10104409 1 Jan van Rijn 9954 Supervised Classification 8815 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1) 8029621 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "mean" 8778 verbose 0 8778 copy true 8779 with_mean true 8779 with_std true 8779 memory null 8780 copy true 8781 fill_value -1 8781 missing_values NaN 8781 strategy "constant" 8781 verbose 0 8781 categorical_features null 8782 categories null 8782 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 8782 handle_unknown "ignore" 8782 n_values null 8782 sparse true 8782 class_weight null 8783 criterion "entropy" 8783 max_depth null 8783 max_features 1.0 8783 max_leaf_nodes null 8783 min_impurity_decrease 0.0 8783 min_impurity_split null 8783 min_samples_leaf 1 8783 min_samples_split 6 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 45406 8783 splitter "best" 8783 n_jobs null 8812 remainder "passthrough" 8812 sparse_threshold 0.3 8812 transformer_weights null 8812 memory null 8813 memory null 8815 threshold 0.0 8816 openml-python Sklearn_0.20.0. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 21129356 description https://api.openml.org/data/download/21129356/description.xml -1 21129357 predictions https://api.openml.org/data/download/21129357/predictions.arff area_under_roc_curve 0.7520132970328279 [0.746232,0.841166,0.874428,0.843316,0.841264,0.65337,0.747199,0.810626,0.653291,0.842862,0.810271,0.809935,0.714193,0.681601,0.8416,0.680674,0.905934,0.588995,0.651219,0.648615,0.747139,0.841797,0.684403,0.999684,0.747633,0.584931,0.587279,0.80962,0.842981,0.810922,0.747593,0.936395,0.746528,0.55451,0.840199,0.778468,0.713206,0.682607,0.905185,0.935527,0.683811,0.809304,0.8416,0.746922,0.780204,0.716106,0.874033,0.685409,0.651278,0.842369,0.682331,0.681739,0.808594,0.645163,0.745896,0.554253,0.999684,0.935054,0.651259,0.744279,0.748895,0.71437,0.68387,0.622435,0.810764,0.747001,0.587812,0.842704,0.587121,0.589153,0.682232,0.841501,0.714469,0.841481,0.488952,0.873422,0.841876,0.613262,0.812263,0.779297,0.778212,0.620423,0.713739,0.683791,0.805674,0.811316,0.810567,0.743746,0.715495,0.778153,0.810211,0.967113,0.777423,0.71293,0.779908,0.716757,0.776495,0.682173,0.584734,0.746903] average_cost 0 f_measure 0.47104398987643087 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number_of_instances 1600 [16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16] precision 0.48319930593254745 [0.470588,0.478261,0.8,0.733333,0.52381,0.384615,0.533333,0.6,0.333333,0.846154,0.6,0.6,0.294118,0.272727,0.611111,0.210526,0.928571,0.176471,0.277778,0.153846,0.470588,0.611111,0.4,0.941176,0.466667,0.130435,0.111111,0.5,0.818182,0.666667,0.466667,0.7,0.571429,0.066667,0.52381,0.533333,0.411765,0.272727,0.722222,0.705882,0.6,0.434783,0.478261,0.444444,0.692308,0.388889,0.733333,0.5,0.3125,0.666667,0.263158,0.3,0.444444,0.125,0.470588,0.125,0.941176,0.619048,0.263158,0.318182,0.727273,0.375,0.375,0.5,0.666667,0.5,0.214286,0.785714,0.272727,0.375,0.375,0.6,0.4,0.666667,0,0.692308,0.666667,0.0625,1,0.642857,0.5625,0.222222,0.4375,0.333333,0.4,0.9,0.666667,0.368421,0.461538,0.428571,0.6,0.714286,0.583333,0.272727,0.7,0.545455,0.411765,0.352941,0,0.421053] predictive_accuracy 0.47125 prior_entropy 6.6438561897747395 recall 0.47125 [0.5,0.6875,0.75,0.6875,0.6875,0.3125,0.5,0.5625,0.3125,0.6875,0.5625,0.5625,0.3125,0.375,0.6875,0.25,0.8125,0.1875,0.3125,0.25,0.5,0.6875,0.375,1,0.4375,0.1875,0.125,0.625,0.5625,0.625,0.4375,0.875,0.5,0.0625,0.6875,0.5,0.4375,0.1875,0.8125,0.75,0.375,0.625,0.6875,0.5,0.5625,0.4375,0.6875,0.375,0.3125,0.625,0.3125,0.375,0.5,0.1875,0.5,0.125,1,0.8125,0.3125,0.4375,0.5,0.375,0.375,0.1875,0.625,0.5,0.1875,0.6875,0.1875,0.1875,0.375,0.5625,0.375,0.625,0,0.5625,0.625,0.0625,0.625,0.5625,0.5625,0.25,0.4375,0.25,0.5,0.5625,0.625,0.4375,0.375,0.5625,0.5625,0.9375,0.4375,0.1875,0.4375,0.375,0.4375,0.375,0,0.5] relative_absolute_error 0.5383838383838294 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.09641933156789631 root_relative_squared_error 0.9690507434775201 total_cost 0 area_under_roc_curve 0.7532100847862433 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[0.493711,0.496835,0.496855,0.75,1,0.493711,0.745253,0.493711,0.487342,1,0.996855,0.5,0.496835,0.746835,0.746835,1,1,0.5,0.493671,1,1,0.996835,0.5,1,1,0.484277,0.987421,0.746835,0.5,0.996855,0.5,0.75,1,0.481132,0.746835,0.748418,0.487342,0.487342,1,1,0.993711,0.748418,0.75,0.746835,1,0.5,1,0.5,1,0.496855,0.5,0.748418,0.996835,0.490506,0.748418,0.487421,1,0.996855,0.496835,0.743671,1,0.746835,0.490506,0.75,0.996855,0.5,0.742089,0.996855,0.996835,0.5,0.490566,0.496855,0.496835,0.996835,0.487342,0.75,1,0.493711,0.5,0.993671,0.996835,0.496855,1,0.496855,0.988924,1,0.996835,0.984177,0.487421,0.996835,1,0.996835,0.996835,0.738924,1,0.75,0.496855,0.746835,0.734177,0.996855] average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 average_cost 0 kappa 0.49486977111286506 kappa 0.40008682953783004 kappa 0.4063782759709504 kappa 0.48845470692717585 kappa 0.4253236501420903 kappa 0.4314592545799116 kappa 0.5073230429118472 kappa 0.4633414884381659 kappa 0.5452033162258193 kappa 0.49463044851547694 kb_relative_information_score 80.55162133892087 kb_relative_information_score 71.11064584741534 kb_relative_information_score 70.89547907857798 kb_relative_information_score 84.24080701418383 kb_relative_information_score 72.79778389265249 kb_relative_information_score 74.93466486925114 kb_relative_information_score 84.75482137598405 kb_relative_information_score 80.45174375029414 kb_relative_information_score 89.62705035075706 kb_relative_information_score 82.56973044644809 mean_absolute_error 0.01021458333333334 mean_absolute_error 0.011702083333333339 mean_absolute_error 0.011589583333333342 mean_absolute_error 0.010275000000000003 mean_absolute_error 0.011325000000000005 mean_absolute_error 0.011279166666666673 mean_absolute_error 0.01018541666666667 mean_absolute_error 0.010489583333333339 mean_absolute_error 0.009312500000000005 mean_absolute_error 0.010227083333333338 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 mean_prior_absolute_error 0.019800000000000033 number_of_instances 160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1] number_of_instances 160 [1,2,2,1,2,1,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,1,1,1,2,2,2,2,1,2,2,2,2,2,1,1,1,1,2,1,1,2,2,2,2,1,1,2,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,1,2,2,1,1,1,2,1,2,2,1,2,2,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,1,2,2,1] number_of_instances 160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2] number_of_instances 160 [2,2,2,1,2,2,1,2,2,2,2,2,1,2,2,2,2,2,1,2,1,1,2,1,1,2,2,1,2,2,1,2,2,2,1,1,1,1,1,2,2,1,1,1,2,1,2,1,2,2,2,1,1,2,1,2,1,2,1,2,1,1,2,1,2,2,2,2,1,2,2,2,1,2,1,2,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,1,1,2,2,1,2,2,1,2] number_of_instances 160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2] number_of_instances 160 [2,1,2,2,1,2,1,2,1,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,2,1,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,1,1,1,2,2,2,2,1,1,2,1,2,2,1,2,2,1,2,2,1,1,2,2,1,2,2,2,2,2,1,1,2,1,2,2,2,1,1,2,2,2,1,1,1,1,2,2,1,2,1,1,2,1,2,1,1,2] number_of_instances 160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2] number_of_instances 160 [2,1,1,2,1,2,2,2,1,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,2,1,1,2,1,2,2,1,1,1,2,2,2,2,2,1,2,2,2,2,1,2,2,2,1,1,1,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,2,1,1,1,1,1,2,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,1,2,2] number_of_instances 160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1] number_of_instances 160 [1,2,1,2,2,1,2,1,2,1,1,1,2,2,2,1,1,1,2,1,2,2,1,2,2,1,1,2,2,1,2,2,1,1,2,2,2,2,2,1,1,2,2,2,2,2,1,2,1,1,2,2,2,2,2,1,2,1,2,2,2,2,2,2,1,1,2,1,2,1,1,1,2,2,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,2,2,2,2,1,2,1,2,2,1] predictive_accuracy 0.5 predictive_accuracy 0.40625 predictive_accuracy 0.4125 predictive_accuracy 0.49375 predictive_accuracy 0.43125 predictive_accuracy 0.4375 predictive_accuracy 0.5125 predictive_accuracy 0.46875 predictive_accuracy 0.55 predictive_accuracy 0.5 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 prior_entropy 6.6438561897747395 recall 0.5 [1,0.5,1,0,0.5,1,0,1,0.5,1,0.5,0.5,0,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0.5,0,1,1,0.5,0,1,0,0,0,1,1,0,0,0.5,1,1,0.5,1,1,0,0.5,0.5,0,1,0,1,0,1,1,1,0.5,0,1,0.5,0,1,0.5,0,1,0,0,0.5,1,0.5,0.5,0,1,0.5,0.5,0.5,0.5,1,0,1,1,0.5,0.5,1,0.5,0.5,0,0.5,1,1,0.5,0.5,0,1,0,0,1] recall 0.40625 [1,1,0.5,1,0.5,0,0.5,1,0.5,1,0,0.5,0,0.5,1,1,1,0,0,1,1,0,0,1,0,0.5,0,0,0.5,0,0,1,0.5,0,0.5,1,0,0,1,1,0,1,1,0.5,0.5,0.5,1,0,0,1,0,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0,1,0,0,0,0,0,0.5,1,0.5,0.5,0,0.5,0,0,0.5,0.5,0.5,0,0,0,0.5,0,0,0,0,0,0,1,0.5,0,0.5,0.5,1,0,0,0] recall 0.4125 [0,0.5,0.5,1,0.5,0,0,1,0.5,0.5,0.5,0.5,1,0,0,0,1,0,0,0,0,1,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0,0,1,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,0,1,0,0,0,0.5,1,0.5,1,0,0,0,0,1,0.5,0.5,0,0.5,0,0,0.5,0.5,0,0.5,0,0,1,0,0.5,1,0,0,0.5,1,0,0,1,0.5,0.5,1,0.5,1,0,0,0,1,0,0.5,0,0.5] recall 0.49375 [0.5,1,1,1,0.5,0,1,0,0,0.5,0,0.5,0,1,1,0.5,0.5,0.5,0,0,0,1,0,1,0,0.5,0.5,1,0.5,0.5,1,1,0.5,0,1,0,1,0,0,1,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,0,0,1,0,1,1,0,1,1,0,0.5,0,0.5,1,0.5,1,0,0.5,0,0,0,0.5,0,0.5,0.5,0,1,0.5,0,0,1,0,1,0,1,1,0,1,0.5,1,0,0,1,0,0,0.5,0,0] recall 0.43125 [0.5,1,0.5,0.5,0,0.5,0,0,0,0.5,1,1,1,0,1,0,0.5,0.5,1,1,0,0.5,0,1,1,0,0,1,1,0.5,0,1,0,0,1,1,0.5,0,0.5,1,0.5,0.5,0,1,1,0,1,0,0.5,0.5,0,0,0.5,0,0,0,1,0.5,0.5,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0.5,0.5,1,0,0,1,0.5,0,0.5,0,0,1,0,0,0,1,0,1,1,0.5,0,1,0,1,0.5,0,0.5,1,0,0] recall 0.4375 [0.5,1,1,1,1,0,1,0.5,1,0.5,1,0.5,0,1,1,0.5,1,0.5,0,0,0.5,0.5,0.5,1,0.5,0,0,1,1,0.5,1,1,0.5,0,0,0,0,0.5,1,0,0.5,0.5,1,1,0,1,1,0,0,0.5,1,1,0.5,0,0.5,0,1,1,0,1,0.5,0,0,0,0.5,0.5,0,1,0,0.5,0,0.5,0,1,0,0,0.5,0,0.5,0,0,0,0.5,0,0,1,0,0,0,1,1,1,0,0,0,0,0.5,0,0,0.5] recall 0.5125 [0.5,0,1,1,1,1,0.5,1,0,0.5,1,1,0.5,0,0,0,0,0,0,0,0.5,1,1,1,0,0,0,0.5,0,1,0.5,0,1,0,1,0,1,0.5,0.5,0,0,1,0.5,0.5,0,0.5,0,0.5,1,1,0,0.5,0.5,0,0.5,0.5,1,0.5,0.5,0,1,0.5,1,0,0.5,1,0,1,0.5,0,1,1,0,1,0,1,1,0,1,1,1,1,0.5,0,1,0.5,0.5,0,1,0,1,1,0,0,0,0.5,1,0,0,1] recall 0.46875 [1,1,1,0,1,0,1,1,0,1,1,0,0,0,0,0,1,0,1,0,0,0.5,0.5,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,0,1,0.5,1,1,0,0,0,0,0,1,0.5,0,1,1,0,1,0,0.5,1,0.5,0.5,1,0,0.5,0,0,0,0.5,1,0,0,1,1,0,1,0,0,0,0,0.5,0,0.5,1,0,1,0.5,0,1,0,0,0,0.5,0.5,0,0,0.5] recall 