8958161 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) 6893769 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 4.432881370625326 7650 cache_size 200 7650 class_weight null 7650 coef0 0.3574122377360255 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.0026375611899159507 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking false 7650 tol 0.0002689360060707767 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 18831590 description https://api.openml.org/data/download/18831590/description.xml -1 18831591 predictions https://api.openml.org/data/download/18831591/predictions.arff area_under_roc_curve 0.9157196969696971 [0.873106,0.96875,1,1,0.9375,0.874684,1,1,0.936553,0.96875,0.874684,0.999684,0.96875,0.780934,1,0.90625,1,0.714646,0.778409,0.559343,0.874684,0.96875,0.966856,1,0.937184,0.746212,0.747475,0.999053,0.937184,1,0.999684,1,0.968434,0.81029,0.90625,0.967487,0.839962,0.684028,0.967487,0.999684,0.873106,0.968119,1,0.998737,0.968434,0.96875,0.874684,0.999369,0.936237,0.905934,0.967803,0.905303,0.90625,0.843434,0.874053,0.654672,1,0.968434,0.811237,0.904987,0.936869,0.999369,0.842172,0.999369,0.936553,0.841856,0.778093,1,0.905934,0.842487,0.905619,0.874684,0.872475,0.96875,0.905303,0.968434,0.968434,0.716856,0.96875,0.999684,0.905934,0.936237,0.9375,0.904356,0.936869,0.937184,0.96875,0.967803,0.905619,1,0.842803,0.968119,0.968434,0.968119,0.96875,0.936869,0.935922,0.96654,0.686237,0.905934] average_cost 0 f_measure 0.832965898384779 [0.705882,0.967742,1,1,0.933333,0.827586,1,1,0.848485,0.967742,0.827586,0.969697,0.967742,0.692308,1,0.896552,1,0.388889,0.529412,0.142857,0.827586,0.967742,0.810811,1,0.903226,0.444444,0.5,0.914286,0.903226,1,0.969697,1,0.9375,0.606061,0.896552,0.857143,0.564103,0.363636,0.857143,0.969697,0.705882,0.909091,1,0.888889,0.9375,0.967742,0.827586,0.941176,0.823529,0.866667,0.882353,0.8125,0.896552,0.785714,0.774194,0.384615,1,0.9375,0.666667,0.787879,0.875,0.941176,0.6875,0.941176,0.848485,0.666667,0.514286,1,0.866667,0.709677,0.83871,0.827586,0.666667,0.967742,0.8125,0.9375,0.9375,0.482759,0.967742,0.969697,0.866667,0.823529,0.933333,0.742857,0.875,0.903226,0.967742,0.882353,0.83871,1,0.733333,0.909091,0.9375,0.909091,0.967742,0.875,0.8,0.789474,0.461538,0.866667] kappa 0.8314393939393939 kb_relative_information_score 1332.4172984787647 mean_absolute_error 0.0033374999999999837 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 0.8405477303558974 [0.666667,1,1,1,1,0.923077,1,1,0.823529,1,0.923077,0.941176,1,0.9,1,1,1,0.35,0.5,0.166667,0.923077,1,0.714286,1,0.933333,0.4,0.5,0.842105,0.933333,1,0.941176,1,0.9375,0.588235,1,0.789474,0.478261,0.352941,0.789474,0.941176,0.666667,0.882353,1,0.8,0.9375,1,0.923077,0.888889,0.777778,0.928571,0.833333,0.8125,1,0.916667,0.8,0.5,1,0.9375,0.714286,0.764706,0.875,0.888889,0.6875,0.888889,0.823529,0.647059,0.473684,1,0.928571,0.733333,0.866667,0.923077,0.6,1,0.8125,0.9375,0.9375,0.538462,1,0.941176,0.928571,0.777778,1,0.684211,0.875,0.933333,1,0.833333,0.866667,1,0.785714,0.882353,0.9375,0.882353,1,0.875,0.736842,0.681818,0.6,0.928571] predictive_accuracy 0.833125 prior_entropy 6.6438561897747395 recall 0.833125 [0.75,0.9375,1,1,0.875,0.75,1,1,0.875,0.9375,0.75,1,0.9375,0.5625,1,0.8125,1,0.4375,0.5625,0.125,0.75,0.9375,0.9375,1,0.875,0.5,0.5,1,0.875,1,1,1,0.9375,0.625,0.8125,0.9375,0.6875,0.375,0.9375,1,0.75,0.9375,1,1,0.9375,0.9375,0.75,1,0.875,0.8125,0.9375,0.8125,0.8125,0.6875,0.75,0.3125,1,0.9375,0.625,0.8125,0.875,1,0.6875,1,0.875,0.6875,0.5625,1,0.8125,0.6875,0.8125,0.75,0.75,0.9375,0.8125,0.9375,0.9375,0.4375,0.9375,1,0.8125,0.875,0.875,0.8125,0.875,0.875,0.9375,0.9375,0.8125,1,0.6875,0.9375,0.9375,0.9375,0.9375,0.875,0.875,0.9375,0.375,0.8125] relative_absolute_error 0.1685606060606049 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.05777110004145657 root_relative_squared_error 0.5806214017078684 total_cost 0 area_under_roc_curve 0.8959477748586895 [0.993711,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,0.5,1,0.5,1,0.75,0.746835,0.996855,0.5,1,0.993711,1,1,0.746835,0.75,0.996835,1,1,1,1,0.746835,0.496835,0.75,0.996855,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.993711,1,0.75,1,1,1,1,0.996855,0.493711,1,1,0.5,1,1,0.996855,0.75,1,1,1,0.746835,1,1,0.5,0.75,1,1,1,0.996855,0.996835,0.75,0.5,1,1,1,0.996835,1,0.996855,1,0.75,0.75,1,0.75,1,0.746835,1,1,0.996835,1,0.746835,0.496855,0.993671,0.496835,1] area_under_roc_curve 0.9400326407133187 [1,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.5,0.75,0.493711,1,1,0.996855,1,1,0.746835,0.493671,1,1,1,1,1,1,1,0.75,0.996855,0.996855,0.996855,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.75,1,0.496855,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.746835,1,1,1,0.746835,1,1,1,0.75,1,0.75,1,1,1,1,1,1,0.996835,0.75,0.496855] area_under_roc_curve 0.9178807419791417 [0.746835,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.75,1,0.496835,1,1,0.75,1,1,0.743671,0.996835,1,1,1,1,1,1,0.496835,1,0.996855,0.993711,0.996855,1,1,0.746835,0.5,1,0.996855,0.996835,1,0.75,1,0.75,1,0.996835,1,1,0.75,1,0.75,1,1,1,0.746835,0.5,1,0.75,1,0.996835,0.993671,0.75,1,0.5,0.75,1,0.996835,0.993711,1,1,1,1,0.496835,0.75,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,0.996855,1,1,1,1,1,0.996835,0.5,1] area_under_roc_curve 0.9022967916567148 [0.75,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,0.75,1,1,1,0.5,0.490566,0.5,1,1,0.996835,1,0.5,0.746835,1,1,1,1,1,1,1,0.75,1,0.996855,0.996855,0.484277,0.996855,1,1,0.996855,1,0.996855,1,1,0.75,1,0.996835,1,1,1,1,0.75,1,0.75,1,1,0.996855,1,1,1,0.996835,1,0.75,0.746835,0.490506,1,1,0.996835,0.5,0.75,0.490566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,1,1,1,1,1,1,0.75,1,0.996855,1,1,0.996855,0.75,0.996835,0.996855,0.75] area_under_roc_curve 0.9242297587771672 [0.996835,1,1,1,1,0.75,1,1,1,1,1,1,1,0.496855,1,1,1,0.746835,0.996855,0.743671,1,1,1,1,1,0.996835,0.5,1,0.996855,1,1,1,1,0.746835,1,1,0.996835,0.746835,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.496855,0.75,1,0.746835,0.5,1,0.996835,0.75,1,1,1,0.5,1,0.75,1,0.496855,1,0.75,0.75,1,1,0.996855,1,0.75,1,0.996835,0.75,1,1,1,1,1,0.993671,0.996855,1,1,1,1,1,1,0.75,1,1,0.75,1,0.996835,0.996855,0.496855,1] area_under_roc_curve 0.9241899530292172 [0.75,1,1,1,1,0.75,1,1,0.996855,0.75,0.75,0.996835,0.5,1,1,1,1,0.993671,1,0.5,0.75,0.75,0.993671,1,0.996835,0.993671,0.746835,1,1,1,1,1,1,0.996835,1,0.75,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,1,1,0.75,0.75,1,1,1,1,0.996835,1,1,1,0.75,1,1,1,0.993711,1,1,0.5,0.996855,1,1,1,1,0.75,1,1,0.75,1,1,0.75,1,1,1,0.75,1,0.996835,1,1,1,0.996855,0.996835,1,0.5,1,1,1,1,1,0.996835,0.996855,0.5,1] area_under_roc_curve 0.9052623198789904 [0.996835,1,1,1,1,0.75,1,1,0.996855,1,1,1,1,1,1,0.75,1,0.993711,0.490506,0.5,0.746835,1,1,1,1,0.5,0.493711,1,1,1,0.996835,1,1,0.996855,1,1,0.490506,0.5,0.990506,1,0.996835,1,1,1,0.5,0.75,0.5,1,1,1,1,0.996835,1,1,0.75,0.5,1,1,0.5,1,0.993671,0.996835,0.996855,0.996835,1,0.5,0.996855,1,1,1,0.996855,1,1,1,0.996835,1,1,0.5,1,1,0.75,1,1,0.996835,1,1,1,0.5,0.5,1,0.996855,1,0.75,1,1,1,1,1,0.5,1] area_under_roc_curve 0.9180996735928666 [0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,0.990566,0.996835,0.5,0.75,1,1,1,0.75,0.490566,1,1,0.5,1,1,1,1,1,1,1,0.993671,0.75,1,0.996855,0.5,1,1,1,1,1,1,1,0.996855,0.5,0.996855,0.75,0.75,0.996855,1,0.5,1,1,1,0.990566,1,1,1,1,1,0.496855,0.996855,1,1,0.746835,1,1,0.996835,1,0.996835,1,1,0.493711,1,1,0.75,1,0.75,1,0.75,0.75,1,0.996855,1,1,0.5,1,1,0.996855,1,1,1,1,0.746835,0.75] area_under_roc_curve 0.9274938301090676 [0.996855,1,1,1,1,0.996855,1,1,0.75,1,1,1,1,1,1,1,1,0.493711,1,0.493711,1,1,1,1,1,0.496855,0.993711,0.996835,1,1,1,1,1,0.996855,1,1,0.75,0.5,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.5,0.5,1,0.5,0.993671,1,1,1,1,0.996835,1,1,0.746835,1,0.996835,0.496855,1,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,0.996835,1,1,1,1,1,1,1,0.75,0.996855,1,0.75,1] area_under_roc_curve 0.9023365974046651 [1,1,1,1,1,1,1,1,1,1,0.996855,1,1,0.75,1,1,1,0.490566,0.5,0.493711,1,1,1,1,1,0.5,0.5,0.996835,0.75,1,1,1,1,0.996855,0.75,1,0.746835,0.5,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,1,0.75,1,0.75,1,0.993711,1,1,0.746835,0.75,0.75,1,0.496835,1,0.993711,0.993711,0.746835,1,0.75,0.996855,1,0.5,0.496835,0.75,0.75,0.75,1,1,1,1,1,0.490566,1,0.496855,1,0.996835,1,1,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,0.75,1,1] 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 0.7915762049500651 kappa 0.8799605133267522 kappa 0.8357289527720739 kappa 0.804231133564888 kappa 0.848365187174222 kappa 0.848365187174222 kappa 0.8104489989337756 kappa 0.8357873129909604 kappa 0.8546890424481737 kappa 0.8042234063548451 kb_relative_information_score 126.92798071085957 kb_relative_information_score 140.95853434867672 kb_relative_information_score 133.94325752976812 kb_relative_information_score 128.93234551626205 kb_relative_information_score 135.9476223351706 kb_relative_information_score 135.9476223351706 kb_relative_information_score 129.93452791896325 kb_relative_information_score 133.94325752976815 kb_relative_information_score 136.94980473787183 kb_relative_information_score 128.93234551626202 mean_absolute_error 0.004125000000000002 mean_absolute_error 0.0023750000000000004 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.0038750000000000013 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.003750000000000001 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.002875000000000001 mean_absolute_error 0.0038750000000000013 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 0.79375 predictive_accuracy 0.88125 predictive_accuracy 0.8375 predictive_accuracy 0.80625 predictive_accuracy 0.85 predictive_accuracy 0.85 predictive_accuracy 0.8125 predictive_accuracy 0.8375 predictive_accuracy 0.85625 predictive_accuracy 0.80625 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 0.79375 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0.5,0.5,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,1,0.5,0,1,0,1] recall 0.88125 