10177040 1 Jan van Rijn 9954 Supervised Classification 9666 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,gradientboostingclassifier=sklearn.ensemble.gradient_boosting.GradientBoostingClassifier)(4) 8102502 copy true 9559 fill_value -1 9559 missing_values NaN 9559 strategy "constant" 9559 verbose 0 9559 n_jobs null 9606 remainder "passthrough" 9606 sparse_threshold 0.3 9606 transformer_weights null 9606 transformers [{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": []}}] 9606 memory null 9607 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "standardscaler", "step_name": "standardscaler"}}] 9607 axis 0 9608 copy true 9608 missing_values "NaN" 9608 strategy "most_frequent" 9608 verbose 0 9608 copy true 9609 with_mean true 9609 with_std true 9609 memory null 9610 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder"}}] 9610 categorical_features null 9611 categories null 9611 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 9611 handle_unknown "ignore" 9611 n_values null 9611 sparse true 9611 threshold 0.0 9612 memory null 9666 steps [{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "gradientboostingclassifier", "step_name": "gradientboostingclassifier"}}] 9666 criterion "friedman_mse" 9667 init null 9667 learning_rate 0.00017365035459504464 9667 loss "deviance" 9667 max_depth 23 9667 max_features 0.66656553661313 9667 max_leaf_nodes null 9667 min_impurity_decrease 0.08871299378588704 9667 min_impurity_split null 9667 min_samples_leaf 2 9667 min_samples_split 8 9667 min_weight_fraction_leaf 0.46119113448843 9667 n_estimators 180 9667 n_iter_no_change 402 9667 presort "auto" 9667 random_state 42587 9667 subsample 0.29509052907389055 9667 tol 4.394493626499898e-05 9667 validation_fraction 0.3154728749776492 9667 verbose 0 9667 warm_start false 9667 openml-python Sklearn_0.20.3. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 21274996 description https://api.openml.org/data/download/21274996/description.xml -1 21274997 predictions https://api.openml.org/data/download/21274997/predictions.arff area_under_roc_curve 0.5876629577020204 [0.854522,0.30157,0.418008,0.805161,0.651397,0.775963,0.536182,0.895163,0.668008,0.71733,0.181187,0.389599,0.813526,0.47242,0.250908,0.64173,0.76093,0.317274,0.799953,0.683002,0.884509,0.203086,0.582741,0.889836,0.679451,0.893624,0.51527,0.75,0.406171,0.277304,0.873224,0.404356,0.839923,0.278804,0.107521,0.663984,0.590712,0.547072,0.631905,0.21366,0.070747,0.50146,0.644926,0.32047,0.602865,0.608744,0.277304,0.404277,0.647214,0.586253,0.780224,0.787287,0.480587,0.102943,0.500552,0.341698,0.8314,0.689433,0.320155,0.704782,0.420691,0.54652,0.327217,0.42444,0.403093,0.583491,0.53342,0.372396,0.628433,0.498935,0.394965,0.510496,0.697956,0.276081,0.666588,0.60101,0.525529,0.735519,0.443537,0.931937,0.90842,0.984414,0.901357,0.980232,0.915404,0.912918,0.868371,0.868292,0.993016,0.62425,0.203086,0.641296,0.525647,0.265704,0.553385,0.856376,0.879774,0.879064,0.210661,0.902068] average_cost 0 kappa -0.003787878787878788 kb_relative_information_score 1.4984793051042171 mean_absolute_error 0.019799712508792557 mean_prior_absolute_error 0.019800000000000036 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] predictive_accuracy 0.00625 prior_entropy 6.6438561897747395 recall 0.00625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.375,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0.0625,0,0.0625] relative_absolute_error 0.9999854802420465 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.09949734000554365 root_relative_squared_error 0.9999858922327449 total_cost 0 area_under_roc_curve 0.6060783377119656 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[0.943396,0.231013,0.534591,0.794304,0.718354,0.993711,0.677215,0.893082,0.727848,0.867925,0.081761,0.062893,0.968354,0.446203,0.256329,0.685535,0.754717,0.176101,0.933544,0.886792,0.89557,0.136076,0.559748,0.898734,0.712025,0.981132,0.333333,0.667722,0.449367,0.056604,0.851266,0.300633,0.899371,0.226415,0.085443,0.699367,0.734177,0.775316,0.68038,0.163522,0.138365,0.43038,0.53481,0.310127,0.541139,0.408228,0.496855,0.300633,0.805031,0.823899,0.848101,0.727848,0.642405,0.161392,0.677215,0.540881,0.775316,0.660377,0.360759,0.882911,0.379747,0.737342,0.411392,0.272152,0.069182,0.968553,0.610759,0.08805,0.724684,0.584906,0.540881,0.440252,0.870253,0.281646,0.620253,0.977848,0.540881,0.949686,0.408805,1,0.971519,0.993711,1,1,0.939873,0.996835,1,0.829114,0.974843,0.658228,0.196203,0.787975,0.743671,0.158228,0.899371,0.797468,0.90566,0.781646,0.177215,0.918239] area_under_roc_curve 0.5806317669771516 [0.836478,0.25,0.339623,0.753165,0.800633,0.710692,0.316456,0.993711,0.582278,0.72956,0.289308,0.100629,0.844937,0.458861,0.306962,0.735849,0.716981,0.301887,0.860759,0.918239,0.943038,0.101266,0.320755,0.822785,0.75,0.943396,0.345912,0.651899,0.325949,0.132075,0.905063,0.449367,0.792453,0.333333,0.123418,0.566456,0.585443,0.677215,0.677215,0.044025,0.025157,0.759494,0.575949,0.120253,0.518987,0.56962,0.490566,0.487342,0.597484,0.773585,0.838608,0.689873,0.556962,0.082278,0.348101,0.358491,0.806962,0.72956,0.06962,0.920886,0.439873,0.724684,0.262658,0.484177,0.27044,0.691824,0.471519,0.301887,0.841772,0.396226,0.811321,0.169811,0.705696,0.186709,0.509494,0.759494,0.559748,0.314465,0.559748,0.984177,0.984177,0.974843,1,0.987421,0.981013,0.996835,0.993671,0.974684,0.987421,0.424051,0.082278,0.39557,0.341772,0.417722,0.352201,0.933544,0.874214,0.873418,0.268987,0.81761] 