10203421 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) 8128854 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 0.00690460154884467 9667 loss "deviance" 9667 max_depth 23 9667 max_features 0.8546125300240667 9667 max_leaf_nodes null 9667 min_impurity_decrease 0.5093012065579804 9667 min_impurity_split null 9667 min_samples_leaf 5 9667 min_samples_split 17 9667 min_weight_fraction_leaf 0.1891711099724881 9667 n_estimators 1046 9667 n_iter_no_change 1472 9667 presort "auto" 9667 random_state 61788 9667 subsample 0.6500951531562161 9667 tol 0.0004044933472138156 9667 validation_fraction 0.7392666181096995 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 21327758 description https://api.openml.org/data/download/21327758/description.xml -1 21327761 predictions https://api.openml.org/data/download/21327761/predictions.arff area_under_roc_curve 0.5415672348484849 [0.698153,0.220999,0.079348,0.631866,0.54289,0.143703,0.162287,0.186237,0.166035,0.711214,0.234848,0.16497,0.264994,0.540246,0.280224,0.25947,0.343474,0.557726,0.626381,0.517124,0.438368,0.365964,0.690499,0.977312,0.593277,0.710859,0.501223,0.763731,0.685527,0.732402,0.780264,0.719066,0.709833,0.602865,0.700087,0.658617,0.626065,0.603614,0.221078,0.685133,0.314078,0.845447,0.802675,0.439867,0.08795,0.626026,0.008523,0.518742,0.775331,0.903054,0.581834,0.427044,0.4811,0.542022,0.5378,0.622001,0.933752,0.746409,0.739583,0.777817,0.77545,0.844658,0.397057,0.706242,0.437697,0.798808,0.476444,0.606376,0.642677,0.782039,0.458688,0.593513,0.335425,0.708846,0.209833,0.857876,0.359138,0.591304,0.688289,0.536616,0.663786,0.472498,0.765152,0.780737,0.530224,0.628906,0.259391,0.539181,0.463423,0.045415,0.156723,0.176886,0.636837,0.159446,0.691682,0.392677,0.908854,0.87721,0.585503,0.806187] average_cost 0 kappa 0.016414141414141416 kb_relative_information_score 61.35118369919724 mean_absolute_error 0.01977380142852129 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 0.02625 prior_entropy 6.6438561897747395 recall 0.02625 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.6875,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0.0625,0,0,0,0,0,0,0,0,0.9375,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.3125,0,0,0.125] relative_absolute_error 0.9986768398243058 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.09961968354096497 root_relative_squared_error 1.0012154910282542 total_cost 0 area_under_roc_curve 0.5498367964334049 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[0.471698,0.079114,0.207547,0.924051,0.566456,0.062893,0.177215,0.056604,0.227848,0.798742,0.264151,0.012579,0.120253,0.493671,0.376582,0.264151,0.257862,0.459119,0.617089,0.559748,0.363924,0.262658,0.867925,0.939873,0.329114,0.465409,0.440252,0.724684,0.617089,0.811321,0.772152,0.699367,0.91195,0.509434,0.655063,0.632911,0.718354,0.699367,0.063291,0.616352,0.36478,0.939873,0.746835,0.439873,0.132911,0.867089,0,0.458861,0.767296,0.874214,0.550633,0.405063,0.522152,0.629747,0.677215,0.389937,0.927215,0.805031,0.791139,0.889241,0.873418,0.841772,0.389241,0.670886,0.27673,0.993711,0.474684,0.427673,0.642405,0.786164,0.622642,0.716981,0.629747,0.870253,0.221519,0.917722,0.220126,0.289308,0.974843,0.772152,0.601266,0.584906,0.899371,0.968553,0.518987,0.648734,0.310127,0.699367,0.327044,0.066456,0.139241,0.06962,0.816456,0.117089,0.63522,0.189873,0.893082,0.892405,0.873418,0.408805] area_under_roc_curve 0.553990526231988 [0.691824,0.151899,0.037736,0.889241,0.56962,0.100629,0.072785,0.150943,0.123418,0.836478,0.245283,0,0.139241,0.474684,0.335443,0.232704,0.18239,0.45283,0.503165,0.628931,0.39557,0.25,0.553459,0.993671,0.436709,0.54717,0.836478,0.939873,0.949367,0.91195,0.917722,0.746835,0.974843,0.647799,0.655063,0.734177,0.60443,0.613924,0.015823,0.72327,0.433962,0.911392,0.724684,0.322785,0.14557,0.541139,0.012579,0.825949,0.880503,1,0.756329,0.639241,0.446203,0.560127,0.367089,0.842767,0.977848,0.798742,0.664557,0.993671,0.661392,0.882911,0.243671,0.870253,0.352201,0.761006,0.35443,0.679245,0.528481,0.584906,0.830189,0.27044,0.278481,0.623418,0.329114,0.835443,0.226415,0.968553,0.572327,0.623418,0.506329,0.534591,0.924528,0.748428,0.693038,0.734177,0.287975,0.512658,0.383648,0.047468,0.196203,0.25,0.753165,0.246835,0.716981,0.272152,0.974843,0.939873,0.699367,0.867925] 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 0.015772870662460567 kappa 0.01553995424785044 kappa 0.013984356482578814 kappa 0.014140137451615454 kappa 0.020807833537331698 kappa 0.02061448542769134 kappa 0.021426034802509566 kappa 0.009112066585144568 kappa 0.008642803583408975 kappa 