0.55 [0,1,1,1,1,1,0.5,0,0.5,1,0,1,1,0.5,1,0,1,0,1,0,1,1,1,1,0.5,0,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0,1,0,0,0.5,0.5,0,0,0.5,1,0,0,1,0.5,0.5,0.5,0.5,0.5,0,1,1,0,0,0,0.5,0,0,1,1,0.5,0,0,1,1,1,0.5,1,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0.5,0.5,0,0.5,1,0.5,1,0,1,0.5,0,1,0,1] recall 0.5 [0,0,0,0.5,1,0,0.5,0,0,1,1,0,0,0.5,0.5,1,1,0,0,1,1,1,0,1,1,0,0,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0,1,1,1,0.5,0.5,0.5,1,0,0,0,1,0,0,0.5,1,0,0.5,0,1,1,0,0.5,1,0,0,0.5,1,0,0.5,1,1,0,0,0,0,1,0,0.5,1,0,0,1,1,0,1,0,1,1,1,0.5,0,1,1,1,1,0.5,1,0.5,0,0.5,0,1] relative_absolute_error 0.5158880471380466 relative_absolute_error 0.5910143097643091 relative_absolute_error 0.585332491582491 relative_absolute_error 0.5189393939393931 relative_absolute_error 0.5719696969696962 relative_absolute_error 0.5696548821548816 relative_absolute_error 0.5144149831649826 relative_absolute_error 0.5297769360269354 relative_absolute_error 0.47032828282828226 relative_absolute_error 0.5165193602693596 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_prior_squared_error 0.09949874371066206 root_mean_squared_error 0.09530597655271504 root_mean_squared_error 0.0997684819970716 root_mean_squared_error 0.10078442339965044 root_mean_squared_error 0.09267644582692577 root_mean_squared_error 0.1004090591862441 root_mean_squared_error 0.09900021043748679 root_mean_squared_error 0.09381590276233083 root_mean_squared_error 0.09718199564619869 root_mean_squared_error 0.09138707360574702 root_mean_squared_error 0.09329035468781208 root_relative_squared_error 0.9578611045568637 root_relative_squared_error 1.0027109717806482 root_relative_squared_error 1.0129215670574403 root_relative_squared_error 0.9314333263988215 root_relative_squared_error 1.0091490147677564 root_relative_squared_error 0.9949895520829389 root_relative_squared_error 0.9428852994882361 root_relative_squared_error 0.9767158058678573 root_relative_squared_error 0.9184746479965274 root_relative_squared_error 0.9376033426019558 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 total_cost 0 usercpu_time_millis 275.9136259992374 usercpu_time_millis 258.14370999978564 usercpu_time_millis 232.7463670008001 usercpu_time_millis 183.01114900077664 usercpu_time_millis 181.22923000009905 usercpu_time_millis 180.97862800004805 usercpu_time_millis 183.35171800026728 usercpu_time_millis 187.53217499943275 usercpu_time_millis 188.96506700002647 usercpu_time_millis 183.3388200002446 usercpu_time_millis_testing 1.8699789998208871 usercpu_time_millis_testing 1.8570440006442368 usercpu_time_millis_testing 1.3590970002042013 usercpu_time_millis_testing 1.350207000541559 usercpu_time_millis_testing 1.3433869999062154 usercpu_time_millis_testing 1.3437979996524518 usercpu_time_millis_testing 1.3402940003288677 usercpu_time_millis_testing 1.3422239999272279 usercpu_time_millis_testing 1.3564090004365426 usercpu_time_millis_testing 1.3453240007947898 usercpu_time_millis_training 274.0436469994165 usercpu_time_millis_training 256.2866659991414 usercpu_time_millis_training 231.3872700005959 usercpu_time_millis_training 181.66094200023508 usercpu_time_millis_training 179.88584300019284 usercpu_time_millis_training 179.6348300003956 usercpu_time_millis_training 182.01142399993842 usercpu_time_millis_training 186.18995099950553 usercpu_time_millis_training 187.60865799958992 usercpu_time_millis_training 181.9934959994498