14, splitter=random;, score=0.571 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.492 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.527 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.296 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.270 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.498 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.500 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.320 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.119 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.486 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.484 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.235 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.234 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.542 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.536 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.487 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.348 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.496 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.501 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.382 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.506 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.505 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.550 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.347 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.311 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.528 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.506 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.241 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.469 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=2, splitter=best;, score=0.492 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=2, splitter=best;, score=0.515 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=2, splitter=random;, score=0.399 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=2, splitter=random;, score=0.325 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=4, splitter=best;, score=0.501 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=4, splitter=best;, score=0.502 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=4, splitter=random;, score=0.438 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=4, splitter=random;, score=0.552 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=6, splitter=best;, score=0.497 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=6, splitter=best;, score=0.506 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=6, splitter=random;, score=0.331 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=6, splitter=random;, score=0.283 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=8, splitter=best;, score=0.464 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=8, splitter=best;, score=0.518 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=8, splitter=random;, score=0.303 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=8, splitter=random;, score=0.403 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=10, splitter=best;, score=0.503 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=10, splitter=best;, score=0.519 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=10, splitter=random;, score=0.542 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=10, splitter=random;, score=0.362 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=12, splitter=best;, score=0.520 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=12, splitter=best;, score=0.556 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=12, splitter=random;, score=0.316 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=12, splitter=random;, score=0.431 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=14, splitter=best;, score=0.537 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=14, splitter=best;, score=0.530 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=14, splitter=random;, score=0.355 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=10, max_features=log2, min_samples_split=14, splitter=random;, score=0.322 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=2, splitter=best;, score=0.397 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=2, splitter=best;, score=0.428 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=2, splitter=random;, score=0.502 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=2, splitter=random;, score=0.459 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=4, splitter=best;, score=0.413 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=4, splitter=best;, score=0.441 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=4, splitter=random;, score=0.522 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=4, splitter=random;, score=0.471 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=6, splitter=best;, score=0.425 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=6, splitter=best;, score=0.452 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=6, splitter=random;, score=0.529 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=6, splitter=random;, score=0.502 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=8, splitter=best;, score=0.430 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=8, splitter=best;, score=0.466 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=8, splitter=random;, score=0.571 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=8, splitter=random;, score=0.558 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=10, splitter=best;, score=0.442 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=10, splitter=best;, score=0.475 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=10, splitter=random;, score=0.524 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=10, splitter=random;, score=0.535 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=12, splitter=best;, score=0.468 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=12, splitter=best;, score=0.483 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=12, splitter=random;, score=0.569 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=12, splitter=random;, score=0.531 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=14, splitter=best;, score=0.479 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=14, splitter=best;, score=0.485 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=14, splitter=random;, score=0.557 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=auto, min_samples_split=14, splitter=random;, score=0.554 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.443 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.443 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.227 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.469 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.443 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.501 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.345 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.379 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.466 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.490 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.472 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.298 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.485 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.471 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.342 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.453 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.468 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.466 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.248 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.301 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.503 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.513 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.313 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.283 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.513 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.488 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.370 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.328 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=2, splitter=best;, score=0.419 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=2, splitter=best;, score=0.405 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=2, splitter=random;, score=0.427 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=2, splitter=random;, score=0.523 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=4, splitter=best;, score=0.451 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=4, splitter=best;, score=0.456 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=4, splitter=random;, score=0.457 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=4, splitter=random;, score=0.374 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=6, splitter=best;, score=0.456 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=6, splitter=best;, score=0.473 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=6, splitter=random;, score=0.351 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=6, splitter=random;, score=0.438 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=8, splitter=best;, score=0.459 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=8, splitter=best;, score=0.476 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=8, splitter=random;, score=0.485 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=8, splitter=random;, score=0.471 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=10, splitter=best;, score=0.466 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=10, splitter=best;, score=0.494 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=10, splitter=random;, score=0.364 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=10, splitter=random;, score=0.502 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=12, splitter=best;, score=0.481 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=12, splitter=best;, score=0.477 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=12, splitter=random;, score=0.289 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=12, splitter=random;, score=0.515 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=14, splitter=best;, score=0.509 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=14, splitter=best;, score=0.480 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=14, splitter=random;, score=0.546 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=12, max_features=log2, min_samples_split=14, splitter=random;, score=0.468 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=2, splitter=best;, score=0.327 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=2, splitter=best;, score=0.326 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=2, splitter=random;, score=0.443 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=2, splitter=random;, score=0.459 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=4, splitter=best;, score=0.361 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=4, splitter=best;, score=0.343 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=4, splitter=random;, score=0.509 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=4, splitter=random;, score=0.472 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=6, splitter=best;, score=0.383 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=6, splitter=best;, score=0.375 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=6, splitter=random;, score=0.525 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=6, splitter=random;, score=0.498 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=8, splitter=best;, score=0.401 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=8, splitter=best;, score=0.399 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=8, splitter=random;, score=0.478 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=8, splitter=random;, score=0.505 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=10, splitter=best;, score=0.415 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=10, splitter=best;, score=0.415 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=10, splitter=random;, score=0.520 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=10, splitter=random;, score=0.524 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=12, splitter=best;, score=0.445 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=12, splitter=best;, score=0.430 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=12, splitter=random;, score=0.531 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=12, splitter=random;, score=0.534 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=14, splitter=best;, score=0.454 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=14, splitter=best;, score=0.435 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=14, splitter=random;, score=0.545 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=auto, min_samples_split=14, splitter=random;, score=0.557 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.372 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.304 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.391 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.489 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.382 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.456 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.325 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.467 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.434 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.396 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.322 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.297 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.423 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.449 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.312 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.341 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.369 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.437 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.405 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.352 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.484 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.483 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.279 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.253 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.469 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.454 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.232 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.493 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=2, splitter=best;, score=0.372 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=2, splitter=best;, score=0.367 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=2, splitter=random;, score=0.423 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=2, splitter=random;, score=0.399 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=4, splitter=best;, score=0.361 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=4, splitter=best;, score=0.411 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=4, splitter=random;, score=0.473 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=4, splitter=random;, score=0.411 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=6, splitter=best;, score=0.371 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=6, splitter=best;, score=0.400 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=6, splitter=random;, score=0.479 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=6, splitter=random;, score=0.405 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=8, splitter=best;, score=0.431 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=8, splitter=best;, score=0.408 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=8, splitter=random;, score=0.360 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=8, splitter=random;, score=0.374 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=10, splitter=best;, score=0.417 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=10, splitter=best;, score=0.437 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=10, splitter=random;, score=0.498 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=10, splitter=random;, score=0.546 total time= 0.0s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=12, splitter=best;, score=0.473 total time= 0.1s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=12, splitter=best;, score=0.484 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=12, splitter=random;, score=0.467 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=12, splitter=random;, score=0.535 total time= 0.1s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=14, splitter=best;, score=0.439 total time= 0.2s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=14, splitter=best;, score=0.446 total time= 0.2s [CV 1/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=14, splitter=random;, score=0.424 total time= 0.0s [CV 2/2] END criterion=friedman_mse, max_depth=14, max_features=log2, min_samples_split=14, splitter=random;, score=0.357 total time= 0.1s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=2, splitter=best;, score=0.437 total time= 7.3s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=2, splitter=best;, score=0.469 total time= 8.5s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=2, splitter=random;, score=-0.138 