0.020885151407477594 kb_relative_information_score 5.934044400391537 kb_relative_information_score 5.391634058105228 kb_relative_information_score 5.5751083983249305 kb_relative_information_score 6.333047633469539 kb_relative_information_score 6.130842279002984 kb_relative_information_score 6.218688184915922 kb_relative_information_score 6.306533746038731 kb_relative_information_score 6.155228756885098 kb_relative_information_score 6.302119979952275 kb_relative_information_score 7.003936262110978 mean_absolute_error 0.019781519329792965 mean_absolute_error 0.0197842423690886 mean_absolute_error 0.01978386387018633 mean_absolute_error 0.019766621176155432 mean_absolute_error 0.019776650178555705 mean_absolute_error 0.019772126340987548 mean_absolute_error 0.01977101765208794 mean_absolute_error 0.019776161947460347 mean_absolute_error 0.019773159011666312 mean_absolute_error 0.019752652409231897 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 0.025 predictive_accuracy 0.025 predictive_accuracy 0.025 predictive_accuracy 0.025 predictive_accuracy 0.03125 predictive_accuracy 0.03125 predictive_accuracy 0.03125 predictive_accuracy 0.01875 predictive_accuracy 0.01875 predictive_accuracy 0.03125 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 0.025 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1] recall 0.025 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1] recall 0.025 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.025 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0] recall 0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0] recall 0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0] recall 0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.01875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.01875 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] recall 0.03125 [0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0] relative_absolute_error 0.9990666328178248 relative_absolute_error 0.9992041600549781 relative_absolute_error 0.999185043948803 relative_absolute_error 0.9983142008159293 relative_absolute_error 0.9988207160886702 relative_absolute_error 0.9985922394438138 relative_absolute_error 0.9985362450549448 relative_absolute_error 0.9987960579525411 relative_absolute_error 0.9986443945286 relative_absolute_error 0.9976087075369628 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 0.09965178022988505 root_mean_squared_error 0.09964219970383602 root_mean_squared_error 0.09966024528489877 root_mean_squared_error 0.09957864355507837 root_mean_squared_error 0.0996594529302425 root_mean_squared_error 0.09962326234364684 root_mean_squared_error 0.09961600129597202 root_mean_squared_error 0.09964384669792699 root_mean_squared_error 0.09960628263504175 root_mean_squared_error 0.0995150294328592 root_relative_squared_error 1.0015380748893474 root_relative_squared_error 1.001441786979654 root_relative_squared_error 1.0016231518933179 root_relative_squared_error 1.0008030236507173 root_relative_squared_error 1.0016151884293913 root_relative_squared_error 1.0012514593485407 root_relative_squared_error 1.001178483073625 root_relative_squared_error 1.0014583398930832 root_relative_squared_error 1.0010808068561388 root_relative_squared_error 1.0001636776665694 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 92180.468187 usercpu_time_millis 92146.50937699997 usercpu_time_millis 92371.48873200004 usercpu_time_millis 92535.63454 usercpu_time_millis 92689.37919300004 usercpu_time_millis 92795.86051000001 usercpu_time_millis 92988.24238600003 usercpu_time_millis 92718.60570600006 usercpu_time_millis 92714.09607700002 usercpu_time_millis 92768.56298499991 usercpu_time_millis_testing 58.243733000011844 usercpu_time_millis_testing 72.51420599999392 usercpu_time_millis_testing 62.059967000038796 usercpu_time_millis_testing 78.13399799999843 usercpu_time_millis_testing 59.71320900005139 usercpu_time_millis_testing 70.6170570000495 usercpu_time_millis_testing 58.44880799998009 usercpu_time_millis_testing 69.62972600001649 usercpu_time_millis_testing 61.39231299994208 usercpu_time_millis_testing 62.71807899997839 usercpu_time_millis_training 92122.224454 usercpu_time_millis_training 92073.99517099997 usercpu_time_millis_training 92309.428765 usercpu_time_millis_training 92457.500542 usercpu_time_millis_training 92629.66598399999 usercpu_time_millis_training 92725.24345299996 usercpu_time_millis_training 92929.79357800004 usercpu_time_millis_training 92648.97598000005 usercpu_time_millis_training 92652.70376400008 usercpu_time_millis_training 92705.84490599993