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,0] recall 0.8375 [0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0,1,0.5,1,1,1,0.5,1,0,0.5,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.80625 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5] recall 0.85 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0.5,0,1,1,0.5,1,1,1,0,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1] recall 0.85 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,1,1,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1] recall 0.8125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.8375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,0,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0.5,0.5] recall 0.85625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1] recall 0.80625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,0.5,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1] relative_absolute_error 0.20833333333333307 relative_absolute_error 0.11994949494949476 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.19570707070707044 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.18939393939393911 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.14520202020202 relative_absolute_error 0.19570707070707044 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 0.06422616289332567 root_mean_squared_error 0.048733971724044825 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.06224949798994367 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.061237243569579464 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.05361902647381805 root_mean_squared_error 0.06224949798994367 root_relative_squared_error 0.6454972243679026 root_relative_squared_error 0.48979484470438195 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.6256309946079566 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.6154574548966634 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.5388914922357191 root_relative_squared_error 0.6256309946079566 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 439.5514859999139 usercpu_time_millis 422.35951200007094 usercpu_time_millis 432.28658399993947 usercpu_time_millis 419.2329139999629 usercpu_time_millis 430.16927500013935 usercpu_time_millis 440.2506070000527 usercpu_time_millis 440.86456100001215 usercpu_time_millis 445.5062880000469 usercpu_time_millis 422.4788809999609 usercpu_time_millis 428.692367000167 usercpu_time_millis_testing 54.68253099991216 usercpu_time_millis_testing 52.89536700001918 usercpu_time_millis_testing 55.10897399994974 usercpu_time_millis_testing 54.3793449999157 usercpu_time_millis_testing 55.299549000096704 usercpu_time_millis_testing 54.907459999981256 usercpu_time_millis_testing 55.618624999965505 usercpu_time_millis_testing 71.4745090000406 usercpu_time_millis_testing 53.82531400005064 usercpu_time_millis_testing 54.13916200006952 usercpu_time_millis_training 384.86895500000173 usercpu_time_millis_training 369.46414500005176 usercpu_time_millis_training 377.1776099999897 usercpu_time_millis_training 364.8535690000472 usercpu_time_millis_training 374.86972600004265 usercpu_time_millis_training 385.34314700007144 usercpu_time_millis_training 385.24593600004664 usercpu_time_millis_training 374.0317790000063 usercpu_time_millis_training 368.65356699991025 usercpu_time_millis_training 374.5532050000975