total time= 4.7s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=2, splitter=random;, score=-0.054 total time= 5.4s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=4, splitter=best;, score=0.437 total time= 7.8s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=4, splitter=best;, score=0.469 total time= 7.7s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=4, splitter=random;, score=-0.142 total time= 4.9s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=4, splitter=random;, score=0.123 total time= 5.4s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=6, splitter=best;, score=0.437 total time= 7.2s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=6, splitter=best;, score=0.469 total time= 7.6s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=6, splitter=random;, score=-0.097 total time= 4.2s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=6, splitter=random;, score=-0.103 total time= 4.3s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=8, splitter=best;, score=0.437 total time= 7.5s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=8, splitter=best;, score=0.469 total time= 8.6s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=8, splitter=random;, score=-0.102 total time= 4.2s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=8, splitter=random;, score=0.360 total time= 3.6s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=10, splitter=best;, score=0.437 total time= 7.2s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=10, splitter=best;, score=0.469 total time= 7.6s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=10, splitter=random;, score=0.277 total time= 5.1s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=10, splitter=random;, score=-0.079 total time= 6.0s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=12, splitter=best;, score=0.437 total time= 7.2s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=12, splitter=best;, score=0.469 total time= 7.7s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=12, splitter=random;, score=0.408 total time= 4.7s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=12, splitter=random;, score=0.409 total time= 3.4s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=14, splitter=best;, score=0.437 total time= 7.2s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=14, splitter=best;, score=0.469 total time= 7.6s [CV 1/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=14, splitter=random;, score=-0.129 total time= 3.9s [CV 2/2] END criterion=mae, max_depth=2, max_features=auto, min_samples_split=14, splitter=random;, score=0.028 total time= 4.9s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=best;, score=-0.129 total time= 2.0s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.416 total time= 2.7s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=random;, score=-0.147 total time= 1.1s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=2, splitter=random;, score=-0.107 total time= 1.7s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=best;, score=-0.148 total time= 1.9s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=best;, score=-0.107 total time= 2.9s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.146 total time= 1.6s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.105 total time= 1.3s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.138 total time= 2.1s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.038 total time= 2.1s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.076 total time= 1.4s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.107 total time= 1.6s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.344 total time= 2.3s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=best;, score=-0.078 total time= 1.9s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=random;, score=-0.148 total time= 1.0s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=8, splitter=random;, score=-0.107 total time= 1.6s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.339 total time= 2.2s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.200 total time= 2.4s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.147 total time= 1.3s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.106 total time= 1.7s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.363 total time= 2.3s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.137 total time= 2.3s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.363 total time= 1.6s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.106 total time= 1.4s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.160 total time= 2.1s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.305 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.148 total time= 0.6s [CV 2/2] END criterion=mae, max_depth=2, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.051 total time= 1.9s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=2, splitter=best;, score=0.363 total time= 3.5s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=2, splitter=best;, score=-0.100 total time= 3.0s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=2, splitter=random;, score=-0.148 total time= 0.7s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=2, splitter=random;, score=-0.094 total time= 2.2s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=4, splitter=best;, score=0.437 total time= 3.1s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=4, splitter=best;, score=0.471 total time= 3.3s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=4, splitter=random;, score=-0.148 total time= 2.0s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=4, splitter=random;, score=-0.105 total time= 1.4s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=6, splitter=best;, score=0.452 total time= 3.1s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=6, splitter=best;, score=0.413 total time= 3.7s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=6, splitter=random;, score=-0.148 total time= 2.2s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=6, splitter=random;, score=-0.106 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=8, splitter=best;, score=0.164 total time= 2.7s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=8, splitter=best;, score=0.276 total time= 3.4s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=8, splitter=random;, score=-0.132 total time= 1.6s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=8, splitter=random;, score=-0.106 total time= 1.9s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=10, splitter=best;, score=0.437 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=10, splitter=best;, score=0.471 total time= 3.5s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=10, splitter=random;, score=-0.148 total time= 1.5s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=10, splitter=random;, score=-0.107 total time= 1.1s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=12, splitter=best;, score=0.354 total time= 3.1s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=12, splitter=best;, score=0.450 total time= 3.6s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=12, splitter=random;, score=-0.146 total time= 2.4s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=12, splitter=random;, score=-0.107 total time= 2.1s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=14, splitter=best;, score=0.339 total time= 3.1s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=14, splitter=best;, score=0.413 total time= 3.4s [CV 1/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=14, splitter=random;, score=-0.102 total time= 1.9s [CV 2/2] END criterion=mae, max_depth=2, max_features=log2, min_samples_split=14, splitter=random;, score=-0.107 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=2, splitter=best;, score=0.471 total time= 12.5s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=2, splitter=best;, score=0.527 total time= 12.1s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=2, splitter=random;, score=0.086 total time= 9.0s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=2, splitter=random;, score=-0.063 total time= 8.8s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=4, splitter=best;, score=0.471 total time= 12.7s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=4, splitter=best;, score=0.529 total time= 12.1s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=4, splitter=random;, score=0.355 total time= 9.9s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=4, splitter=random;, score=0.290 total time= 7.2s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=6, splitter=best;, score=0.471 total time= 12.5s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=6, splitter=best;, score=0.523 total time= 12.2s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=6, splitter=random;, score=0.030 total time= 8.7s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=6, splitter=random;, score=-0.102 total time= 9.0s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=8, splitter=best;, score=0.471 total time= 12.5s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=8, splitter=best;, score=0.527 total time= 12.1s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=8, splitter=random;, score=-0.008 total time= 8.4s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=8, splitter=random;, score=0.333 total time= 7.2s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=10, splitter=best;, score=0.471 total time= 12.6s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=10, splitter=best;, score=0.523 total time= 12.0s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=10, splitter=random;, score=0.391 total time= 7.5s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=10, splitter=random;, score=0.336 total time= 7.4s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=12, splitter=best;, score=0.471 total time= 12.6s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=12, splitter=best;, score=0.523 total time= 12.2s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=12, splitter=random;, score=0.411 total time= 7.4s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=12, splitter=random;, score=0.345 total time= 9.2s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=14, splitter=best;, score=0.471 total time= 12.6s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=14, splitter=best;, score=0.523 total time= 12.0s [CV 1/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=14, splitter=random;, score=-0.064 total time= 9.5s [CV 2/2] END criterion=mae, max_depth=4, max_features=auto, min_samples_split=14, splitter=random;, score=0.386 total time= 7.3s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.184 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.415 total time= 3.4s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=random;, score=-0.130 total time= 2.1s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=2, splitter=random;, score=-0.107 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.201 total time= 3.4s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.430 total time= 4.5s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.132 total time= 1.8s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.123 total time= 1.8s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.286 total time= 4.0s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.023 total time= 2.8s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.113 total time= 2.3s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.104 total time= 2.2s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.156 total time= 3.4s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.371 total time= 3.6s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=random;, score=-0.146 total time= 0.8s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.057 total time= 2.7s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.418 total time= 4.1s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.541 total time= 3.9s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.089 total time= 3.0s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.105 total time= 2.1s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.457 total time= 3.6s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.121 total time= 3.6s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.148 total time= 1.2s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.107 total time= 0.7s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.427 total time= 3.5s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.441 total time= 4.0s [CV 1/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.125 total time= 0.8s [CV 2/2] END criterion=mae, max_depth=4, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.107 total time= 1.8s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=2, splitter=best;, score=0.451 total time= 4.9s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=2, splitter=best;, score=0.528 total time= 4.9s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=2, splitter=random;, score=0.261 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=2, splitter=random;, score=-0.048 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=4, splitter=best;, score=0.441 total time= 4.7s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=4, splitter=best;, score=0.503 total time= 5.4s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=4, splitter=random;, score=-0.147 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=4, splitter=random;, score=-0.106 total time= 3.7s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=6, splitter=best;, score=0.480 total time= 4.5s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=6, splitter=best;, score=0.456 total time= 4.7s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=6, splitter=random;, score=-0.148 total time= 1.1s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=6, splitter=random;, score=0.039 total time= 3.6s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=8, splitter=best;, score=0.449 total time= 5.1s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=8, splitter=best;, score=0.554 total time= 5.5s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=8, splitter=random;, score=-0.148 total time= 1.2s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=8, splitter=random;, score=0.140 total time= 4.2s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=10, splitter=best;, score=0.464 total time= 3.9s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=10, splitter=best;, score=0.491 total time= 6.5s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=10, splitter=random;, score=-0.148 total time= 1.4s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=10, splitter=random;, score=0.069 total time= 2.6s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=12, splitter=best;, score=0.355 total time= 5.7s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=12, splitter=best;, score=0.410 total time= 5.9s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=12, splitter=random;, score=0.227 total time= 3.2s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=12, splitter=random;, score=-0.016 total time= 3.5s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=14, splitter=best;, score=0.444 total time= 5.5s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=14, splitter=best;, score=0.434 total time= 5.6s [CV 1/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=14, splitter=random;, score=-0.068 total time= 3.9s [CV 2/2] END criterion=mae, max_depth=4, max_features=log2, min_samples_split=14, splitter=random;, score=-0.094 total time= 3.2s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=2, splitter=best;, score=0.474 total time= 15.6s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=2, splitter=best;, score=0.528 total time= 15.6s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=2, splitter=random;, score=0.053 total time= 13.6s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=2, splitter=random;, score=0.156 total time= 13.4s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=4, splitter=best;, score=0.474 total time= 16.4s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=4, splitter=best;, score=0.533 total time= 15.4s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=4, splitter=random;, score=0.169 total time= 9.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=4, splitter=random;, score=0.327 total time= 13.7s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=6, splitter=best;, score=0.481 total time= 15.7s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=6, splitter=best;, score=0.528 total time= 15.5s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=6, splitter=random;, score=0.452 total time= 13.2s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=6, splitter=random;, score=0.453 total time= 12.0s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=8, splitter=best;, score=0.479 total time= 15.5s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=8, splitter=best;, score=0.526 total time= 15.2s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=8, splitter=random;, score=0.391 total time= 12.0s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=8, splitter=random;, score=0.215 total time= 12.6s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=10, splitter=best;, score=0.492 total time= 16.7s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=10, splitter=best;, score=0.527 total time= 15.3s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=10, splitter=random;, score=0.318 total time= 13.8s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=10, splitter=random;, score=0.230 total time= 12.8s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=12, splitter=best;, score=0.489 total time= 14.