8958589 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) 6894194 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 3.5278090462444274 7650 cache_size 200 7650 class_weight null 7650 coef0 0.340938623630204 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.0021475318230621183 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking false 7650 tol 0.00023107581556458399 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 18832442 description https://api.openml.org/data/download/18832442/description.xml -1 18832443 predictions https://api.openml.org/data/download/18832443/predictions.arff area_under_roc_curve 0.9037247474747474 [0.872159,0.96875,1,1,0.9375,0.843434,0.999684,1,0.935922,0.96875,0.874684,0.999684,0.999369,0.812184,1,0.90625,1,0.683081,0.777778,0.560606,0.843434,0.96875,0.872475,1,0.905934,0.684975,0.746843,0.998737,0.936869,1,0.999684,1,0.968434,0.810606,0.875,0.96654,0.839015,0.620896,0.967487,0.999684,0.873106,0.999369,1,0.998737,0.96875,0.96875,0.905934,0.936553,0.967172,0.905934,0.874369,0.905303,0.90625,0.843119,0.874053,0.620896,1,0.968434,0.716856,0.842487,0.904987,0.968119,0.873106,0.968119,0.936553,0.841856,0.714646,1,0.905934,0.779987,0.873422,0.874684,0.841856,0.96875,0.905303,0.968434,0.968434,0.716225,0.96875,0.999684,0.905934,0.904987,0.936869,0.841225,0.936553,0.937184,0.96875,0.936869,0.843119,0.96875,0.811237,0.968434,0.968434,0.937184,0.96875,0.905303,0.936237,0.96654,0.624369,0.905619] average_cost 0 f_measure 0.8093116702036344 [0.648649,0.967742,1,1,0.933333,0.785714,0.969697,1,0.8,0.967742,0.827586,0.969697,0.941176,0.740741,1,0.896552,1,0.333333,0.5,0.166667,0.785714,0.967742,0.666667,1,0.866667,0.4,0.470588,0.888889,0.875,1,0.969697,1,0.9375,0.625,0.857143,0.789474,0.52381,0.242424,0.857143,0.969697,0.705882,0.941176,1,0.888889,0.967742,0.967742,0.866667,0.848485,0.833333,0.866667,0.8,0.8125,0.896552,0.758621,0.774194,0.242424,1,0.9375,0.482759,0.709677,0.787879,0.909091,0.705882,0.909091,0.848485,0.666667,0.388889,1,0.866667,0.62069,0.727273,0.827586,0.666667,0.967742,0.8125,0.9375,0.9375,0.451613,0.967742,0.969697,0.866667,0.787879,0.875,0.628571,0.848485,0.903226,0.967742,0.875,0.758621,0.967742,0.666667,0.9375,0.9375,0.903226,0.967742,0.8125,0.823529,0.789474,0.363636,0.83871] kappa 0.8074494949494949 kb_relative_information_score 1294.3343671761222 mean_absolute_error 0.003812499999999974 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 0.8190492697447032 [0.571429,1,1,1,1,0.916667,0.941176,1,0.736842,1,0.923077,0.941176,0.888889,0.909091,1,1,1,0.3,0.45,0.25,0.916667,1,0.6,1,0.928571,0.428571,0.444444,0.8,0.875,1,0.941176,1,0.9375,0.625,1,0.681818,0.423077,0.235294,0.789474,0.941176,0.666667,0.888889,1,0.8,1,1,0.928571,0.823529,0.75,0.928571,0.857143,0.8125,1,0.846154,0.8,0.235294,1,0.9375,0.538462,0.733333,0.764706,0.882353,0.666667,0.882353,0.823529,0.647059,0.35,1,0.928571,0.692308,0.705882,0.923077,0.647059,1,0.8125,0.9375,0.9375,0.466667,1,0.941176,0.928571,0.764706,0.875,0.578947,0.823529,0.933333,1,0.875,0.846154,1,0.714286,0.9375,0.9375,0.933333,1,0.8125,0.777778,0.681818,0.666667,0.866667] predictive_accuracy 0.809375 prior_entropy 6.6438561897747395 recall 0.809375 [0.75,0.9375,1,1,0.875,0.6875,1,1,0.875,0.9375,0.75,1,1,0.625,1,0.8125,1,0.375,0.5625,0.125,0.6875,0.9375,0.75,1,0.8125,0.375,0.5,1,0.875,1,1,1,0.9375,0.625,0.75,0.9375,0.6875,0.25,0.9375,1,0.75,1,1,1,0.9375,0.9375,0.8125,0.875,0.9375,0.8125,0.75,0.8125,0.8125,0.6875,0.75,0.25,1,0.9375,0.4375,0.6875,0.8125,0.9375,0.75,0.9375,0.875,0.6875,0.4375,1,0.8125,0.5625,0.75,0.75,0.6875,0.9375,0.8125,0.9375,0.9375,0.4375,0.9375,1,0.8125,0.8125,0.875,0.6875,0.875,0.875,0.9375,0.875,0.6875,0.9375,0.625,0.9375,0.9375,0.875,0.9375,0.8125,0.875,0.9375,0.25,0.8125] relative_absolute_error 0.1925505050505034 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.06174544517614213 root_relative_squared_error 0.6205650732203728 total_cost 0 area_under_roc_curve 0.8897380781784887 [0.990566,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,0.5,1,0.5,1,0.5,0.746835,0.990566,0.5,1,0.993711,1,1,0.5,1,0.996835,1,1,1,1,0.746835,0.5,0.75,0.993711,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.993711,1,0.75,1,1,1,1,0.996855,0.487421,1,1,0.5,1,1,0.996855,0.75,1,1,1,0.496835,1,1,0.5,0.75,1,1,1,0.996855,0.996835,0.75,0.5,1,1,1,0.996835,1,0.996855,1,0.75,0.75,1,0.5,1,0.746835,1,1,1,1,0.746835,0.996855,0.993671,0.5,1] area_under_roc_curve 0.9211249104370671 [1,1,1,1,1,1,0.996835,1,1,1,0.75,1,1,0.75,1,1,1,0.5,0.75,0.496855,1,1,0.996855,1,1,0.5,0.493671,1,0.996835,1,1,1,1,1,0.75,0.996855,0.996855,0.996855,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.487421,1,1,0.996855,0.5,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993671,1,1,0.746835,0.75,0.996855,1,0.746835,1,1,0.996835,0.5,1,0.75,1,1,0.75,1,1,1,0.996835,0.5,0.496855] area_under_roc_curve 0.892723509274739 [0.743671,1,1,1,1,1,1,1,0.996835,1,0.75,1,0.996855,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,0.743671,0.996835,0.996855,1,1,1,1,1,0.496835,1,0.996855,0.990566,0.987421,1,1,0.75,1,1,0.996855,1,1,1,1,0.75,1,0.996835,1,1,0.75,1,0.5,1,1,0.993711,0.496835,0.496855,1,0.75,0.5,0.996835,0.993671,0.5,1,0.5,0.5,1,0.996835,0.993711,1,1,1,1,0.496835,0.75,0.996835,1,1,0.996835,0.5,1,1,1,1,0.996835,0.5,1,1,1,1,1,1,1,0.746835,0.5,1] area_under_roc_curve 0.8928429265185891 [0.75,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,0.75,1,1,1,0.5,0.487421,0.5,1,1,0.993671,1,0.5,0.75,0.75,1,1,1,1,1,1,0.75,1,0.993711,0.996855,0.484277,0.996855,1,0.996835,0.996855,1,0.996855,1,1,0.75,1,0.996835,1,1,1,1,0.75,1,0.75,1,1,0.993711,1,1,1,0.996835,1,0.75,0.75,0.487342,1,1,0.746835,0.5,0.75,0.490566,1,1,1,1,0.75,1,1,1,1,0.75,0.5,1,1,1,1,1,1,0.75,1,0.996855,1,1,0.996855,0.75,0.996835,1,0.75] area_under_roc_curve 0.9179404506010667 [0.996835,1,1,1,1,0.75,1,1,1,1,1,1,1,0.496855,1,1,1,0.746835,0.993711,0.75,0.75,1,0.75,1,1,0.996835,0.496835,1,0.996855,1,1,1,1,0.746835,1,1,0.996835,0.746835,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.5,0.496855,0.75,0.996855,0.746835,0.5,1,0.996835,0.5,1,1,1,1,1,0.75,1,0.993711,1,0.75,0.75,0.996835,1,1,1,0.75,1,0.996835,0.75,1,1,1,1,1,0.993671,0.996855,1,1,1,1,1,1,0.75,1,1,0.75,1,0.996835,0.996855,0.496855,1] area_under_roc_curve 0.9242496616511421 [0.75,1,1,1,1,0.75,1,1,0.996855,0.75,0.75,0.996835,1,1,1,1,1,0.996835,0.990566,0.5,0.75,0.75,1,1,0.996835,0.996835,0.746835,1,1,1,1,1,1,0.996835,1,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,1,1,0.75,1,1,1,1,0.996835,1,1,1,0.5,1,0.993671,1,0.993711,1,1,0.5,0.996855,1,1,1,0.75,0.75,1,1,0.75,1,1,0.746835,1,1,1,0.75,1,0.993671,1,1,1,1,0.996835,1,0.496855,1,1,1,1,1,1,0.996855,0.5,1] area_under_roc_curve 0.8894992436907886 [0.996835,1,1,1,1,0.75,1,1,0.996855,1,0.996855,1,1,1,1,0.75,1,0.993711,0.496835,0.5,0.746835,1,0.746835,1,0.75,0.5,0.490566,1,1,1,0.996835,1,1,0.996855,1,0.996835,0.484177,0.5,0.990506,1,0.996835,1,1,1,0.5,0.75,0.5,0.746835,1,1,1,0.996835,1,1,0.75,0.5,1,1,0.5,1,0.996835,0.996835,0.996855,0.996835,1,0.5,0.993711,1,1,1,0.996855,1,0.75,1,0.996835,1,1,0.5,1,1,0.75,1,1,0.996835,1,1,1,0.5,0.5,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1] area_under_roc_curve 0.8960273863545896 [0.75,1,1,1,1,0.75,1,1,0.996855,1,1,1,0.996835,1,1,0.75,1,0.987421,0.75,0.5,0.75,1,0.996835,1,0.75,0.490566,1,1,0.5,1,1,1,1,1,0.75,1,0.990506,0.496835,1,0.996855,0.5,1,1,1,1,1,1,0.75,0.993711,0.5,0.496855,0.75,0.75,0.996855,1,0.5,1,1,1,0.990566,1,1,1,1,1,0.496855,0.996855,1,1,0.746835,1,1,1,1,0.996835,1,1,0.490566,1,1,0.75,1,0.75,1,0.75,0.75,1,0.496855,1,1,0.5,1,1,0.996855,1,0.996835,0.75,1,0.746835,1] area_under_roc_curve 0.921244327680917 [0.993711,1,1,1,1,0.996855,1,1,0.75,1,1,1,1,1,1,1,1,0.993711,1,0.496855,1,1,0.996855,1,1,0.496855,0.993711,0.996835,1,1,1,1,1,0.996855,1,1,0.75,0.5,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.5,0.5,1,0.5,0.75,1,0.75,1,0.996835,0.996835,1,0.996855,0.746835,1,0.996835,0.496855,0.996855,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.996855,1,1,1,1,1,1,0.75,1,1,1,1,0.75,0.996855,1,0.75,0.5] area_under_roc_curve 0.8929026351405142 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1,1,0.487421,0.75,0.496855,1,1,1,1,1,0.5,0.5,0.996835,0.75,1,1,1,1,0.996855,0.75,1,0.5,0.5,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.75,1,0.984277,1,1,0.746835,0.75,0.75,0.75,0.496835,1,0.993711,0.993711,0.496835,1,0.75,0.996855,1,0.5,0.496835,0.75,0.75,0.75,1,1,1,1,1,0.490566,1,0.496855,0.996835,0.996835,1,1,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,1,0.75,0.996855] 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 0.7790055248618785 kappa 0.8420782502270125 kappa 0.7852433776795231 kappa 0.7853027073960059 kappa 0.8357484107869073 kappa 0.848389134554643 kappa 0.7788833609729132 kappa 0.7915926583777383 kappa 0.8420844848006317 kappa 0.7853027073960059 kb_relative_information_score 124.92361590545715 kb_relative_information_score 134.9454399324694 kb_relative_information_score 125.92579830815835 kb_relative_information_score 125.92579830815838 kb_relative_information_score 133.94325752976815 kb_relative_information_score 135.9476223351706 kb_relative_information_score 124.92361590545711 kb_relative_information_score 126.92798071085957 kb_relative_information_score 134.94543993246936 kb_relative_information_score 125.92579830815838 mean_absolute_error 0.004375000000000002 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.004250000000000002 mean_absolute_error 0.004250000000000002 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.004375000000000002 mean_absolute_error 0.004125000000000002 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.004250000000000002 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 0.78125 predictive_accuracy 0.84375 predictive_accuracy 0.7875 predictive_accuracy 0.7875 predictive_accuracy 0.8375 predictive_accuracy 0.85 predictive_accuracy 0.78125 predictive_accuracy 0.79375 predictive_accuracy 0.84375 predictive_accuracy 0.7875 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 0.78125 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0,0.5,1,0,1,1,1,1,0,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0,1,0.5,1,1,1,1,0.5,1,1,0,1] recall 