8958839 1935 Hilde Weerts 9954 Supervised Classification 8317 sklearn.pipeline.Pipeline(imputation=hyperimp.utils.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,clf=sklearn.svm.classes.SVC)(1) 6894443 categorical_features [] 7644 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7644 handle_unknown "ignore" 7644 n_values "auto" 7644 sparse true 7644 threshold 0.0 7645 copy true 7646 with_mean false 7646 with_std true 7646 C 111.00483735628511 7650 cache_size 200 7650 class_weight null 7650 coef0 0.5897239368660909 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.04786221131501912 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking true 7650 tol 0.0022850502104658217 7650 verbose false 7650 axis 0 8316 categorical_features [] 8316 copy true 8316 fill_empty 0 8316 missing_values "NaN" 8316 strategy "mean" 8316 strategy_nominal "most_frequent" 8316 verbose 0 8316 memory null 8317 openml-python Sklearn_0.19.1. study_98 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18832938 description https://api.openml.org/data/download/18832938/description.xml -1 18832939 predictions https://api.openml.org/data/download/18832939/predictions.arff area_under_roc_curve 0.9201388888888886 [0.874053,0.96875,1,0.937184,0.937184,0.842803,1,1,0.905934,0.9375,0.968119,0.968434,0.96875,0.905934,0.966225,1,1,0.747159,0.745896,0.619949,0.874369,0.96875,0.967172,1,0.937184,0.746528,0.74779,0.967172,0.937184,1,0.968434,0.96875,0.999369,0.809975,0.905619,0.936869,0.77904,0.685606,0.968119,1,0.936237,0.968119,1,0.905303,0.968434,0.96875,0.936869,0.968119,0.936237,0.905619,0.873422,0.905303,0.905934,0.842803,0.905303,0.748106,1,0.968434,0.841856,0.842172,0.999369,0.968434,0.842803,0.999369,0.968119,0.842487,0.717172,1,0.968119,0.905303,0.874369,0.968434,0.93529,0.96875,0.904987,0.96875,0.968434,0.810922,0.96875,0.999684,0.937184,0.936553,0.90625,0.810922,0.905619,0.968434,1,0.936553,0.937184,0.96875,0.936869,0.968434,0.937184,0.968434,0.96875,0.936869,0.936553,0.936553,0.811237,0.937184] average_cost 0 f_measure 0.8429125762410946 [0.774194,0.967742,1,0.903226,0.903226,0.733333,1,1,0.866667,0.933333,0.909091,0.9375,0.967742,0.866667,0.769231,1,1,0.484848,0.432432,0.222222,0.8,0.967742,0.833333,1,0.903226,0.457143,0.516129,0.833333,0.903226,1,0.9375,0.967742,0.941176,0.588235,0.83871,0.875,0.5625,0.428571,0.909091,1,0.823529,0.909091,1,0.8125,0.9375,0.967742,0.875,0.909091,0.823529,0.83871,0.727273,0.8125,0.866667,0.733333,0.8125,0.533333,1,0.9375,0.666667,0.6875,0.941176,0.9375,0.733333,0.941176,0.909091,0.709677,0.5,1,0.909091,0.8125,0.8,0.9375,0.756757,0.967742,0.787879,0.967742,0.9375,0.645161,0.967742,0.969697,0.903226,0.848485,0.896552,0.645161,0.83871,0.9375,1,0.848485,0.903226,0.967742,0.875,0.9375,0.903226,0.9375,0.967742,0.875,0.848485,0.848485,0.666667,0.903226] kappa 0.8402777777777778 kb_relative_information_score 1346.447852116581 mean_absolute_error 0.0031624999999999874 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] precision 0.8478412757169994 [0.8,1,1,0.933333,0.933333,0.785714,1,1,0.928571,1,0.882353,0.9375,1,0.928571,0.652174,1,1,0.470588,0.380952,0.2,0.857143,1,0.75,1,0.933333,0.421053,0.533333,0.75,0.933333,1,0.9375,1,0.888889,0.555556,0.866667,0.875,0.5625,0.5,0.882353,1,0.777778,0.882353,1,0.8125,0.9375,1,0.875,0.882353,0.777778,0.866667,0.705882,0.8125,0.928571,0.785714,0.8125,0.571429,1,0.9375,0.647059,0.6875,0.888889,0.9375,0.785714,0.888889,0.882353,0.733333,0.583333,1,0.882353,0.8125,0.857143,0.9375,0.666667,1,0.764706,1,0.9375,0.666667,1,0.941176,0.933333,0.823529,1,0.666667,0.866667,0.9375,1,0.823529,0.933333,1,0.875,0.9375,0.933333,0.9375,1,0.875,0.823529,0.823529,0.714286,0.933333] predictive_accuracy 0.841875 prior_entropy 6.6438561897747395 recall 0.841875 [0.75,0.9375,1,0.875,0.875,0.6875,1,1,0.8125,0.875,0.9375,0.9375,0.9375,0.8125,0.9375,1,1,0.5,0.5,0.25,0.75,0.9375,0.9375,1,0.875,0.5,0.5,0.9375,0.875,1,0.9375,0.9375,1,0.625,0.8125,0.875,0.5625,0.375,0.9375,1,0.875,0.9375,1,0.8125,0.9375,0.9375,0.875,0.9375,0.875,0.8125,0.75,0.8125,0.8125,0.6875,0.8125,0.5,1,0.9375,0.6875,0.6875,1,0.9375,0.6875,1,0.9375,0.6875,0.4375,1,0.9375,0.8125,0.75,0.9375,0.875,0.9375,0.8125,0.9375,0.9375,0.625,0.9375,1,0.875,0.875,0.8125,0.625,0.8125,0.9375,1,0.875,0.875,0.9375,0.875,0.9375,0.875,0.9375,0.9375,0.875,0.875,0.875,0.625,0.875] relative_absolute_error 0.15972222222222132 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.05623610939600985 root_relative_squared_error 