10088523 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) 8013599 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "most_frequent" 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 "gini" 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 4 8783 min_samples_split 5 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 30056 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 21097583 description https://api.openml.org/data/download/21097583/description.xml -1 21097584 predictions https://api.openml.org/data/download/21097584/predictions.arff area_under_roc_curve 0.7807112136994949 [0.862532,0.932943,0.873895,0.903902,0.873284,0.83868,0.841106,0.806581,0.746607,0.968415,0.744515,0.839528,0.715159,0.708787,0.965002,0.777107,0.873994,0.615984,0.707722,0.708846,0.775568,0.677754,0.768584,0.936415,0.869022,0.582307,0.645794,0.679431,0.869752,0.779376,0.810507,0.903863,0.776259,0.585938,0.807371,0.840909,0.741675,0.708728,0.839094,0.902403,0.681443,0.840002,0.841422,0.774858,0.682923,0.839863,0.84227,0.871271,0.774148,0.80885,0.773497,0.745324,0.74489,0.647234,0.776476,0.645084,0.999605,0.839863,0.680003,0.61334,0.872001,0.873205,0.745522,0.775765,0.84229,0.586628,0.64753,0.871942,0.647096,0.644709,0.613617,0.842113,0.715692,0.839765,0.740767,0.776949,0.904691,0.677774,0.903981,0.745975,0.676649,0.872455,0.743371,0.80524,0.808239,0.742523,0.873185,0.775331,0.869772,0.777876,0.90406,0.872652,0.743963,0.806483,0.776397,0.682706,0.682331,0.744614,0.640783,0.776772] average_cost 0 f_measure 0.45967126046377343 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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 0.4801384579482936 [0.244444,0.387097,0.916667,0.631579,0.666667,0.375,0.611111,0.4,0.466667,0.9375,0.555556,0.583333,0.4375,0.294118,0.5,0.533333,0.785714,0.0625,0.2,0.15,0.35,0.285714,0.16,0.823529,0.4,0.166667,0.090909,0.222222,0.533333,0.5625,0.692308,0.631579,0.380952,0.090909,0.391304,0.666667,0.238095,0.230769,0.583333,0.818182,0.416667,0.666667,0.666667,0.533333,0.142857,0.5,0.733333,0.6,0.307692,0.434783,0.35,0.411765,0.75,0.4,0.384615,0.142857,0.941176,0.6,0.238095,0.083333,0.625,0.75,0.466667,0.5,0.6875,0.142857,0.272727,0.615385,0.333333,0.117647,0.3,0.6875,0.666667,0.692308,0.263158,0.666667,0.705882,0.272727,0.705882,0.777778,0.181818,0.7,0.411765,0.272727,0.6,0.4,0.75,0.6,0.727273,0.615385,0.65,0.769231,0.538462,0.428571,0.428571,0.6,0.666667,0.5,0.117647,0.444444] predictive_accuracy 0.45875 prior_entropy 6.6438561897747395 recall 0.45875 