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=12, splitter=best;, score=0.528 total time= 15.7s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=12, splitter=random;, score=0.005 total time= 14.6s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=12, splitter=random;, score=0.413 total time= 12.1s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=14, splitter=best;, score=0.477 total time= 15.1s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=14, splitter=best;, score=0.526 total time= 15.9s [CV 1/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=14, splitter=random;, score=0.475 total time= 9.6s [CV 2/2] END criterion=mae, max_depth=6, max_features=auto, min_samples_split=14, splitter=random;, score=0.460 total time= 11.9s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.423 total time= 4.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.021 total time= 2.8s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=random;, score=-0.093 total time= 3.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.004 total time= 2.7s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=best;, score=-0.134 total time= 2.1s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.460 total time= 5.0s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.056 total time= 1.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.265 total time= 3.5s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.445 total time= 4.1s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.296 total time= 5.8s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.108 total time= 2.6s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.107 total time= 1.9s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.491 total time= 4.7s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.385 total time= 4.7s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=random;, score=-0.140 total time= 1.6s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=8, splitter=random;, score=-0.076 total time= 3.3s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.484 total time= 3.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.510 total time= 5.8s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.069 total time= 2.2s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.106 total time= 3.1s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.461 total time= 5.0s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.143 total time= 3.9s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.088 total time= 2.7s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.103 total time= 1.7s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.475 total time= 5.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.272 total time= 3.0s [CV 1/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.092 total time= 2.6s [CV 2/2] END criterion=mae, max_depth=6, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.006 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=2, splitter=best;, score=0.482 total time= 8.3s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=2, splitter=best;, score=0.257 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=2, splitter=random;, score=-0.142 total time= 2.8s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=2, splitter=random;, score=0.109 total time= 3.9s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=4, splitter=best;, score=0.457 total time= 6.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=4, splitter=best;, score=0.402 total time= 8.5s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=4, splitter=random;, score=0.026 total time= 5.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=4, splitter=random;, score=-0.107 total time= 3.5s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=6, splitter=best;, score=0.490 total time= 7.0s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=6, splitter=best;, score=0.515 total time= 6.9s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=6, splitter=random;, score=0.303 total time= 2.8s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=6, splitter=random;, score=0.343 total time= 3.6s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=8, splitter=best;, score=0.464 total time= 4.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=8, splitter=best;, score=0.516 total time= 7.1s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=8, splitter=random;, score=-0.124 total time= 5.0s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=8, splitter=random;, score=0.012 total time= 6.2s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=10, splitter=best;, score=0.450 total time= 8.0s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=10, splitter=best;, score=0.473 total time= 7.6s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=10, splitter=random;, score=0.403 total time= 5.0s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=10, splitter=random;, score=0.461 total time= 3.0s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=12, splitter=best;, score=0.465 total time= 6.1s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=12, splitter=best;, score=0.536 total time= 8.0s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=12, splitter=random;, score=-0.148 total time= 0.9s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=12, splitter=random;, score=-0.099 total time= 4.8s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=14, splitter=best;, score=0.451 total time= 6.5s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=14, splitter=best;, score=0.477 total time= 7.9s [CV 1/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=14, splitter=random;, score=-0.129 total time= 4.1s [CV 2/2] END criterion=mae, max_depth=6, max_features=log2, min_samples_split=14, splitter=random;, score=-0.056 total time= 5.8s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=2, splitter=best;, score=0.469 total time= 19.7s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=2, splitter=best;, score=0.501 total time= 18.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=2, splitter=random;, score=0.142 total time= 16.6s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=2, splitter=random;, score=0.526 total time= 11.3s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=4, splitter=best;, score=0.475 total time= 21.0s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=4, splitter=best;, score=0.501 total time= 17.3s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=4, splitter=random;, score=0.451 total time= 7.0s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=4, splitter=random;, score=0.239 total time= 10.3s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=6, splitter=best;, score=0.471 total time= 19.8s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=6, splitter=best;, score=0.514 total time= 17.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=6, splitter=random;, score=0.203 total time= 11.3s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=6, splitter=random;, score=0.550 total time= 15.5s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=8, splitter=best;, score=0.467 total time= 17.0s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=8, splitter=best;, score=0.513 total time= 19.0s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=8, splitter=random;, score=0.491 total time= 11.9s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=8, splitter=random;, score=0.328 total time= 14.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=10, splitter=best;, score=0.478 total time= 19.1s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=10, splitter=best;, score=0.512 total time= 19.0s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=10, splitter=random;, score=0.473 total time= 11.3s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=10, splitter=random;, score=0.480 total time= 17.4s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=12, splitter=best;, score=0.471 total time= 17.4s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=12, splitter=best;, score=0.518 total time= 17.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=12, splitter=random;, score=0.175 total time= 7.7s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=12, splitter=random;, score=0.261 total time= 15.2s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=14, splitter=best;, score=0.480 total time= 17.1s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=14, splitter=best;, score=0.513 total time= 19.5s [CV 1/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=14, splitter=random;, score=0.164 total time= 16.0s [CV 2/2] END criterion=mae, max_depth=8, max_features=auto, min_samples_split=14, splitter=random;, score=0.484 total time= 16.9s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.442 total time= 4.3s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.471 total time= 5.0s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=random;, score=-0.127 total time= 2.6s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.354 total time= 4.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=best;, score=-0.103 total time= 2.2s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.476 total time= 7.3s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.148 total time= 2.0s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.107 total time= 0.7s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.454 total time= 6.0s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.434 total time= 5.8s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.045 total time= 2.1s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.079 total time= 4.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.402 total time= 6.5s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.024 total time= 2.9s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=random;, score=-0.035 total time= 4.3s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.170 total time= 4.6s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.453 total time= 5.8s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.505 total time= 7.2s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.145 total time= 3.8s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.136 total time= 3.3s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.483 total time= 6.6s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.241 total time= 7.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.116 total time= 3.1s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.051 total time= 2.8s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.206 total time= 4.5s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.190 total time= 4.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.124 total time= 3.4s [CV 2/2] END criterion=mae, max_depth=8, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.107 total time= 2.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=2, splitter=best;, score=0.456 total time= 9.0s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=2, splitter=best;, score=0.452 total time= 7.9s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=2, splitter=random;, score=0.293 total time= 4.5s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=2, splitter=random;, score=-0.078 total time= 3.4s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=4, splitter=best;, score=0.456 total time= 5.8s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=4, splitter=best;, score=0.479 total time= 10.0s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=4, splitter=random;, score=-0.077 total time= 3.0s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=4, splitter=random;, score=0.324 total time= 7.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=6, splitter=best;, score=0.445 total time= 5.8s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=6, splitter=best;, score=0.514 total time= 6.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=6, splitter=random;, score=-0.146 total time= 1.7s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=6, splitter=random;, score=-0.020 total time= 5.6s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=8, splitter=best;, score=0.458 total time= 7.7s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=8, splitter=best;, score=0.471 total time= 9.6s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=8, splitter=random;, score=0.407 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=8, splitter=random;, score=0.155 total time= 7.6s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=10, splitter=best;, score=0.465 total time= 9.9s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=10, splitter=best;, score=0.457 total time= 8.4s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=10, splitter=random;, score=0.250 total time= 4.7s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=10, splitter=random;, score=-0.107 total time= 2.3s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=12, splitter=best;, score=0.470 total time= 8.4s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=12, splitter=best;, score=0.520 total time= 7.9s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=12, splitter=random;, score=-0.128 total time= 2.5s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=12, splitter=random;, score=0.089 total time= 5.4s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=14, splitter=best;, score=0.447 total time= 6.9s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=14, splitter=best;, score=0.548 total time= 7.1s [CV 1/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=14, splitter=random;, score=0.318 total time= 5.3s [CV 2/2] END criterion=mae, max_depth=8, max_features=log2, min_samples_split=14, splitter=random;, score=-0.060 total time= 2.3s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=2, splitter=best;, score=0.413 total time= 22.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=2, splitter=best;, score=0.460 total time= 22.3s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=2, splitter=random;, score=0.180 total time= 18.0s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=2, splitter=random;, score=0.457 total time= 21.8s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=4, splitter=best;, score=0.443 total time= 20.6s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=4, splitter=best;, score=0.470 total time= 19.1s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=4, splitter=random;, score=0.471 total time= 17.7s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=4, splitter=random;, score=0.337 total time= 18.2s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=6, splitter=best;, score=0.433 total time= 17.7s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=6, splitter=best;, score=0.471 total time= 21.5s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=6, splitter=random;, score=0.465 total time= 19.6s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=6, splitter=random;, score=0.495 total time= 13.9s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=8, splitter=best;, score=0.446 total time= 22.6s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=8, splitter=best;, score=0.482 total time= 20.6s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=8, splitter=random;, score=0.353 total time= 19.7s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=8, splitter=random;, score=0.247 total time= 20.0s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=10, splitter=best;, score=0.432 total time= 20.4s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=10, splitter=best;, score=0.467 total time= 20.8s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=10, splitter=random;, score=0.150 total time= 13.0s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=10, splitter=random;, score=0.460 total time= 17.4s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=12, splitter=best;, score=0.444 total time= 25.0s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=12, splitter=best;, score=0.477 total time= 21.5s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=12, splitter=random;, score=0.172 total time= 10.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=12, splitter=random;, score=0.422 total time= 20.4s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=14, splitter=best;, score=0.441 total time= 18.6s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=14, splitter=best;, score=0.480 total time= 19.9s [CV 1/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=14, splitter=random;, score=0.161 total time= 8.9s [CV 2/2] END criterion=mae, max_depth=10, max_features=auto, min_samples_split=14, splitter=random;, score=0.521 total time= 10.7s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.411 total time= 8.2s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.436 total time= 4.7s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=random;, score=-0.146 total time= 1.9s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.388 total time= 3.0s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.244 total time= 6.6s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.453 total time= 6.7s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.080 total time= 1.7s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.032 total time= 3.4s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.422 total time= 5.7s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.039 total time= 5.4s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.148 total time= 2.