8956754 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) 6892368 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 6.089200937852132 7650 cache_size 200 7650 class_weight null 7650 coef0 0.3803126460647601 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.0035098561751672245 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking true 7650 tol 0.0003320860131175292 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 18828794 description https://api.openml.org/data/download/18828794/description.xml -1 18828795 predictions https://api.openml.org/data/download/18828795/predictions.arff area_under_roc_curve 0.9191919191919191 [0.904356,0.968434,1,1,0.9375,0.874684,1,1,0.936869,0.96875,0.905934,0.999684,1,0.843434,1,0.90625,1,0.746528,0.716856,0.558712,0.905934,0.96875,0.966856,1,0.968119,0.714962,0.747159,0.967803,0.9375,1,0.999684,1,0.999369,0.779356,0.905619,0.967487,0.903093,0.714646,0.967803,0.999684,0.842487,0.968119,1,0.999053,0.968434,0.96875,0.905934,0.968119,0.904672,0.905934,0.936553,0.905619,0.90625,0.874053,0.811553,0.71654,1,0.968434,0.841856,0.904672,0.968119,0.968434,0.873422,0.999053,0.999369,0.842487,0.622159,1,0.874369,0.873737,0.905934,0.875,0.90404,0.96875,0.905303,0.968434,0.968434,0.81029,0.96875,0.999684,0.9375,0.905619,0.90625,0.904356,0.936869,0.937184,0.96875,0.936237,0.968434,1,0.874369,0.999369,0.968434,0.968119,0.96875,0.937184,0.936553,0.966856,0.686553,0.937184] average_cost 0 f_measure 0.8398360917057656 [0.742857,0.9375,1,1,0.933333,0.827586,1,1,0.875,0.967742,0.866667,0.969697,1,0.785714,1,0.896552,1,0.457143,0.482759,0.133333,0.866667,0.967742,0.810811,1,0.909091,0.4,0.484848,0.882353,0.933333,1,0.969697,1,0.941176,0.580645,0.83871,0.857143,0.666667,0.388889,0.882353,0.969697,0.709677,0.909091,1,0.914286,0.9375,0.967742,0.866667,0.909091,0.764706,0.866667,0.848485,0.83871,0.896552,0.774194,0.689655,0.466667,1,0.9375,0.666667,0.764706,0.909091,0.9375,0.727273,0.914286,0.941176,0.709677,0.275862,1,0.8,0.75,0.866667,0.857143,0.722222,0.967742,0.8125,0.9375,0.9375,0.606061,0.967742,0.969697,0.933333,0.83871,0.896552,0.742857,0.875,0.903226,0.967742,0.823529,0.9375,1,0.8,0.941176,0.9375,0.909091,0.967742,0.903226,0.848485,0.810811,0.48,0.903226] kappa 0.8383838383838383 kb_relative_information_score 1343.4413049084772 mean_absolute_error 0.0031999999999999867 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 0.8461717897890835 [0.684211,0.9375,1,1,1,0.923077,1,1,0.875,1,0.928571,0.941176,1,0.916667,1,1,1,0.421053,0.538462,0.142857,0.928571,1,0.714286,1,0.882353,0.368421,0.470588,0.833333,1,1,0.941176,1,0.888889,0.6,0.866667,0.789474,0.565217,0.35,0.833333,0.941176,0.733333,0.882353,1,0.842105,0.9375,1,0.928571,0.882353,0.722222,0.928571,0.823529,0.866667,1,0.8,0.769231,0.5,1,0.9375,0.647059,0.722222,0.882353,0.9375,0.705882,0.842105,0.888889,0.733333,0.307692,1,0.857143,0.75,0.928571,1,0.65,1,0.8125,0.9375,0.9375,0.588235,1,0.941176,1,0.866667,1,0.684211,0.875,0.933333,1,0.777778,0.9375,1,0.857143,0.888889,0.9375,0.882353,1,0.933333,0.823529,0.714286,0.666667,0.933333] predictive_accuracy 0.84 prior_entropy 6.6438561897747395 recall 0.84 [0.8125,0.9375,1,1,0.875,0.75,1,1,0.875,0.9375,0.8125,1,1,0.6875,1,0.8125,1,0.5,0.4375,0.125,0.8125,0.9375,0.9375,1,0.9375,0.4375,0.5,0.9375,0.875,1,1,1,1,0.5625,0.8125,0.9375,0.8125,0.4375,0.9375,1,0.6875,0.9375,1,1,0.9375,0.9375,0.8125,0.9375,0.8125,0.8125,0.875,0.8125,0.8125,0.75,0.625,0.4375,1,0.9375,0.6875,0.8125,0.9375,0.9375,0.75,1,1,0.6875,0.25,1,0.75,0.75,0.8125,0.75,0.8125,0.9375,0.8125,0.9375,0.9375,0.625,0.9375,1,0.875,0.8125,0.8125,0.8125,0.875,0.875,0.9375,0.875,0.9375,1,0.75,1,0.9375,0.9375,0.9375,0.875,0.875,0.9375,0.375,0.875] relative_absolute_error 