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[0.984277,0.996835,1,0.996835,1,0.993711,0.493671,1,0.75,1,0.493711,0.993711,0.493671,0.738924,0.995253,1,1,0.496855,0.5,0.493711,1,0.740506,0.987421,0.75,0.998418,0.487421,0.493711,0.987342,0.996835,1,0.745253,0.996835,0.496855,0.493711,0.995253,0.995253,0.490506,0.493671,0.990506,0.496855,0.487421,0.5,0.493671,0.738924,0.743671,0.746835,1,1,0.490566,0.993711,0.745253,0.748418,0.745253,0.745253,0.496835,0.487421,1,0.490566,0.75,0.735759,0.75,0.75,0.743671,0.745253,0.493711,0.493711,0.490506,1,0.490506,0.962264,1,0.496855,0.5,0.748418,0.490506,0.496835,0.996855,0.487421,1,0.746835,0.990506,1,1,0.493711,1,0.998418,0.996835,0.746835,0.490566,1,0.996835,0.992089,1,0.745253,0.493711,0.740506,0.493711,0.748418,0.716772,1] area_under_roc_curve 0.7844843414138998 [0.490566,0.745253,0.493711,0.746835,1,0.996855,0.996835,0.984277,0.998418,1,1,1,0.740506,0.740506,1,0.493711,1,0.487421,0.732595,0.493711,0.493671,0.468354,0.987421,1,0.493671,0.984277,0.490566,0.477848,0.984177,1,0.75,1,1,0.496855,0.995253,1,0.743671,0.738924,0.493671,0.977987,0.496855,0.742089,0.748418,0.746835,0.748418,0.745253,1,0.746835,0.490566,0.490566,0.748418,0.748418,0.748418,0.742089,0.748418,0.487421,1,0.493711,0.742089,0.743671,1,1,0.496835,0.746835,0.993711,0.987421,0.743671,0.990566,0.732595,0.493711,0.996855,1,0.493671,0.743671,0.981013,0.745253,0.996855,0.987421,0.996855,0.743671,0.743671,0.496855,0.996855,0.493711,0.993671,1,0.493671,0.745253,1,0.745253,1,1,0.973101,0.743671,0.474843,0.993671,0.493711,0.748418,0.493671,1] 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 0.4633838383838384 kappa 0.4569635739374088 kappa 0.3936762325820077 kappa 0.41882501579279846 kappa 0.4441154407990841 kappa 0.4633203109585257 kappa 0.4819553028508253 kappa 0.4945107021562278 kappa 0.4694667035092566 kappa 0.4442032132001737 kb_relative_information_score 83.9609949587926 kb_relative_information_score 84.01331014536696 kb_relative_information_score 77.12057208937577 kb_relative_information_score 79.6543353655852 kb_relative_information_score 81.42772579769974 kb_relative_information_score 85.75537161488032 kb_relative_information_score 87.80780226673598 kb_relative_information_score 83.4643871884807 kb_relative_information_score 82.02496884697877 kb_relative_information_score 82.34426539392125 mean_absolute_error 0.01110446428571429 mean_absolute_error 0.011317261904761901 mean_absolute_error 0.012005059523809524 mean_absolute_error 0.012017261904761913 mean_absolute_error 0.011934523809523812 mean_absolute_error 0.011303869047619047 mean_absolute_error 0.010765178571428573 mean_absolute_error 0.010876785714285719 mean_absolute_error 0.011236607142857147 mean_absolute_error 0.01163779761904762 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 0.46875 predictive_accuracy 0.4625 predictive_accuracy 0.4 predictive_accuracy 0.425 predictive_accuracy 0.45 predictive_accuracy 0.46875 predictive_accuracy 0.4875 predictive_accuracy 0.5 predictive_accuracy 0.475 