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.069 total time= 3.2s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.189 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.508 total time= 6.2s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.264 total time= 2.7s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.151 total time= 4.7s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.480 total time= 5.9s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.478 total time= 6.2s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.046 total time= 2.3s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.186 total time= 3.5s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.428 total time= 6.6s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.141 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.138 total time= 5.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.103 total time= 2.0s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.460 total time= 5.0s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.252 total time= 5.0s [CV 1/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.132 total time= 2.6s [CV 2/2] END criterion=mae, max_depth=10, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.168 total time= 3.3s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=2, splitter=best;, score=0.462 total time= 7.9s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=2, splitter=best;, score=0.484 total time= 9.4s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=2, splitter=random;, score=0.349 total time= 5.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=2, splitter=random;, score=0.245 total time= 4.2s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=4, splitter=best;, score=0.434 total time= 8.3s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=4, splitter=best;, score=0.501 total time= 10.6s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=4, splitter=random;, score=-0.082 total time= 2.9s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=4, splitter=random;, score=-0.107 total time= 1.0s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=6, splitter=best;, score=0.452 total time= 9.9s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=6, splitter=best;, score=0.515 total time= 8.6s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=6, splitter=random;, score=0.310 total time= 4.4s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=6, splitter=random;, score=0.409 total time= 9.0s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=8, splitter=best;, score=0.418 total time= 9.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=8, splitter=best;, score=0.503 total time= 9.8s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=8, splitter=random;, score=0.406 total time= 4.7s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=8, splitter=random;, score=0.222 total time= 6.7s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=10, splitter=best;, score=0.433 total time= 6.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=10, splitter=best;, score=0.481 total time= 7.2s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=10, splitter=random;, score=-0.148 total time= 1.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=10, splitter=random;, score=0.088 total time= 3.8s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=12, splitter=best;, score=0.453 total time= 6.6s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=12, splitter=best;, score=0.501 total time= 8.0s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=12, splitter=random;, score=-0.017 total time= 2.5s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=12, splitter=random;, score=0.042 total time= 3.3s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=14, splitter=best;, score=0.460 total time= 8.1s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=14, splitter=best;, score=0.498 total time= 7.3s [CV 1/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=14, splitter=random;, score=-0.148 total time= 0.9s [CV 2/2] END criterion=mae, max_depth=10, max_features=log2, min_samples_split=14, splitter=random;, score=-0.099 total time= 2.0s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=2, splitter=best;, score=0.382 total time= 21.1s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=2, splitter=best;, score=0.405 total time= 22.8s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=2, splitter=random;, score=0.467 total time= 14.5s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=2, splitter=random;, score=0.492 total time= 12.5s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=4, splitter=best;, score=0.402 total time= 23.5s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=4, splitter=best;, score=0.408 total time= 19.7s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=4, splitter=random;, score=0.158 total time= 13.0s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=4, splitter=random;, score=0.484 total time= 18.7s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=6, splitter=best;, score=0.429 total time= 22.5s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=6, splitter=best;, score=0.429 total time= 21.7s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=6, splitter=random;, score=0.443 total time= 6.4s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=6, splitter=random;, score=0.494 total time= 13.0s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=8, splitter=best;, score=0.427 total time= 28.9s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=8, splitter=best;, score=0.435 total time= 22.2s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=8, splitter=random;, score=0.467 total time= 23.7s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=8, splitter=random;, score=0.501 total time= 20.9s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=10, splitter=best;, score=0.414 total time= 22.5s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=10, splitter=best;, score=0.436 total time= 22.1s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=10, splitter=random;, score=0.341 total time= 17.5s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=10, splitter=random;, score=0.312 total time= 26.1s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=12, splitter=best;, score=0.433 total time= 18.9s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=12, splitter=best;, score=0.446 total time= 22.2s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=12, splitter=random;, score=0.162 total time= 17.5s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=12, splitter=random;, score=0.500 total time= 24.0s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=14, splitter=best;, score=0.419 total time= 18.0s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=14, splitter=best;, score=0.446 total time= 19.3s [CV 1/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=14, splitter=random;, score=0.473 total time= 20.4s [CV 2/2] END criterion=mae, max_depth=12, max_features=auto, min_samples_split=14, splitter=random;, score=0.426 total time= 22.9s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.136 total time= 1.9s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.480 total time= 6.2s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.060 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.367 total time= 4.2s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.407 total time= 6.0s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.220 total time= 2.7s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.428 total time= 2.3s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=4, splitter=random;, score=0.168 total time= 2.5s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.399 total time= 8.1s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.091 total time= 4.6s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.105 total time= 3.7s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=6, splitter=random;, score=-0.107 total time= 0.4s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.367 total time= 5.3s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.436 total time= 6.5s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.011 total time= 2.5s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=8, splitter=random;, score=-0.082 total time= 2.1s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.450 total time= 4.8s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=best;, score=-0.016 total time= 2.7s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.068 total time= 4.1s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.258 total time= 5.7s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.416 total time= 5.1s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.464 total time= 5.6s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.031 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.329 total time= 4.5s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.461 total time= 8.2s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.437 total time= 6.3s [CV 1/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.148 total time= 1.2s [CV 2/2] END criterion=mae, max_depth=12, max_features=sqrt, min_samples_split=14, splitter=random;, score=-0.106 total time= 2.6s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=2, splitter=best;, score=0.435 total time= 10.3s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=2, splitter=best;, score=0.470 total time= 13.0s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=2, splitter=random;, score=0.132 total time= 4.9s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=2, splitter=random;, score=-0.043 total time= 5.4s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=4, splitter=best;, score=0.391 total time= 11.4s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=4, splitter=best;, score=0.424 total time= 8.2s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=4, splitter=random;, score=-0.148 total time= 1.1s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=4, splitter=random;, score=0.199 total time= 7.6s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=6, splitter=best;, score=0.395 total time= 7.7s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=6, splitter=best;, score=0.002 total time= 4.2s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=6, splitter=random;, score=-0.101 total time= 2.8s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=6, splitter=random;, score=-0.084 total time= 4.0s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=8, splitter=best;, score=0.396 total time= 10.1s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=8, splitter=best;, score=0.462 total time= 8.6s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=8, splitter=random;, score=-0.078 total time= 3.8s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=8, splitter=random;, score=0.215 total time= 2.7s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=10, splitter=best;, score=-0.099 total time= 5.1s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=10, splitter=best;, score=0.468 total time= 10.1s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=10, splitter=random;, score=-0.147 total time= 3.3s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=10, splitter=random;, score=0.328 total time= 3.8s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=12, splitter=best;, score=0.447 total time= 10.1s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=12, splitter=best;, score=0.442 total time= 10.4s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=12, splitter=random;, score=-0.076 total time= 2.4s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=12, splitter=random;, score=0.163 total time= 3.7s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=14, splitter=best;, score=-0.148 total time= 1.6s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=14, splitter=best;, score=0.453 total time= 13.4s [CV 1/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=14, splitter=random;, score=0.492 total time= 7.3s [CV 2/2] END criterion=mae, max_depth=12, max_features=log2, min_samples_split=14, splitter=random;, score=0.022 total time= 1.3s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=2, splitter=best;, score=0.360 total time= 26.3s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=2, splitter=best;, score=0.407 total time= 24.0s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=2, splitter=random;, score=0.413 total time= 11.6s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=2, splitter=random;, score=0.478 total time= 22.2s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=4, splitter=best;, score=0.385 total time= 20.0s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=4, splitter=best;, score=0.384 total time= 19.8s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=4, splitter=random;, score=0.150 total time= 10.4s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=4, splitter=random;, score=0.448 total time= 16.6s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=6, splitter=best;, score=0.387 total time= 18.1s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=6, splitter=best;, score=0.399 total time= 23.8s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=6, splitter=random;, score=0.423 total time= 15.3s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=6, splitter=random;, score=0.223 total time= 18.8s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=8, splitter=best;, score=0.387 total time= 17.5s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=8, splitter=best;, score=0.403 total time= 27.1s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=8, splitter=random;, score=0.444 total time= 20.6s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=8, splitter=random;, score=0.492 total time= 11.1s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=10, splitter=best;, score=0.407 total time= 24.4s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=10, splitter=best;, score=0.418 total time= 21.9s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=10, splitter=random;, score=0.455 total time= 23.9s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=10, splitter=random;, score=0.462 total time= 19.8s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=12, splitter=best;, score=0.414 total time= 18.9s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=12, splitter=best;, score=0.417 total time= 20.2s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=12, splitter=random;, score=0.437 total time= 19.5s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=12, splitter=random;, score=0.455 total time= 13.7s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=14, splitter=best;, score=0.417 total time= 19.2s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=14, splitter=best;, score=0.429 total time= 21.8s [CV 1/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=14, splitter=random;, score=0.468 total time= 20.7s [CV 2/2] END criterion=mae, max_depth=14, max_features=auto, min_samples_split=14, splitter=random;, score=0.294 total time= 22.9s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=best;, score=-0.148 total time= 1.2s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=best;, score=0.394 total time= 7.7s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=random;, score=-0.034 total time= 2.5s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=2, splitter=random;, score=0.018 total time= 3.9s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.406 total time= 8.8s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=best;, score=0.427 total time= 6.2s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.148 total time= 0.9s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=4, splitter=random;, score=-0.003 total time= 3.8s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.388 total time= 6.1s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=best;, score=0.413 total time= 7.0s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.294 total time= 3.9s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=6, splitter=random;, score=0.110 total time= 3.4s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.430 total time= 7.5s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=best;, score=0.154 total time= 2.4s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=random;, score=-0.111 total time= 3.0s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=8, splitter=random;, score=0.118 total time= 2.3s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.368 total time= 7.8s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=best;, score=0.447 total time= 6.6s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=random;, score=-0.134 total time= 3.5s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=10, splitter=random;, score=0.035 total time= 1.9s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.414 total time= 7.7s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=best;, score=0.433 total time= 6.5s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=random;, score=-0.143 total time= 2.0s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=12, splitter=random;, score=0.092 total time= 3.0s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.413 total time= 