0.84375 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,0,0] recall 0.7875 [0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,0,1,0.5,0,1,1,0,1,0,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,0,1] recall 0.7875 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5] recall 0.8375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1] recall 0.85 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1] recall 0.78125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,0.5,1,1,0,1] recall 0.79375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1] recall 0.84375 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0,0,1,0,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,0] recall 0.7875 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,0.5,0.5,0.5,0.5,0,1,1,1,0,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1] relative_absolute_error 0.2209595959595957 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.2146464646464644 relative_absolute_error 0.2146464646464644 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.2209595959595957 relative_absolute_error 0.20833333333333307 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.2146464646464644 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 0.06614378277661478 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.06519202405202651 root_mean_squared_error 0.06519202405202651 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.06614378277661478 root_mean_squared_error 0.06422616289332567 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.06519202405202651 root_relative_squared_error 0.6647700293478879 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.6552044942557468 root_relative_squared_error 0.6552044942557468 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.6647700293478879 root_relative_squared_error 0.6454972243679026 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.6552044942557468 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 447.383555000215 usercpu_time_millis 465.91230200010614 usercpu_time_millis 437.1430319997671 usercpu_time_millis 425.9862830001566 usercpu_time_millis 424.1706980001254 usercpu_time_millis 433.76612099996237 usercpu_time_millis 431.94678800000474 usercpu_time_millis 426.64554499992846 usercpu_time_millis 438.43212000001586 usercpu_time_millis 428.55655200014553 usercpu_time_millis_testing 55.35509700007424 usercpu_time_millis_testing 57.04646100002719 usercpu_time_millis_testing 54.18558899987147 usercpu_time_millis_testing 52.48921900010828 usercpu_time_millis_testing 53.41597100004947 usercpu_time_millis_testing 51.982814999973925 usercpu_time_millis_testing 52.70144499991147 usercpu_time_millis_testing 53.73119199998655 usercpu_time_millis_testing 52.06300600002578 usercpu_time_millis_testing 52.42701900010616 usercpu_time_millis_training 392.02845800014074 usercpu_time_millis_training 408.86584100007894 usercpu_time_millis_training 382.95744299989565 usercpu_time_millis_training 373.49706400004834 usercpu_time_millis_training 370.75472700007595 usercpu_time_millis_training 381.78330599998844 usercpu_time_millis_training 379.24534300009327 usercpu_time_millis_training 372.9143529999419 usercpu_time_millis_training 386.3691139999901 usercpu_time_millis_training 376.1295330000394