0.5651941652604373 total_cost 0 area_under_roc_curve 0.9116511424249664 [0.996855,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,1,1,1,1,0.75,0.496835,0.490566,0.5,1,0.996855,1,1,0.743671,0.743671,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,1,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,0.996835,1,1,1,0.996855,0.496855,1,1,0.5,1,1,1,0.75,1,1,1,0.5,1,1,0.5,0.75,1,1,1,1,1,0.75,0.5,1,1,1,0.996835,1,0.996855,1,1,1,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.5,0.993671,0.746835,1] area_under_roc_curve 0.9179404506010667 [1,1,1,0.996855,1,1,1,1,1,1,1,1,1,0.75,0.75,1,1,0.75,0.746835,0.487421,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.996835,0.75,0.496855,0.496855,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.75,1,0.496855,1,1,1,0.5,1,1,0.75,1,1,0.996835,0.75,1,1,1,0.996835,1,1,1,0.996855,1,1,0.993671,1,1,0.75,0.996835,1,1,0.75,1,1,0.996835,0.75,1,0.75,1,0.75,1,1,1,1,1,0.75,0.496855] area_under_roc_curve 0.9368879866252685 [1,1,1,1,1,0.746835,1,1,1,1,1,0.75,1,1,0.996835,1,1,0.743671,1,0.496835,1,1,0.75,1,1,0.746835,1,0.996855,1,1,1,1,1,0.75,0.993711,1,0.993711,0.5,1,1,0.75,1,1,1,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.996855,0.496835,1,1,0.75,1,1,1,1,1,1,0.75,1,0.996835,0.996855,1,0.993711,1,1,0.746835,0.75,0.996835,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1] area_under_roc_curve 0.9148156993869915 [0.746835,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,1,1,1,1,0.743671,0.493711,0.746835,1,1,0.993671,1,0.5,1,1,1,1,1,1,1,1,0.75,1,1,1,0.496855,1,1,1,0.996855,1,0.996855,1,1,1,1,0.996835,1,0.996835,1,1,0.75,1,0.743671,1,1,0.996855,1,0.996855,1,1,1,0.75,0.5,0.496835,1,1,0.746835,0.5,1,0.990566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996855,0.75,0.996835,0.993711,0.75] area_under_roc_curve 0.9115715309290661 [0.75,1,1,1,1,0.75,1,1,1,1,0.996835,1,1,0.496855,0.996855,1,1,0.746835,0.990566,0.743671,0.75,1,1,1,1,0.987342,0.5,1,1,1,1,0.5,1,0.496835,1,1,0.746835,0.5,1,1,0.996835,1,1,0.5,1,1,1,1,1,1,0.496855,0.496855,0.75,0.996855,0.496835,0.5,1,0.996835,0.746835,1,1,1,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,0.75,1,0.996835,1,1,1,1,1,1,0.746835,0.996855,1,1,1,1,1,1,0.75,1,1,0.75,1,0.996835,1,0.5,1] area_under_roc_curve 0.921025396067192 [0.75,1,1,0.75,1,0.75,1,1,1,0.75,1,0.996835,0.5,1,0.993711,1,1,0.996835,1,0.75,0.75,0.75,0.996835,1,0.996835,0.746835,0.746835,0.5,1,1,1,1,1,0.996835,1,0.75,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,0.996835,1,0.75,0.75,1,1,1,1,0.996835,0.996835,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.75,1,1,0.996835,1,0.75,1,1,1,0.75,1,1,0.746835,1,1,0.996855,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,0.75,1,1,1,1,1,1,0.996835,1,1,1] area_under_roc_curve 0.9242098559031922 [1,1,1,1,0.996855,1,1,1,1,1,1,1,1,1,1,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,1,1,1,0.746835,1,1,0.996855,1,1,0.493671,0.746835,0.993671,1,1,0.75,1,1,0.5,0.75,0.75,0.996835,1,1,1,0.996835,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,1,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1] area_under_roc_curve 0.917920547727092 [0.75,1,1,1,1,0.75,1,1,1,1,1,1,1,1,0.996855,1,1,0.496855,0.984177,0.496835,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.5,0.746835,1,1,1,1,1,1,1,1,1,0.75,0.996855,0.5,0.496855,0.75,0.75,0.996855,1,0.746835,1,1,0.996835,0.990566,1,0.996835,1,1,1,0.496855,0.5,1,1,0.996835,1,1,0.996835,1,0.996835,1,1,0.5,1,1,0.75,1,0.75,0.75,0.75,0.75,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.996835,1] area_under_roc_curve 0.9369078894992435 [0.996855,1,1,0.75,1,0.996855,1,1,0.75,1,1,1,1,1,0.996835,1,1,0.493711,1,0.496855,1,1,1,1,1,0.496855,0.996855,0.993671,1,1,1,1,1,0.996855,1,1,1,0.5,0.75,1,0.5,1,1,0.746835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.75,0.5,1,0.5,0.996835,1,1,1,1,0.996835,1,1,0.743671,1,0.996835,1,1,1,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1] area_under_roc_curve 0.9085661969588406 [1,1,1,1,1,0.996855,1,1,0.75,0.5,0.996855,1,1,0.75,0.993671,1,1,0.5,0.5,0.990566,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.996835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.746835,1,1,1,1,0.996835,0.75,1,1,0.496835,1,0.993711,0.996855,0.5,1,0.75,1,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,0.996835,1,0.746835,1,1,1,0.996835,1,0.75,1,1,0.996855,0.75,1,1] 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 0.8231136731551308 kappa 0.8357354392892399 kappa 