9.2s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=best;, score=0.455 total time= 8.2s [CV 1/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.310 total time= 2.9s [CV 2/2] END criterion=mae, max_depth=14, max_features=sqrt, min_samples_split=14, splitter=random;, score=0.444 total time= 4.7s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=2, splitter=best;, score=0.371 total time= 12.9s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=2, splitter=best;, score=0.410 total time= 9.2s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=2, splitter=random;, score=0.136 total time= 5.8s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=2, splitter=random;, score=0.107 total time= 6.4s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=4, splitter=best;, score=0.368 total time= 12.5s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=4, splitter=best;, score=0.492 total time= 14.7s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=4, splitter=random;, score=0.342 total time= 5.1s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=4, splitter=random;, score=0.223 total time= 7.0s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=6, splitter=best;, score=-0.148 total time= 1.5s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=6, splitter=best;, score=0.425 total time= 12.4s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=6, splitter=random;, score=-0.144 total time= 1.8s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=6, splitter=random;, score=0.217 total time= 6.5s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=8, splitter=best;, score=0.423 total time= 13.3s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=8, splitter=best;, score=0.420 total time= 9.9s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=8, splitter=random;, score=-0.148 total time= 1.0s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=8, splitter=random;, score=-0.068 total time= 6.4s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=10, splitter=best;, score=0.396 total time= 6.8s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=10, splitter=best;, score=0.446 total time= 11.5s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=10, splitter=random;, score=-0.148 total time= 2.0s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=10, splitter=random;, score=0.118 total time= 9.2s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=12, splitter=best;, score=0.414 total time= 11.6s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=12, splitter=best;, score=0.451 total time= 12.9s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=12, splitter=random;, score=0.419 total time= 3.9s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=12, splitter=random;, score=0.115 total time= 4.0s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=14, splitter=best;, score=0.433 total time= 8.5s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=14, splitter=best;, score=0.467 total time= 12.1s [CV 1/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=14, splitter=random;, score=0.388 total time= 4.1s [CV 2/2] END criterion=mae, max_depth=14, max_features=log2, min_samples_split=14, splitter=random;, score=0.187 total time= 5.4s
# criterion, splitter, max_features, max_depth, min_samples_split
print("Best Criterion: ", criterion)
print("Best Splitter: ", splitter)
print("Best number of features: ", max_features)
print("Best Depth: ", max_depth)
print("Best Min Sample Split: ", min_samples_split)
Best Criterion: mse Best Splitter: best Best number of features: auto Best Depth: 6 Best Min Sample Split: 2
decisionTreeReg = DecisionTreeRegressor(criterion="mse",splitter="best",max_features="auto",max_depth=6,min_samples_split=2)
decisionTreeReg.fit(x_train, y_train)
y_pred = decisionTreeReg.predict(x_test)
r2 = r2_score(y_test,y_pred)
print(f"\nR2 Score: {r2}")
R2 Score: 0.12637498734822006
Support Vector Machines
from sklearn import svm
list_kernel = ["linear", "rbf"]
for kern in list_kernel:
print("Kernel :", kern)
clf = svm.SVR(kernel=kern)
y_pred = clf.fit(x_train, y_train).predict(x_test)
mse = np.mean((clf.predict(x_test) - y_test) ** 2)
# print("Mean squared error: %.2f" % np.mean((clf.predict(x_test) - y_test) ** 2))
r2 = r2_score(y_test,y_pred)
print(f"\nR2 Score: {r2}")
# print('Variance score: %.2f' % clf.score(x_test, y_test))
# print()
Kernel : linear R2 Score: 0.04039864596330811 Kernel : rbf R2 Score: 0.5762773021729313
XG Boost
Using GridSearch to find best parameters for XGBoost
def get_best_params_for_xgboost(train_x,train_y):
try:
param_grid_xgboost = {
'learning_rate': [0.5, 0.1, 0.01, 0.001],
'max_depth': [3, 5, 10, 20],
'n_estimators': [10, 50, 100, 200]
}
grid= GridSearchCV(XGBRegressor(objective='reg:linear'),param_grid_xgboost, verbose=3,cv=5)
grid.fit(train_x, train_y)
learning_rate = grid.best_params_['learning_rate']
max_depth = grid.best_params_['max_depth']
n_estimators = grid.best_params_['n_estimators']
return learning_rate, max_depth, n_estimators
except Exception as e:
print(e)
learning_rate, max_depth, n_estimators = get_best_params_for_xgboost(x_train,y_train)
Fitting 5 folds for each of 64 candidates, totalling 320 fits [14:15:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.644 total time= 0.5s [14:15:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.546 total time= 0.5s [14:15:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.663 total time= 0.4s [14:15:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.638 total time= 0.5s [14:15:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=3, n_estimators=10;, score=0.534 total time= 0.5s [14:15:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.650 total time= 1.0s [14:16:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.549 total time= 1.0s [14:16:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.653 total time= 1.6s [14:16:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.643 total time= 1.0s [14:16:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=3, n_estimators=50;, score=0.543 total time= 1.0s [14:16:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.648 total time= 1.9s [14:16:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.549 total time= 2.0s [14:16:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.646 total time= 1.9s [14:16:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.645 total time= 1.9s [14:16:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=3, n_estimators=100;, score=0.548 total time= 1.9s [14:16:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.636 total time= 3.8s [14:16:18] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.543 total time= 3.8s [14:16:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.641 total time= 3.8s [14:16:26] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.636 total time= 3.8s [14:16:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=3, n_estimators=200;, score=0.534 total time= 3.7s [14:16:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.649 total time= 0.4s [14:16:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.548 total time= 0.4s [14:16:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.654 total time= 0.4s [14:16:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.643 total time= 0.4s [14:16:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=5, n_estimators=10;, score=0.555 total time= 0.4s [14:16:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.635 total time= 1.5s [14:16:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.531 total time= 1.6s [14:16:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.628 total time= 1.5s [14:16:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.631 total time= 1.6s [14:16:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=5, n_estimators=50;, score=0.534 total time= 1.5s [14:16:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.614 total time= 3.0s [14:16:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.519 total time= 3.0s [14:16:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.606 total time= 3.0s [14:16:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.619 total time= 3.0s [14:16:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=5, n_estimators=100;, score=0.517 total time= 3.0s [14:16:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.593 total time= 6.0s [14:17:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.496 total time= 6.1s [14:17:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.571 total time= 6.0s [14:17:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.603 total time= 6.0s [14:17:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=5, n_estimators=200;, score=0.485 total time= 6.0s [14:17:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.604 total time= 0.7s [14:17:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.490 total time= 0.8s [14:17:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.569 total time= 1.2s [14:17:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.603 total time= 1.1s [14:17:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=10, n_estimators=10;, score=0.459 total time= 0.7s [14:17:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.552 total time= 3.4s [14:17:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.433 total time= 3.4s [14:17:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.505 total time= 3.4s [14:17:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.557 total time= 3.4s [14:17:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=10, n_estimators=50;, score=0.366 total time= 4.0s [14:17:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.539 total time= 6.8s [14:17:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.402 total time= 6.8s [14:18:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.491 total time= 6.8s [14:18:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.540 total time= 6.7s [14:18:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=10, n_estimators=100;, score=0.327 total time= 6.8s [14:18:24] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.533 total time= 14.2s [14:18:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.393 total time= 14.5s [14:18:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.484 total time= 15.0s [14:19:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.534 total time= 14.6s [14:19:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=10, n_estimators=200;, score=0.311 total time= 13.6s [14:19:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.565 total time= 1.6s [14:19:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.392 total time= 1.6s [14:19:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.507 total time= 1.6s [14:19:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.537 total time= 1.6s [14:19:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=20, n_estimators=10;, score=0.331 total time= 1.6s [14:19:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.560 total time= 6.7s [14:19:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.381 total time= 7.0s [14:19:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.499 total time= 6.9s [14:20:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.530 total time= 6.8s [14:20:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=20, n_estimators=50;, score=0.318 total time= 6.7s [14:20:18] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.560 total time= 7.2s [14:20:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.381 total time= 7.4s [14:20:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.499 total time= 7.3s [14:20:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.530 total time= 7.1s [14:20:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=20, n_estimators=100;, score=0.318 total time= 7.1s [14:20:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.560 total time= 8.1s [14:21:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.381 total time= 8.4s [14:21:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.499 total time= 8.3s [14:21:19] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.530 total time= 8.1s [14:21:27] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.5, max_depth=20, n_estimators=200;, score=0.318 total time= 8.0s [14:21:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=-1.106 total time= 0.2s [14:21:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=-1.563 total time= 0.3s [14:21:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=-1.007 total time= 0.3s [14:21:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=-1.379 total time= 0.2s [14:21:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=10;, score=-2.686 total time= 0.3s [14:21:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.653 total time= 1.0s [14:21:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.549 total time= 1.0s [14:21:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.670 total time= 1.0s [14:21:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.639 total time= 1.0s [14:21:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=50;, score=0.539 total time= 1.0s [14:21:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.657 total time= 1.9s [14:21:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.553 total time= 1.9s [14:21:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.669 total time= 1.9s [14:21:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.646 total time= 1.9s [14:21:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=100;, score=0.547 total time= 1.9s [14:21:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.656 total time= 3.7s [14:21:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.552 total time= 3.7s [14:21:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.667 total time= 3.7s [14:22:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.649 total time= 3.7s [14:22:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=3, n_estimators=200;, score=0.551 total time= 3.7s [14:22:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=-1.092 total time= 0.4s [14:22:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=-1.518 total time= 0.4s [14:22:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=-0.991 total time= 0.4s [14:22:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=-1.354 total time= 0.4s [14:22:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=10;, score=-2.621 total time= 0.4s [14:22:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.662 total time= 1.6s [14:22:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.554 total time= 1.6s [14:22:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.668 total time= 1.5s [14:22:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.651 total time= 1.6s [14:22:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=50;, score=0.558 total time= 1.5s [14:22:19] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.659 total time= 3.0s [14:22:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.554 total time= 3.0s [14:22:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.669 total time= 3.0s [14:22:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.651 total time= 3.0s [14:22:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=100;, score=0.561 total time= 3.0s [14:22:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.656 total time= 5.9s [14:22:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.550 total time= 6.0s [14:22:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.658 total time= 6.0s [14:22:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.646 total time= 6.0s [14:22:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=5, n_estimators=200;, score=0.557 total time= 5.9s [14:23:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=-1.111 total time= 0.7s [14:23:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=-1.517 total time= 0.7s [14:23:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=-1.011 total time= 0.7s [14:23:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=-1.360 total time= 0.7s [14:23:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=10, n_estimators=10;, score=-2.627 total time= 0.7s [14:23:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.634 total time= 3.4s [14:23:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.520 total time= 3.4s [14:23:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.619 total time= 3.4s [14:23:18] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.629 total time= 3.5s [14:23:21] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=10, n_estimators=50;, score=0.502 total time= 3.5s [14:23:24] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.620 total time= 6.5s [14:23:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.510 total time= 6.6s [14:23:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.604 total time= 6.6s [14:23:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.619 total time= 6.6s [14:23:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=10, n_estimators=100;, score=0.496 total time= 6.6s [14:23:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.598 total time= 13.1s [14:24:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.489 total time= 13.2s [14:24:24] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.584 total time= 13.2s [14:24:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.607 total time= 13.1s [14:24:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=10, n_estimators=200;, score=0.467 total time= 13.5s [14:25:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=-1.142 total time= 1.3s [14:25:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=-1.562 total time= 1.3s [14:25:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=-1.065 total time= 1.3s [14:25:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=-1.385 total time= 1.3s [14:25:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=20, n_estimators=10;, score=-2.686 total time= 1.3s [14:25:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.593 total time= 7.4s [14:25:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.459 total time= 7.4s [14:25:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.543 total time= 7.4s [14:25:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.591 total time= 7.4s [14:25:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=20, n_estimators=50;, score=0.406 total time= 7.5s [14:25:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.585 total time= 16.7s [14:26:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.450 total time= 16.4s [14:26:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.537 total time= 16.3s [14:26:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.582 total time= 16.9s [14:26:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=20, n_estimators=100;, score=0.399 total time= 16.5s [14:27:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.584 total time= 34.7s [14:27:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.448 total time= 35.1s [14:28:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.535 total time= 35.0s [14:28:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.580 total time= 36.3s [14:29:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.1, max_depth=20, n_estimators=200;, score=0.393 total time= 36.8s [14:30:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=-11.850 total time= 0.6s [14:30:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=-13.241 total time= 0.6s [14:30:09] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=-10.208 total time= 0.5s [14:30:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=-13.119 total time= 0.6s [14:30:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=10;, score=-19.650 total time= 0.6s [14:30:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=-4.839 total time= 1.8s [14:30:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=-5.679 total time= 1.8s [14:30:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=-4.246 total time= 1.8s [14:30:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=-5.487 total time= 2.2s [14:30:18] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=50;, score=-8.700 total time= 1.2s [14:30:19] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=-1.292 total time= 1.9s [14:30:21] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=-1.770 total time= 2.0s [14:30:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=-1.173 total time= 1.9s [14:30:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=-1.586 total time= 1.9s [14:30:27] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=100;, score=-2.996 total time= 1.9s [14:30:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.419 total time= 3.8s [14:30:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.218 total time= 3.8s [14:30:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.394 total time= 3.8s [14:30:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=0.346 total time= 3.7s [14:30:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=3, n_estimators=200;, score=-0.023 total time= 3.8s [14:30:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=-11.851 total time= 0.4s [14:30:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=-13.228 total time= 0.4s [14:30:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=-10.208 total time= 0.4s [14:30:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=-13.115 total time= 0.4s [14:30:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=10;, score=-19.632 total time= 0.4s [14:30:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=-4.834 total time= 1.5s [14:30:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=-5.637 total time= 1.5s [14:30:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=-4.241 total time= 1.5s [14:30:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=-5.470 total time= 1.6s [14:30:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=50;, score=-8.642 total time= 1.5s [14:30:58] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=-1.279 total time= 3.0s [14:31:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=-1.728 total time= 3.1s [14:31:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=-1.157 total time= 3.1s [14:31:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=-1.563 total time= 3.1s [14:31:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=100;, score=-2.929 total time= 5.6s [14:31:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.438 total time= 7.4s [14:31:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.242 total time= 6.0s [14:31:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.409 total time= 6.1s [14:31:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.368 total time= 6.0s [14:31:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=5, n_estimators=200;, score=0.023 total time= 6.1s [14:31:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=-11.854 total time= 0.7s [14:31:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=-13.227 total time= 0.7s [14:31:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=-10.210 total time= 0.7s [14:31:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=-13.117 total time= 0.7s [14:31:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=10, n_estimators=10;, score=-19.627 total time= 0.7s [14:31:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=-4.847 total time= 3.2s [14:31:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=-5.635 total time= 3.2s [14:31:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=-4.250 total time= 3.2s [14:32:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=-5.476 total time= 3.2s [14:32:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=10, n_estimators=50;, score=-8.645 total time= 3.4s [14:32:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=-1.298 total time= 6.5s [14:32:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=-1.728 total time= 6.6s [14:32:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=-1.173 total time= 6.5s [14:32:26] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=-1.569 total time= 6.5s [14:32:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=10, n_estimators=100;, score=-2.940 total time= 6.7s [14:32:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.422 total time= 13.5s [14:32:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.229 total time= 13.5s [14:33:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.378 total time= 13.6s [14:33:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=0.364 total time= 13.7s [14:33:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=10, n_estimators=200;, score=-0.002 total time= 13.6s [14:33:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=-11.859 total time= 1.2s [14:33:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=-13.232 total time= 1.2s [14:33:50] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=-10.217 total time= 1.2s [14:33:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=-13.120 total time= 1.2s [14:33:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=20, n_estimators=10;, score=-19.638 total time= 1.2s [14:33:53] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=-4.864 total time= 5.8s [14:33:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=-5.659 total time= 5.8s [14:34:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=-4.289 total time= 5.8s [14:34:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=-5.491 total time= 5.9s [14:34:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=20, n_estimators=50;, score=-8.679 total time= 6.0s [14:34:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=-1.329 total time= 12.0s [14:34:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=-1.771 total time= 12.0s [14:34:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=-1.231 total time= 11.8s [14:34:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=-1.594 total time= 12.3s [14:35:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=20, n_estimators=100;, score=-2.999 total time= 12.3s [14:35:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.386 total time= 25.5s [14:35:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.172 total time= 25.8s [14:36:15] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.307 total time= 25.3s [14:36:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=0.323 total time= 26.0s [14:37:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.01, max_depth=20, n_estimators=200;, score=-0.099 total time= 26.1s [14:37:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=-14.383 total time= 0.3s [14:37:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=-15.952 total time= 0.3s [14:37:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=-12.347 total time= 0.3s [14:37:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=-15.865 total time= 0.3s [14:37:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=10;, score=-23.562 total time= 0.3s [14:37:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-13.206 total time= 1.0s [14:37:34] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-14.693 total time= 1.0s [14:37:35] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-11.353 total time= 1.0s [14:37:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-14.589 total time= 1.0s [14:37:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=50;, score=-21.745 total time= 1.0s [14:37:38] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-11.862 total time= 1.9s [14:37:40] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-13.253 total time= 1.9s [14:37:42] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-10.218 total time= 1.9s [14:37:44] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-13.131 total time= 1.9s [14:37:46] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=100;, score=-19.668 total time= 1.9s [14:37:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=-9.551 total time= 3.7s [14:37:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=-10.771 total time= 3.7s [14:37:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=-8.261 total time= 3.7s [14:37:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=-10.621 total time= 3.7s [14:38:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=3, n_estimators=200;, score=-16.083 total time= 3.7s [14:38:06] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=-14.383 total time= 0.4s [14:38:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=-15.951 total time= 0.4s [14:38:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=-12.347 total time= 0.4s [14:38:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=-15.865 total time= 0.4s [14:38:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=10;, score=-23.560 total time= 0.4s [14:38:08] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-13.206 total time= 1.6s [14:38:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-14.686 total time= 1.5s [14:38:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-11.353 total time= 1.5s [14:38:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-14.587 total time= 1.6s [14:38:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=50;, score=-21.735 total time= 1.5s [14:38:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-11.862 total time= 3.0s [14:38:19] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-13.241 total time= 3.0s [14:38:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-10.218 total time= 3.1s [14:38:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-13.127 total time= 3.1s [14:38:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=100;, score=-19.650 total time= 3.0s [14:38:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=-9.550 total time= 5.9s [14:38:37] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=-10.747 total time= 5.9s [14:38:43] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=-8.260 total time= 6.0s [14:38:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=-10.613 total time= 6.1s [14:38:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=5, n_estimators=200;, score=-16.049 total time= 6.0s [14:39:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=-14.383 total time= 0.7s [14:39:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=-15.951 total time= 0.7s [14:39:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=-12.347 total time= 0.7s [14:39:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=-15.865 total time= 0.7s [14:39:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=10, n_estimators=10;, score=-23.559 total time= 0.7s [14:39:04] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=-13.207 total time= 3.1s [14:39:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=-14.685 total time= 3.1s [14:39:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=-11.354 total time= 3.2s [14:39:14] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=-14.588 total time= 3.2s [14:39:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=10, n_estimators=50;, score=-21.733 total time= 3.1s [14:39:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=-11.866 total time= 6.2s [14:39:26] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=-13.239 total time= 6.2s [14:39:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=-10.220 total time= 6.2s [14:39:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=-13.129 total time= 6.2s [14:39:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=10, n_estimators=100;, score=-19.645 total time= 6.3s [14:39:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=-9.556 total time= 12.3s [14:40:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=-10.747 total time= 12.5s [14:40:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=-8.265 total time= 12.7s [14:40:29] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=-10.617 total time= 12.5s [14:40:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=10, n_estimators=200;, score=-16.044 total time= 12.6s [14:40:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=-14.384 total time= 1.2s [14:40:55] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=-15.951 total time= 1.2s [14:40:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=-12.348 total time= 1.2s [14:40:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=-15.865 total time= 1.2s [14:40:59] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=20, n_estimators=10;, score=-23.560 total time= 1.2s [14:41:00] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=-13.210 total time= 5.7s [14:41:05] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=-14.687 total time= 5.7s [14:41:11] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=-11.357 total time= 5.6s [14:41:17] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=-14.590 total time= 5.7s [14:41:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=20, n_estimators=50;, score=-21.739 total time= 5.8s [14:41:28] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=-11.870 total time= 11.2s [14:41:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=-13.245 total time= 11.3s [14:41:51] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=-10.227 total time= 11.3s [14:42:02] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=-13.132 total time= 11.4s [14:42:13] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=20, n_estimators=100;, score=-19.656 total time= 11.4s [14:42:25] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 1/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=-9.564 total time= 22.7s [14:42:48] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 2/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=-10.759 total time= 22.8s [14:43:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 3/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=-8.281 total time= 22.8s [14:43:33] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 4/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=-10.622 total time= 22.7s [14:43:56] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [CV 5/5] END learning_rate=0.001, max_depth=20, n_estimators=200;, score=-16.063 total time= 23.2s [14:44:19] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.
--------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-33-fffc78618978> in <module>() 1 learning_rate, max_depth, n_estimators = get_best_params_for_xgboost(x_train,y_train) ----> 2 print("Best Learning Rate: " + learning_rate) 3 print("Best Max Depth: " + max_depth) 4 print("Best N Estimators: " + n_estimators) TypeError: can only concatenate str (not "float") to str
print("Best Learning Rate: ", learning_rate)
print("Best Max Depth: ", max_depth)
print("Best N Estimators: ", n_estimators)
Best Learning Rate: 0.1 Best Max Depth: 5 Best N Estimators: 100
xg_reg = xgb.XGBRegressor(objective ='reg:linear', learning_rate = 0.1,
max_depth = 5, n_estimators = 100)
xg_reg.fit(x_train,y_train)
y_pred = xg_reg.predict(x_test)
r2 = r2_score(y_test,y_pred)
print(f"\nR2 Score: {r2}")
[16:01:49] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. R2 Score: 0.18201136360103887
Since for individual models we are not getting significant errors in the predicted values. We decided to change our approach from single model to a multimodel approach.