predictive_accuracy 0.45 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 0.46875 [1,1,1,1,0,1,0.5,1,0,1,0,0,0.5,0,0.5,0,0,0,0.5,0,0,0,1,1,0,0,0,1,0.5,0,0.5,0.5,0.5,0,0,0,0,0,1,0.5,1,1,0.5,0.5,0,1,1,1,1,0.5,0,0.5,0,0,1,1,1,1,0,0,1,1,0.5,0.5,1,0,0.5,1,0,0,0,1,0,1,0,1,0.5,0,1,1,0.5,1,1,1,0,0.5,0,0.5,0.5,0,1,1,0.5,0.5,0.5,0,1,1,0,1] recall 0.4625 [1,1,0.5,1,1,0,1,0,0.5,1,0.5,0.5,0.5,0,0.5,1,0.5,0,0.5,0,1,0,0,1,1,0,0,0,1,0,1,1,0.5,0,1,1,0,0,0,1,1,1,1,0,0,1,1,1,0,1,1,0.5,1,0.5,0,0,1,1,1,0,1,0,0.5,0.5,0,0,0,1,0,0,0,1,0,0.5,0,0.5,0,0,1,0,0,0,0,1,0.5,0.5,1,0,0.5,0,0.5,1,0.5,0.5,1,0,0,0,0.5,0] recall 0.4 [0.5,0.5,1,1,1,0.5,0,0.5,0.5,1,0,0.5,1,0.5,1,0.5,1,0,0,0,0,0,0,1,0,0,0,0,0.5,1,1,1,0,0,1,0,1,0,1,0,0.5,0,1,1,0,0,0.5,1,0,0.5,0.5,0,0,0,0,0,1,1,0,0,0,1,0.5,0,1,0,0.5,0.5,0,0,0,1,1,0,0,0.5,1,0,0,0.5,0,0.5,0,0,1,0,1,0.5,0.5,1,0.5,1,0,0,0.5,1,0,0.5,0,0] recall 0.425 [1,1,1,1,0.5,0.5,1,1,0.5,1,0,0,0,0,1,0.5,0.5,0,0,0,1,0,0.5,0,0,0,0,0,0.5,1,0,0.5,1,0,1,0,1,0,0,1,0.5,0,0,1,0,0,0.5,1,0.5,0,0,1,0,0,0,0,1,0.5,1,0,1,0,0.5,0,0.5,0,0,0.5,0,0,0,0.5,0,1,0,0.5,1,0.5,1,0.5,0,0,0.5,0,1,0,1,0.5,1,1,1,1,0,0.5,0.5,0,0,0.5,1,0] recall 0.45 [0.5,0,0.5,0.5,1,0,0,0,0,1,0.5,0,0,0,1,1,0.5,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0.5,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,0,0.5,0,1,0,0.5,0,0.5,1,0,1,0.5,0.5,0,0,0,0,0.5,0.5,1,0,0.5,0,0.5,0.5,0.5,1,0,1,0.5,0.5,0,0,1,1,0.5,0.5,1,0,0,1,0,0,0.5,1,0,0.5] recall 0.46875 [0.5,1,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,1,0,0.5,1,0,0,0,0.5,0.5,0.5,1,0.5,0.5,0,0,1,1,1,1,0.5,0.5,0,0.5,0,0,0,0,0,1,0,1,0,1,0.5,0.5,1,0.5,1,1,0,0,0,0.5,1,0.5,0,1,1,0.5,1,0,1,0,0,1,0.5,0,0.5,0,1,1,0.5,0,1,0,1,0,0,0.5,0,1,0,1,1,1,0,0,1,0,0,1,0,1,0.5,0,0,0.5] recall 0.4875 [1,0,0,0.5,1,1,1,0.5,1,1,1,1,1,0,1,0,1,0,0.5,0,0.5,0,0,1,0,0,0,0,0,0,0.5,0,1,0,0.5,0.5,0.5,1,1,1,0,0,1,0.5,0,0.5,0,0.5,1,1,0,0,1,0,0.5,0,1,0.5,0,0,0,1,1,0,1,0,0,0.5,0.5,0.5,0,1,1,1,0.5,0,1,0,1,0,0.5,0,1,0.5,1,0,1,0,0,0.5,1,1,0,0,0,0,1,1,0,1] recall 0.5 [1,1,1,0.5,0,0.5,1,0.5,0,1,0,1,0.5,1,0,0.5,1,0,0,1,0,0,0,1,1,0,1,0.5,0,0.5,0.5,1,1,1,0.5,0.5,0,0,0.5,1,0,1,1,0.5,0,0.5,1,0.5,0,1,1,0.5,0,1,0.5,0,1,1,0,0,0.5,1,0,1,0.5,0,0,0,0.5,0.5,0,1,0.5,1,0.5,1,1,1,0,1,0,0,0,0,0,0,1,0,1,1,0,0.5,1,0,1,0.5,0.5,0,0,0.5] recall 0.475 [0,1,1,1,1,1,0,1,0.5,1,0,0,0,0.5,0.5,1,1,0,0,0,1,0.5,0,0.5,1,0,0,0.5,1,1,0.5,1,0,0,1,1,0,0,0.5,0,0,0,0,0.5,0,0.5,1,1,0,1,0.5,0.5,0.5,0.5,0,0,1,0,0.5,0,0.5,0.5,0.5,0.5,0,0,0,1,0,0,1,0,0,0.5,0,0,1,0,1,0.5,0.5,1,1,0,1,1,1,0,0,1,1,0.5,1,0.5,0,0.5,0,0.5,0,1] recall 0.45 [0,0.5,0,0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,1,0,0.5,0,0,0,0,1,0,0,0,0,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,0,0,0,0,0.5,0.5,0.5,0.5,1,0.5,0,0,0.5,0.5,0.5,0.5,0.5,0,1,0,0.5,0,1,1,0,0.5,1,1,0.5,0,0.5,0,0,1,0,0,0.5,0,1,0,1,0,0.5,0,1,0,1,0.5,0,0.5,1,0,1,1,0.5,0,0,1,0,0.5,0,1] relative_absolute_error 