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 -0.005493853997865698 kappa -0.005136309758988542 kappa 0.007585335018963337 kappa -0.01149788612746454 kappa 0.00797725298159703 kappa 0.0011063695274221598 kappa -0.008946517952153865 kappa -0.009026053368018604 kappa -0.008390120928034032 kappa -0.008429843220672811 kb_relative_information_score 0.14777579676870084 kb_relative_information_score 0.1418946430997584 kb_relative_information_score 0.13994295586914668 kb_relative_information_score 0.13725634378655466 kb_relative_information_score 0.14388604571429517 kb_relative_information_score 0.1470986632451423 kb_relative_information_score 0.1610027864591011 kb_relative_information_score 0.16772168233866114 kb_relative_information_score 0.15628513742226766 kb_relative_information_score 0.15561525040058757 mean_absolute_error 0.01979972292337725 mean_absolute_error 0.019799759322745136 mean_absolute_error 0.01979980145602158 mean_absolute_error 0.019799819834183198 mean_absolute_error 0.0197997767173139 mean_absolute_error 0.0197997824639288 mean_absolute_error 0.01979959960975215 mean_absolute_error 0.019799568292435274 mean_absolute_error 0.019799622567321654 mean_absolute_error 0.019799671900846673 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 0.00625 predictive_accuracy 0.00625 predictive_accuracy 0.01875 predictive_accuracy 0 predictive_accuracy 0.01875 predictive_accuracy 0.0125 predictive_accuracy 0 predictive_accuracy 0 predictive_accuracy 0 predictive_accuracy 0 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 0.00625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.00625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.01875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5] recall 0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.01875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0] recall 0.0125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] relative_absolute_error 0.9999860062311724 relative_absolute_error 0.9999878445830861 relative_absolute_error 0.9999899725263407 relative_absolute_error 0.9999909007163216 relative_absolute_error 0.99998872309666 relative_absolute_error 0.9999890133297356 relative_absolute_error 0.9999797782703089 relative_absolute_error 0.9999781965876384 relative_absolute_error 0.9999809377435163 relative_absolute_error 0.999983429335689 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 0.09949739369026118 root_mean_squared_error 0.0994975759089021 root_mean_squared_error 0.0994977859753486 root_mean_squared_error 0.09949787812550273 root_mean_squared_error 0.09949766141114752 root_mean_squared_error 0.09949769063217119 root_mean_squared_error 0.09949677248827411 root_mean_squared_error 0.09949661653161383 root_mean_squared_error 0.0994968887794427 root_mean_squared_error 0.09949713650340654 root_relative_squared_error 0.9999864317844576 root_relative_squared_error 0.9999882631507052 root_relative_squared_error 0.9999903743979296 root_relative_squared_error 0.9999913005418254 root_relative_squared_error 0.9999891224806045 root_relative_squared_error 0.9999894161629423 root_relative_squared_error 0.9999801884695784 root_relative_squared_error 0.9999786210461672 root_relative_squared_error 0.9999813572397984 root_relative_squared_error 0.9999838469593123 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 24563.442663999012 usercpu_time_millis 24606.23922099967 usercpu_time_millis 24575.141154999983 usercpu_time_millis 24706.216780999966 usercpu_time_millis 24613.69358399952 usercpu_time_millis 24763.39904700035 usercpu_time_millis 24634.402401999978 usercpu_time_millis 24676.33879799996 usercpu_time_millis 24686.628067999663 usercpu_time_millis 24737.075993999497 usercpu_time_millis_testing 17.211479999787116 usercpu_time_millis_testing 12.89981700028875 usercpu_time_millis_testing 13.307060999977693 usercpu_time_millis_testing 14.596125000025495 usercpu_time_millis_testing 13.331073999324872 usercpu_time_millis_testing 16.0246619998361 usercpu_time_millis_testing 13.754931999756081 usercpu_time_millis_testing 12.86258199979784 usercpu_time_millis_testing 15.598033999594918 usercpu_time_millis_testing 13.096550999762258 usercpu_time_millis_training 24546.231183999225 usercpu_time_millis_training 24593.33940399938 usercpu_time_millis_training 24561.834094000005 usercpu_time_millis_training 24691.62065599994 usercpu_time_millis_training 24600.362510000195 usercpu_time_millis_training 24747.374385000512 usercpu_time_millis_training 24620.64747000022 usercpu_time_millis_training 24663.476216000163 usercpu_time_millis_training 24671.03003400007 usercpu_time_millis_training 24723.979442999735