This will be done by first using K-Means clustering to divide the dataset into mutiple clusters and then using separate models for each cluster
pip install kneed==0.5.1
Collecting kneed==0.5.1 Downloading kneed-0.5.1-py2.py3-none-any.whl (9.9 kB) Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from kneed==0.5.1) (3.2.2) Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from kneed==0.5.1) (1.4.1) Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from kneed==0.5.1) (1.0.2) Requirement already satisfied: numpy>=1.14.2 in /usr/local/lib/python3.7/dist-packages (from kneed==0.5.1) (1.21.6) Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->kneed==0.5.1) (2.8.2) Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->kneed==0.5.1) (0.11.0) Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->kneed==0.5.1) (3.0.8) Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->kneed==0.5.1) (1.4.2) Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib->kneed==0.5.1) (4.1.1) Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->kneed==0.5.1) (1.15.0) Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->kneed==0.5.1) (1.1.0) Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn->kneed==0.5.1) (3.1.0) Installing collected packages: kneed Successfully installed kneed-0.5.1
from sklearn.cluster import KMeans
from kneed import KneeLocator
from sklearn import svm
Finding Knee of the Cluster for optimal number of clusters
x_train, x_test, y_train, y_test = train_test_split(X_, y, train_size=0.7)
def elbow_plot(data):
wcss=[] #within-clusters-sum-of-squares
try:
for i in range (1,11):
kmeans=KMeans(n_clusters=i,init='k-means++',random_state=42)
kmeans.fit(data)
wcss.append(kmeans.inertia_)
plt.plot(range(1,11),wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()
kn = KneeLocator(range(1, 11), wcss, curve='convex', direction='decreasing')
print('The optimum number of clusters is: '+ str(kn.knee))
return kn.knee
except Exception as e:
print(e)
model_directory='models/'
def save_model_(model,filename):
try:
path = os.path.join(model_directory,filename)
if os.path.isdir(path):
shutil.rmtree(model_directory)
os.makedirs(path)
else:
os.makedirs(path)
with open(path +'/' + filename+'.sav',
'wb') as f:
pickle.dump(model, f)
return 'success'
except Exception as e:
print(e)
def create_clusters(data,number_of_clusters):
data=data
try:
kmeans = KMeans(n_clusters=number_of_clusters, init='k-means++', random_state=42)
y_kmeans=kmeans.fit_predict(data)
save_model = save_model_(kmeans, 'KMeans') # saving the KMeans model to directory
# passing 'Model' as the functions need three parameters
data['Cluster']=y_kmeans # create a new column in dataset for storing the cluster information
return data
except Exception as e:
print(exec)
number_of_clusters=elbow_plot(x_train) # using the elbow plot to find the number of optimum clusters
The optimum number of clusters is: 4
x_train
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|
58457 | 1.014454 | 0.964336 | -1.844945 | -0.667312 | -0.13621 | 0.264630 | 1.280085 | -0.075699 |
4415 | 1.014454 | 0.210558 | -0.533330 | 0.016601 | -0.13621 | -1.112097 | -0.696737 | -0.075699 |
25929 | -0.365404 | 0.763328 | 1.106189 | 0.358558 | -0.13621 | 1.854339 | -0.037796 | -0.075699 |
70355 | -1.170321 | -0.392464 | -1.353089 | 2.068342 | -0.13621 | -0.530224 | -1.179114 | -0.075699 |
59826 | -0.537886 | -0.543219 | 1.598045 | 0.743259 | -0.13621 | -0.953157 | 0.809322 | -0.075699 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
34762 | -0.882851 | -0.342212 | -0.041474 | 0.486792 | -0.13621 | -1.112097 | -0.696737 | -0.075699 |
29293 | 0.094548 | -0.342212 | 1.106189 | 1.042472 | -0.13621 | 1.482417 | -0.884914 | -0.075699 |
1232 | -1.112827 | -0.291960 | 0.614333 | -0.239866 | -0.13621 | -1.325079 | -0.037796 | -0.075699 |
37703 | -0.767863 | -0.593471 | -1.353089 | 1.256195 | -0.13621 | 1.286476 | 0.971759 | -0.075699 |
34537 | -1.112827 | 1.064839 | 0.286430 | -0.111632 | -0.13621 | -1.229208 | -0.488538 | -0.075699 |
52583 rows × 8 columns
# Divide the data into clusters
new_data = x_train[:]
new_data=create_clusters(x_train, number_of_clusters)
new_data
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | Cluster | |
---|---|---|---|---|---|---|---|---|---|
58457 | 1.014454 | 0.964336 | -1.844945 | -0.667312 | -0.13621 | 0.264630 | 1.280085 | -0.075699 | 2 |
4415 | 1.014454 | 0.210558 | -0.533330 | 0.016601 | -0.13621 | -1.112097 | -0.696737 | -0.075699 | 0 |
25929 | -0.365404 | 0.763328 | 1.106189 | 0.358558 | -0.13621 | 1.854339 | -0.037796 | -0.075699 | 2 |
70355 | -1.170321 | -0.392464 | -1.353089 | 2.068342 | -0.13621 | -0.530224 | -1.179114 | -0.075699 | 0 |
59826 | -0.537886 | -0.543219 | 1.598045 | 0.743259 | -0.13621 | -0.953157 | 0.809322 | -0.075699 | 1 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
34762 | -0.882851 | -0.342212 | -0.041474 | 0.486792 | -0.13621 | -1.112097 | -0.696737 | -0.075699 | 1 |
29293 | 0.094548 | -0.342212 | 1.106189 | 1.042472 | -0.13621 | 1.482417 | -0.884914 | -0.075699 | 0 |
1232 | -1.112827 | -0.291960 | 0.614333 | -0.239866 | -0.13621 | -1.325079 | -0.037796 | -0.075699 | 1 |
37703 | -0.767863 | -0.593471 | -1.353089 | 1.256195 | -0.13621 | 1.286476 | 0.971759 | -0.075699 | 2 |
34537 | -1.112827 | 1.064839 | 0.286430 | -0.111632 | -0.13621 | -1.229208 | -0.488538 | -0.075699 | 0 |
52583 rows × 9 columns
#create a new column in the dataset consisting of the corresponding cluster assignments.
new_data['Labels']=y
new_data
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | Cluster | Labels | |
---|---|---|---|---|---|---|---|---|---|---|
58457 | 1.014454 | 0.964336 | -1.844945 | -0.667312 | -0.13621 | 0.264630 | 1.280085 | -0.075699 | 2 | 10.0 |
4415 | 1.014454 | 0.210558 | -0.533330 | 0.016601 | -0.13621 | -1.112097 | -0.696737 | -0.075699 | 0 | 10.0 |
25929 | -0.365404 | 0.763328 | 1.106189 | 0.358558 | -0.13621 | 1.854339 | -0.037796 | -0.075699 | 2 | 10.0 |
70355 | -1.170321 | -0.392464 | -1.353089 | 2.068342 | -0.13621 | -0.530224 | -1.179114 | -0.075699 | 0 | 10.0 |
59826 | -0.537886 | -0.543219 | 1.598045 | 0.743259 | -0.13621 | -0.953157 | 0.809322 | -0.075699 | 1 | 10.0 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
34762 | -0.882851 | -0.342212 | -0.041474 | 0.486792 | -0.13621 | -1.112097 | -0.696737 | -0.075699 | 1 | 10.0 |
29293 | 0.094548 | -0.342212 | 1.106189 | 1.042472 | -0.13621 | 1.482417 | -0.884914 | -0.075699 | 0 | 10.0 |
1232 | -1.112827 | -0.291960 | 0.614333 | -0.239866 | -0.13621 | -1.325079 | -0.037796 | -0.075699 | 1 | 10.0 |
37703 | -0.767863 | -0.593471 | -1.353089 | 1.256195 | -0.13621 | 1.286476 | 0.971759 | -0.075699 | 2 | 10.0 |
34537 | -1.112827 | 1.064839 | 0.286430 | -0.111632 | -0.13621 | -1.229208 | -0.488538 | -0.075699 | 0 | 10.0 |
52583 rows × 10 columns
# getting the unique clusters from our dataset
list_of_clusters=new_data['Cluster'].unique()
print(list_of_clusters)
[2 0 1 3]
def standardScalingData(X):
scalar = StandardScaler()
X_scaled = scalar.fit_transform(X)
return X_scaled
def get_best_model(x_train,y_train,x_test,y_test):
try:
result = []
# create best model for Decision Tree Regressor
decisionTreeReg = DecisionTreeRegressor(criterion="mse",splitter="best",max_features="auto",max_depth=6,min_samples_split=2)
decisionTreeReg.fit(x_train, y_train)
y_pred = decisionTreeReg.predict(x_test)
decisionTreeReg_error = mean_squared_error(y_test,y_pred)
result.append(decisionTreeReg_error)
# create best model for XGBoost
xg_reg = xgb.XGBRegressor(objective ='reg:linear', learning_rate = 0.1,
max_depth = 5, n_estimators = 100)
xg_reg.fit(x_train,y_train)
y_pred = xg_reg.predict(x_test)
prediction_xgboost_error = mean_squared_error(y_test,y_pred)
result.append(prediction_xgboost_error)
# create best model for SVM Regression
list_kernel = ["rbf"]
for kern in list_kernel:
clf = svm.SVR(kernel=kern)
y_pred = clf.fit(x_train, y_train).predict(x_test)
prediction_svm_error = np.mean((clf.predict(x_test) - y_test) ** 2)
result.append(prediction_svm_error)
# create best model for Polynomial Regression
poly = PolynomialFeatures()
X_poly = poly.fit_transform(x_train)
poly.fit(x_train,y_train)
model = LinearRegression()
model.fit(X_poly, y_train)
y_pred = model.predict(poly.fit_transform(x_test))
prediction_poly_error = mean_squared_error(y_test,y_pred)
result.append(prediction_poly_error)
print(result)
#comparing all the models
sorted_result = result[:]
sorted_result.sort()
min = sorted_result[0]
ind = result.index(min)
if ind == 0:
return 'DecisionTreeReg',decisionTreeReg
elif ind == 1:
return 'XGBoost', xg_reg
elif ind == 2:
