8958488 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) 6894094 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 146.7575815402708 7650 cache_size 200 7650 class_weight null 7650 coef0 0.6098645612544872 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.06153547214658849 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking false 7650 tol 0.002750795118732039 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 18832242 description https://api.openml.org/data/download/18832242/description.xml -1 18832243 predictions https://api.openml.org/data/download/18832243/predictions.arff area_under_roc_curve 0.9207702020202021 [0.874053,0.96875,1,0.934659,0.9375,0.842487,1,0.96875,0.874684,0.9375,0.968434,0.968434,0.96875,0.90625,0.965593,0.96875,1,0.747159,0.746528,0.651831,0.905619,0.9375,0.967172,1,0.937184,0.747159,0.74779,0.967172,0.936869,1,0.968434,0.96875,0.999369,0.809975,0.936869,0.936553,0.77904,0.71654,0.968119,0.968434,0.936237,0.968119,1,0.967803,0.937184,0.96875,0.936869,0.999369,0.935922,0.905934,0.874053,0.905303,0.905934,0.843119,0.936553,0.748106,1,0.968434,0.841856,0.873422,0.999369,0.968434,0.842803,0.999369,0.936869,0.842487,0.717172,1,0.968119,0.905303,0.874369,0.968119,0.93529,0.9375,0.905619,0.96875,0.968434,0.811237,0.96875,0.968434,0.937184,0.936869,0.90625,0.810922,0.905619,0.968434,1,0.936869,0.937184,0.96875,0.936869,0.999684,0.9375,0.968434,0.96875,0.937184,0.936553,0.967803,0.810922,0.937184] average_cost 0 f_measure 0.8442617170606332 [0.774194,0.967742,1,0.717949,0.933333,0.709677,1,0.967742,0.827586,0.933333,0.9375,0.9375,0.967742,0.896552,0.731707,0.967742,1,0.484848,0.457143,0.285714,0.83871,0.933333,0.833333,1,0.903226,0.484848,0.516129,0.833333,0.875,1,0.9375,0.967742,0.941176,0.588235,0.875,0.848485,0.5625,0.466667,0.909091,0.9375,0.823529,0.909091,1,0.882353,0.903226,0.967742,0.875,0.941176,0.8,0.866667,0.774194,0.8125,0.866667,0.758621,0.848485,0.533333,1,0.9375,0.666667,0.727273,0.941176,0.9375,0.733333,0.941176,0.875,0.709677,0.5,1,0.909091,0.8125,0.8,0.909091,0.756757,0.933333,0.83871,0.967742,0.9375,0.666667,0.967742,0.9375,0.903226,0.875,0.896552,0.645161,0.83871,0.9375,1,0.875,0.903226,0.967742,0.875,0.969697,0.933333,0.9375,0.967742,0.903226,0.848485,0.882353,0.645161,0.903226] kappa 0.8415404040404041 kb_relative_information_score 1348.4522169219833 mean_absolute_error 0.003137499999999988 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 0.8504637634523385 [0.8,1,1,0.608696,1,0.733333,1,1,0.923077,1,0.9375,0.9375,1,1,0.6,1,1,0.470588,0.421053,0.263158,0.866667,1,0.75,1,0.933333,0.470588,0.533333,0.75,0.875,1,0.9375,1,0.888889,0.555556,0.875,0.823529,0.5625,0.5,0.882353,0.9375,0.777778,0.882353,1,0.833333,0.933333,1,0.875,0.888889,0.736842,0.928571,0.8,0.8125,0.928571,0.846154,0.823529,0.571429,1,0.9375,0.647059,0.705882,0.888889,0.9375,0.785714,0.888889,0.875,0.733333,0.583333,1,0.882353,0.8125,0.857143,0.882353,0.666667,1,0.866667,1,0.9375,0.714286,1,0.9375,0.933333,0.875,1,0.666667,0.866667,0.9375,1,0.875,0.933333,1,0.875,0.941176,1,0.9375,1,0.933333,0.823529,0.833333,0.666667,0.933333] predictive_accuracy 0.843125 prior_entropy 6.6438561897747395 recall 0.843125 [0.75,0.9375,1,0.875,0.875,0.6875,1,0.9375,0.75,0.875,0.9375,0.9375,0.9375,0.8125,0.9375,0.9375,1,0.5,0.5,0.3125,0.8125,0.875,0.9375,1,0.875,0.5,0.5,0.9375,0.875,1,0.9375,0.9375,1,0.625,0.875,0.875,0.5625,0.4375,0.9375,0.9375,0.875,0.9375,1,0.9375,0.875,0.9375,0.875,1,0.875,0.8125,0.75,0.8125,0.8125,0.6875,0.875,0.5,1,0.9375,0.6875,0.75,1,0.9375,0.6875,1,0.875,0.6875,0.4375,1,0.9375,0.8125,0.75,0.9375,0.875,0.875,0.8125,0.9375,0.9375,0.625,0.9375,0.9375,0.875,0.875,0.8125,0.625,0.8125,0.9375,1,0.875,0.875,0.9375,0.875,1,0.875,0.9375,0.9375,0.875,0.875,0.9375,0.625,0.875] relative_absolute_error 0.15845959595959505 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.05601339125602009 root_relative_squared_error 0.5629557637320983 total_cost 0 area_under_roc_curve 0.9022171801608154 [0.996855,0.75,1,0.996855,0.75,0.996855,1,1,0.75,1,0.75,1,1,1,1,0.5,1,0.75,0.496835,0.490566,0.5,1,0.996855,1,1,0.743671,0.743671,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,1,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,0.996835,1,1,1,0.996855,0.496855,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,1,0.5,0.75,0.996855,1,1,1,1,0.75,0.5,1,1,1,0.996835,1,0.996855,1,1,1,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.5,0.993671,0.746835,1] area_under_roc_curve 0.9274540243611176 [1,1,1,0.990566,1,1,1,1,1,1,1,1,1,0.75,0.75,1,1,0.75,0.746835,0.490566,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.996835,1,0.496855,0.496855,0.496855,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.496855,1,1,1,0.75,1,1,1,1,1,0.996835,0.75,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.75,0.75,1,1,1,0.75,1,1,0.996835,0.75,1,0.75,1,0.75,1,1,1,1,1,0.75,0.496855] area_under_roc_curve 0.9369078894992435 [1,1,1,0.990566,1,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.743671,1,0.746835,1,0.5,0.75,1,1,0.746835,1,0.996855,1,1,1,1,1,0.75,0.993711,1,0.996855,0.5,1,1,0.75,1,1,1,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.996855,0.496835,1,1,0.75,1,1,1,1,1,1,0.75,1,0.996835,0.996855,1,0.996855,1,1,0.746835,0.75,0.996835,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1] 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 0.9179404506010668 [0.75,1,1,1,1,0.75,1,0.75,1,1,0.996835,1,1,0.5,0.987421,1,1,0.746835,0.993711,0.746835,1,1,1,1,1,0.993671,0.5,1,0.996855,1,1,0.5,1,0.746835,1,1,0.746835,0.75,1,1,0.996835,1,1,1,0.5,1,1,1,1,1,0.5,0.496855,0.75,0.996855,0.746835,0.5,1,0.996835,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.996835,1,1,0.5,0.75,1,0.996835,1,1,1,1,1,1,0.746835,0.996855,1,1,1,1,1,1,1,1,1,0.75,1,0.996835,1,0.496855,1] area_under_roc_curve 0.9178608391051668 [0.75,1,1,0.75,1,0.75,1,1,1,0.75,1,0.996835,0.5,1,0.993711,1,1,0.996835,1,0.75,0.75,0.75,0.996835,1,0.996835,0.746835,0.746835,0.5,1,1,1,1,1,0.746835,1,0.746835,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,0.996835,1,0.75,0.75,1,1,1,1,0.996835,0.996835,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.75,1,1,0.996835,1,0.75,1,1,1,0.75,1,1,0.746835,1,1,0.996855,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,0.75,1,1,1,1,1,1,0.996835,1,1,1] area_under_roc_curve 0.921065201815142 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,1,1,1,0.746835,1,1,0.996855,1,1,0.493671,0.746835,0.993671,0.5,1,0.75,1,1,0.5,0.75,0.75,0.996835,1,1,1,0.996835,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,1,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1] area_under_roc_curve 0.9179603534750417 [0.75,1,1,1,1,0.75,1,1,1,1,1,1,1,1,0.996855,1,1,0.496855,0.987342,0.496835,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.496835,0.746835,1,0.996855,1,1,1,1,1,1,1,1,0.993711,0.5,0.496855,0.75,0.75,0.996855,1,0.746835,1,1,0.996835,0.990566,1,0.996835,1,1,1,0.496855,0.5,1,1,0.996835,1,1,0.996835,1,0.996835,1,1,0.5,1,1,0.75,1,0.75,0.75,0.75,0.75,1,0.5,1,1,1,1,1,1,1,1,1,1,0.996835,1] 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 0.9148953108828914 [1,1,1,0.993671,1,0.996855,1,1,0.75,0.5,1,1,1,0.75,1,1,1,0.5,0.5,0.990566,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.996835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.75,1,1,1,1,0.996835,0.75,1,1,0.496835,1,0.993711,0.996855,0.5,1,0.75,0.996855,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,0.996835,1,1,1,1,1,0.996835,1,0.75,1,1,0.996855,1,1,1] 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 f_measure 0.8599999999999997 [1,1,1,0.4,1,1,1,1,1,1,1,1,1,0.666667,0.666667,1,1,0.666667,0.5,0,1,1,0.666667,1,1,0.5,0,1,0.8,1,1,1,1,0.8,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0.8,1,1,1,1,0.666667,1,0,1,1,1,0.666667,1,1,1,1,1,0.8,0.666667,1,1,1,0.8,1,1,1,1,1,1,0.8,1,0.666667,0.666667,1,1,1,0.666667,1,1,0.8,0.666667,1,0.666667,1,0.666667,1,1,1,1,1,0.666667,0] kappa 0.8041770302814956 kappa 0.8546947800679143 kappa 0.8736576121288693 kappa 0.8294445102451735 kappa 0.8357289527720739 kappa 0.8357354392892399 kappa 0.8420345944238212 kappa 0.8357224657426056 kappa 0.8736326659558504 kappa 0.8294512435846823 kb_relative_information_score 