0.8736576121288693 kappa 0.8294445102451735 kappa 0.8230857323381905 kappa 0.8420533070088845 kappa 0.8483532106468683 kappa 0.835715978200774 kappa 0.8736326659558504 kappa 0.8168180023687327 kb_relative_information_score 131.9388927243657 kb_relative_information_score 133.94325752976818 kb_relative_information_score 139.95635194597543 kb_relative_information_score 132.94107512706694 kb_relative_information_score 131.93889272436573 kb_relative_information_score 134.94543993246936 kb_relative_information_score 135.9476223351706 kb_relative_information_score 133.94325752976815 kb_relative_information_score 139.95635194597548 kb_relative_information_score 130.93671032166446 mean_absolute_error 0.003500000000000001 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.0025000000000000005 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.003500000000000001 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.0025000000000000005 mean_absolute_error 0.003625000000000001 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 0.825 predictive_accuracy 0.8375 predictive_accuracy 0.875 predictive_accuracy 0.83125 predictive_accuracy 0.825 predictive_accuracy 0.84375 predictive_accuracy 0.85 predictive_accuracy 0.8375 predictive_accuracy 0.875 predictive_accuracy 0.81875 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 0.825 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.8375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0] recall 0.875 [1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1] recall 0.83125 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5] recall 0.825 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0,1,0,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,0,0,0.5,1,0,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1] recall 0.84375 [0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1] recall 0.85 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,0,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1] recall 0.8375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1] recall 0.875 [1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1] recall 0.81875 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1] relative_absolute_error 0.17676767676767655 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.12626262626262608 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.17676767676767655 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.12626262626262608 relative_absolute_error 0.18308080808080782 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 0.05916079783099617 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.05 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.05916079783099617 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.05 root_mean_squared_error 0.06020797289396149 root_relative_squared_error 0.5945883900105629 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.5025189076296057 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5945883900105629 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.5025189076296057 root_relative_squared_error 0.6051128953853288 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 732.6434490000793 usercpu_time_millis 750.8887540002434 usercpu_time_millis 735.7970789998944 usercpu_time_millis 730.4960480003047 usercpu_time_millis 738.8840529999925 usercpu_time_millis 746.2838589999592 usercpu_time_millis 772.7314179999212 usercpu_time_millis 747.2217060001185 usercpu_time_millis 735.8341849999306 usercpu_time_millis 738.4881670000141 usercpu_time_millis_testing 54.49739000005138 usercpu_time_millis_testing 55.78021600013017 usercpu_time_millis_testing 52.85320099983437 usercpu_time_millis_testing 55.283643000166194 usercpu_time_millis_testing 55.579932000000554 usercpu_time_millis_testing 56.19297899988851 usercpu_time_millis_testing 59.216022999862616 usercpu_time_millis_testing 54.93100500007131 usercpu_time_millis_testing 55.48565999993116 usercpu_time_millis_testing 58.470340999974724 usercpu_time_millis_training 678.1460590000279 usercpu_time_millis_training 695.1085380001132 usercpu_time_millis_training 682.9438780000601 usercpu_time_millis_training 675.2124050001385 usercpu_time_millis_training 683.3041209999919 usercpu_time_millis_training 690.0908800000707 usercpu_time_millis_training 713.5153950000586 usercpu_time_millis_training 692.2907010000472 usercpu_time_millis_training 680.3485249999994 usercpu_time_millis_training 680.0178260000393