return 'SVM', clf
else:
return 'Poly', model
except Exception as e:
print(e)
for i in list_of_clusters:
cluster_data=new_data[new_data['Cluster']==i] # filter the data for one cluster
# Prepare the feature and Label columns
cluster_features=cluster_data.drop(['Labels','Cluster'],axis=1)
cluster_label= cluster_data['Labels']
# splitting the data into training and test set for each cluster one by one
train_x, test_x, train_y, test_y = train_test_split(cluster_features, cluster_label, test_size=1 / 3)
x_train_scaled = standardScalingData(train_x)
x_test_scaled = standardScalingData(test_x)
#getting the best model for each of the clusters
best_model_name,best_model=get_best_model(x_train_scaled,train_y,x_test_scaled,test_y)
save_model = save_model_(best_model,best_model_name+str(i))
print('Successful End of Training')
[17:09:01] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [3.2276681447710285, 2.9175835428959003, 4.025773089753407, 19421.807376420755] [17:09:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [2.8036627113605324, 2.5272176780990137, 3.416694778285685, 3310.7076100367176] [17:09:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [0.39583941876916967, 0.35898640030694157, 0.4943090213473558, 380301.99795636936] [17:09:23] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [4.223704093403168, 3.334224403869502, 4.7849532154777465, 586752816.080847] Successful End of Training
Testing
x_test
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|
34289 | -1.227815 | -1.146241 | -0.533330 | 2.752255 | -0.136210 | -0.011420 | 1.260064 | -0.075699 |
53811 | -0.767863 | 0.562321 | -0.697282 | -0.624568 | -0.136210 | -1.300928 | -0.266644 | -0.075699 |
7029 | -0.192922 | 0.612573 | -0.205426 | -0.923780 | -0.136210 | -1.112097 | -0.696737 | -0.075699 |
43008 | -1.227815 | 0.110054 | -0.205426 | 0.187580 | -0.136210 | -0.953157 | 0.809322 | -0.075699 |
37222 | -0.825357 | -0.593471 | 0.942237 | 0.273069 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
2465 | -0.250416 | -0.543219 | -0.861233 | 2.025597 | -0.136210 | -0.530224 | -1.179114 | -0.075699 |
42984 | -1.112827 | 0.863832 | 0.778285 | -0.710057 | 0.161577 | -0.530224 | 1.103522 | -0.075699 |
44310 | -1.687768 | -1.045738 | 0.286430 | 0.444047 | -0.136210 | 0.540680 | 1.260064 | -0.075699 |
31282 | 1.301924 | -0.040701 | -0.533330 | 0.230324 | -0.136210 | 0.540680 | -1.335656 | -0.075699 |
53031 | -1.400298 | -1.749263 | 1.106189 | -0.068888 | -0.136210 | -1.112097 | 0.621144 | -0.075699 |
22536 rows × 8 columns
y_test
34289 10.0 53811 10.0 7029 10.0 43008 10.0 37222 10.0 ... 2465 10.0 42984 10.0 44310 10.0 31282 10.0 53031 10.0 Name: HOURLYVISIBILITY, Length: 22536, dtype: float64
data = x_test[:]
data
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|
34289 | -1.227815 | -1.146241 | -0.533330 | 2.752255 | -0.136210 | -0.011420 | 1.260064 | -0.075699 |
53811 | -0.767863 | 0.562321 | -0.697282 | -0.624568 | -0.136210 | -1.300928 | -0.266644 | -0.075699 |
7029 | -0.192922 | 0.612573 | -0.205426 | -0.923780 | -0.136210 | -1.112097 | -0.696737 | -0.075699 |
43008 | -1.227815 | 0.110054 | -0.205426 | 0.187580 | -0.136210 | -0.953157 | 0.809322 | -0.075699 |
37222 | -0.825357 | -0.593471 | 0.942237 | 0.273069 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
2465 | -0.250416 | -0.543219 | -0.861233 | 2.025597 | -0.136210 | -0.530224 | -1.179114 | -0.075699 |
42984 | -1.112827 | 0.863832 | 0.778285 | -0.710057 | 0.161577 | -0.530224 | 1.103522 | -0.075699 |
44310 | -1.687768 | -1.045738 | 0.286430 | 0.444047 | -0.136210 | 0.540680 | 1.260064 | -0.075699 |
31282 | 1.301924 | -0.040701 | -0.533330 | 0.230324 | -0.136210 | 0.540680 | -1.335656 | -0.075699 |
53031 | -1.400298 | -1.749263 | 1.106189 | -0.068888 | -0.136210 | -1.112097 | 0.621144 | -0.075699 |
22536 rows × 8 columns
def load_model(filename):
try:
with open(model_directory + filename + '/' + filename + '.sav',
'rb') as f:
return pickle.load(f)
except Exception as e:
print(e)
def find_correct_model_file(cluster_number):
try:
cluster_number= cluster_number
folder_name=model_directory
list_of_model_files = []
list_of_files = os.listdir(folder_name)
for file in list_of_files:
try:
if (file.index(str(cluster_number))!=-1):
model_name=file
except:
continue
model_name=model_name.split('.')[0]
return model_name
except Exception as e:
print(e)
kmeans=load_model('KMeans')
data_ = pd.DataFrame(data,columns=data.columns)
clusters=kmeans.predict(data_)
data_['clusters']=clusters
clusters=data_['clusters'].unique()
result=[] # initialize blank list for storing predicitons
for i in clusters:
cluster_data= data_[data_['clusters']==i]
cluster_data = cluster_data.drop(['clusters'],axis=1)
model_name = find_correct_model_file(i)
model = load_model(model_name)
for val in (model.predict(cluster_data.values)):
result.append(val)
r2 = r2_score(y_test,result)
print(f"\nR2 Score: {abs(r2)}")
result = pd.DataFrame(result,columns=['Predictions'])
result.to_csv("Predictions.csv",header=True) #appends result to prediction file
print('End of Prediction')
[08:05:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [08:05:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. [08:05:52] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror. R2 Score: 0.7560532721077209 End of Prediction
Deep Learning
from keras.models import Sequential
from keras.layers import Dense
from keras.wrappers.scikit_learn import KerasRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
def baseline_model():
model = Sequential()
model.add(Dense(4, kernel_initializer='normal', activation='relu'))
model.add(Dense(16,kernel_initializer='normal', activation='relu'))
model.add(Dense(32, kernel_initializer='normal', activation='relu'))
model.add(Dense(16, kernel_initializer='normal', activation='relu'))
model.add(Dense(4, kernel_initializer='normal', activation='relu'))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_squared_error', optimizer='adam')
return model
X_
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|
0 | -1.285309 | 1.366350 | -1.844945 | -0.154377 | 0.161577 | 0.264630 | 1.280085 | -0.075699 |
1 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | 0.459363 | 0.264630 | 1.280085 | -0.075699 |
2 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
3 | -1.285309 | 1.567358 | -1.844945 | -0.239866 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
4 | -1.285309 | 1.366350 | -1.844945 | -0.282611 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
75114 | 1.186936 | 1.165343 | -1.353089 | -0.026143 | -0.136210 | -0.953157 | -0.884914 | -0.075699 |
75115 | 0.784477 | 1.768365 | 0.286430 | -0.068888 | 34.407033 | 1.286476 | 0.971759 | -0.075699 |
75116 | 0.899466 | 1.567358 | -1.844945 | 0.059346 | 0.161577 | 0.264630 | 1.280085 | -0.075699 |
75117 | 0.956960 | 1.466854 | -1.025185 | -0.026143 | -0.136210 | 1.482417 | 0.809322 | -0.075699 |
75118 | 0.956960 | 1.617609 | -1.844945 | 0.016601 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
75119 rows × 8 columns
X_
HOURLYDRYBULBTEMPF | HOURLYRelativeHumidity | HOURLYWindSpeed | HOURLYSeaLevelPressure | HOURLYPrecip | HOURLYWindDirectionSin | HOURLYWindDirectionCos | HOURLYPressureTendencyCons | |
---|---|---|---|---|---|---|---|---|
0 | -1.285309 | 1.366350 | -1.844945 | -0.154377 | 0.161577 | 0.264630 | 1.280085 | -0.075699 |
1 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | 0.459363 | 0.264630 | 1.280085 | -0.075699 |
2 | -1.285309 | 1.567358 | -1.844945 | -0.154377 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
3 | -1.285309 | 1.567358 | -1.844945 | -0.239866 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
4 | -1.285309 | 1.366350 | -1.844945 | -0.282611 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
... | ... | ... | ... | ... | ... | ... | ... | ... |
75114 | 1.186936 | 1.165343 | -1.353089 | -0.026143 | -0.136210 | -0.953157 | -0.884914 | -0.075699 |
75115 | 0.784477 | 1.768365 | 0.286430 | -0.068888 | 34.407033 | 1.286476 | 0.971759 | -0.075699 |
75116 | 0.899466 | 1.567358 | -1.844945 | 0.059346 | 0.161577 | 0.264630 | 1.280085 | -0.075699 |
75117 | 0.956960 | 1.466854 | -1.025185 | -0.026143 | -0.136210 | 1.482417 | 0.809322 | -0.075699 |
75118 | 0.956960 | 1.617609 | -1.844945 | 0.016601 | -0.136210 | 0.264630 | 1.280085 | -0.075699 |
75119 rows × 8 columns
estimator = KerasRegressor(build_fn=baseline_model, epochs=50, batch_size=64, verbose=0)
kfold = KFold(n_splits=10)
results = cross_val_score(estimator, X_, y, cv=kfold, scoring='r2')
print("Baseline: %.2f (%.2f) R2 Score" % (results.mean(), results.std()))
Baseline: 0.43 (0.08) R2 Score