128.93234551626202 kb_relative_information_score 136.94980473787183 kb_relative_information_score 139.95635194597543 kb_relative_information_score 132.94107512706694 kb_relative_information_score 133.94325752976815 kb_relative_information_score 133.94325752976815 kb_relative_information_score 134.94543993246936 kb_relative_information_score 133.94325752976815 kb_relative_information_score 139.95635194597548 kb_relative_information_score 132.94107512706694 mean_absolute_error 0.0038750000000000013 mean_absolute_error 0.002875000000000001 mean_absolute_error 0.0025000000000000005 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.0025000000000000005 mean_absolute_error 0.003375000000000001 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] precision 0.9005208333333332 [1,1,1,0.25,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,0,1,0.666667,1,1,1,1,0.666667,1,0,0,0,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,0.666667,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,1,0] predictive_accuracy 0.80625 predictive_accuracy 0.85625 predictive_accuracy 0.875 predictive_accuracy 0.83125 predictive_accuracy 0.8375 predictive_accuracy 0.8375 predictive_accuracy 0.84375 predictive_accuracy 0.8375 predictive_accuracy 0.875 predictive_accuracy 0.83125 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 0.80625 [1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.85625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0] recall 0.875 [1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1] 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 0.8375 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1] recall 0.8375 [0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1] recall 0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,0.5,1,0,1,0.5,1,1,0,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1] recall 0.8375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.83125 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1] relative_absolute_error 0.19570707070707044 relative_absolute_error 0.14520202020202 relative_absolute_error 0.12626262626262608 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.12626262626262608 relative_absolute_error 0.17045454545454522 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 0.06224949798994367 root_mean_squared_error 0.05361902647381805 root_mean_squared_error 0.05 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.05 root_mean_squared_error 0.058094750193111264 root_relative_squared_error 0.6256309946079566 root_relative_squared_error 0.5388914922357191 root_relative_squared_error 0.5025189076296057 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.5025189076296057 root_relative_squared_error 0.5838742081211419 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 695.8635629998753 usercpu_time_millis 699.9872369999594 usercpu_time_millis 708.4778450000613 usercpu_time_millis 704.441679999718 usercpu_time_millis 702.2267860002103 usercpu_time_millis 701.183176000086 usercpu_time_millis 729.1788449999785 usercpu_time_millis 698.2064689998424 usercpu_time_millis 711.6606640001919 usercpu_time_millis 695.8046680001644 usercpu_time_millis_testing 54.735621999952855 usercpu_time_millis_testing 55.25433899993004 usercpu_time_millis_testing 54.41397599997799 usercpu_time_millis_testing 55.821033999791325 usercpu_time_millis_testing 55.31320700015385 usercpu_time_millis_testing 56.44821500004582 usercpu_time_millis_testing 61.11265200001981 usercpu_time_millis_testing 56.18159599998762 usercpu_time_millis_testing 55.2754590000859 usercpu_time_millis_testing 52.151928000057524 usercpu_time_millis_training 641.1279409999224 usercpu_time_millis_training 644.7328980000293 usercpu_time_millis_training 654.0638690000833 usercpu_time_millis_training 648.6206459999266 usercpu_time_millis_training 646.9135790000564 usercpu_time_millis_training 644.7349610000401 usercpu_time_millis_training 668.0661929999587 usercpu_time_millis_training 642.0248729998548 usercpu_time_millis_training 656.385205000106 usercpu_time_millis_training 643.6527400001069