0.5608315295815288 relative_absolute_error 0.571578884078883 relative_absolute_error 0.6063161375661366 relative_absolute_error 0.6069324194324188 relative_absolute_error 0.6027537277537269 relative_absolute_error 0.5709024771524761 relative_absolute_error 0.5436958874458866 relative_absolute_error 0.5493326118326112 relative_absolute_error 0.5675054112554104 relative_absolute_error 0.5877675565175556 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 0.09127006426818858 root_mean_squared_error 0.09246657406108494 root_mean_squared_error 0.09619682985263156 root_mean_squared_error 0.0933602708029443 root_mean_squared_error 0.09535148384016653 root_mean_squared_error 0.09163914243544542 root_mean_squared_error 0.09040297005886949 root_mean_squared_error 0.09057716796195978 root_mean_squared_error 0.09308489316533004 root_mean_squared_error 0.09407615977424079 root_relative_squared_error 0.9172986599066806 root_relative_squared_error 0.9293240357885688 root_relative_squared_error 0.966814517099509 root_relative_squared_error 0.938306025997995 root_relative_squared_error 0.9583184700044497 root_relative_squared_error 0.9210080350554777 root_relative_squared_error 0.9085840352091009 root_relative_squared_error 0.910334790008547 root_relative_squared_error 0.935538376605204 root_relative_squared_error 0.9455009808747948 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 159.6730450000905 usercpu_time_millis 143.64908700008527 usercpu_time_millis 147.50107999998363 usercpu_time_millis 107.82221500016931 usercpu_time_millis 107.35240199983309 usercpu_time_millis 106.11552199975449 usercpu_time_millis 106.0374060000413 usercpu_time_millis 105.81872600027964 usercpu_time_millis 103.44104299997525 usercpu_time_millis 104.31516099993132 usercpu_time_millis_testing 2.0477500002016313 usercpu_time_millis_testing 1.992075000089244 usercpu_time_millis_testing 1.922058999980436 usercpu_time_millis_testing 1.5899800000624964 usercpu_time_millis_testing 1.4805569999225554 usercpu_time_millis_testing 1.5135249998365907 usercpu_time_millis_testing 1.569305999964854 usercpu_time_millis_testing 1.493882000204394 usercpu_time_millis_testing 1.47055200000068 usercpu_time_millis_testing 1.4357069999277883 usercpu_time_millis_training 157.62529499988887 usercpu_time_millis_training 141.65701199999603 usercpu_time_millis_training 145.5790210000032 usercpu_time_millis_training 106.23223500010681 usercpu_time_millis_training 105.87184499991054 usercpu_time_millis_training 104.6019969999179 usercpu_time_millis_training 104.46810000007645 usercpu_time_millis_training 104.32484400007525 usercpu_time_millis_training 101.97049099997457 usercpu_time_millis_training 102.87945400000353