0.16161616161616066 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.056568542494923685 root_relative_squared_error 0.5685352436149594 total_cost 0 area_under_roc_curve 0.9022370830347903 [0.993711,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,1,1,0.5,1,0.75,0.746835,0.493711,0.5,1,0.993711,1,1,0.746835,0.746835,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,1,1,1,1,0.996855,0.496855,1,1,0.5,1,1,1,0.75,1,1,1,0.496835,1,1,0.5,0.75,1,1,1,0.996855,0.996835,0.75,0.5,1,1,1,0.996835,1,0.996855,1,0.75,0.75,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.496855,0.993671,0.496835,1] area_under_roc_curve 0.9337035267892683 [1,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.746835,0.75,0.496855,1,1,0.996855,1,1,0.746835,0.490506,1,1,1,1,1,1,1,0.75,0.996855,0.996855,0.496855,1,1,1,1,1,1,1,1,1,1,0.746835,1,0.993671,1,1,0.75,1,0.496855,1,1,0.996855,0.75,1,1,1,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.75,0.75,1,1,0.746835,1,1,0.996835,1,1,0.75,1,1,1,1,1,1,0.996835,0.75,0.496855] area_under_roc_curve 0.930538969827243 [1,1,1,1,1,1,1,1,1,1,0.75,1,1,0.75,1,1,1,0.743671,1,0.490506,1,1,1,1,1,0.743671,1,1,1,1,1,1,1,0.496835,0.996855,0.996855,0.993711,0.5,1,1,0.746835,0.5,1,0.996855,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,0.75,1,1,1,0.746835,1,1,1,0.996855,1,1,0.5,1,0.5,0.75,1,1,0.996855,1,1,1,1,0.746835,0.75,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,1,1,1,1,0.996835,0.5,1] area_under_roc_curve 0.9148356022609663 [0.75,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,0.75,1,1,1,0.743671,0.493711,0.5,1,1,0.996835,1,1,0.746835,1,1,1,1,1,1,1,0.75,1,0.996855,1,0.490566,1,1,1,0.996855,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.75,1,1,0.996855,1,1,1,0.996835,1,1,0.746835,0.490506,1,1,0.996835,0.5,0.75,0.490566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,1,1,1,1,1,1,0.75,1,0.996855,1,1,0.996855,0.75,0.996835,0.996855,0.75] area_under_roc_curve 0.921005493193217 [0.996835,1,1,1,1,0.75,1,1,1,1,1,1,1,0.5,1,1,1,0.496835,0.996855,0.743671,1,1,1,1,1,0.996835,0.5,1,1,1,1,1,1,0.746835,1,1,0.996835,0.743671,1,1,0.746835,1,1,1,1,1,1,1,1,1,1,0.496855,0.75,1,0.496835,0.5,1,0.996835,0.75,1,1,1,0.5,1,1,1,0.5,1,0.75,0.746835,0.996835,1,0.996855,1,0.75,1,0.996835,0.746835,1,1,1,1,1,0.993671,0.996855,1,1,1,1,1,1,1,1,1,0.75,1,0.996835,0.996855,0.496855,1] area_under_roc_curve 0.921045298941167 [0.75,0.996855,1,1,1,0.75,1,1,1,0.75,1,0.996835,1,1,1,1,1,0.996835,1,0.75,0.75,0.75,0.993671,1,0.996835,0.743671,0.746835,0.5,1,1,1,1,1,0.746835,1,0.75,0.75,0.743671,1,1,1,1,1,0.996855,1,1,1,1,0.75,0.75,1,1,1,0.996855,0.996835,0.993671,1,1,0.75,0.996855,1,1,0.993711,1,1,0.5,0.996855,1,0.746835,1,1,0.75,1,1,0.75,1,1,1,1,1,1,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,1,0.5,1,1,1,1,1,1,1,0.5,1] area_under_roc_curve 0.9241700501552422 [0.996835,1,1,1,1,0.75,1,1,0.996855,1,1,1,1,1,1,0.75,1,1,0.493671,0.5,0.996835,1,1,1,0.996835,0.5,0.493711,1,1,1,0.996835,1,1,1,1,1,0.743671,0.746835,0.990506,1,1,1,1,1,0.5,0.75,0.5,0.996835,0.996855,1,1,1,1,1,0.75,0.75,1,1,0.5,1,0.993671,1,0.996855,0.996835,1,0.5,0.996855,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,1,1,0.75,1,1,0.75,1,1,0.5,1] area_under_roc_curve 0.9148953108828912 [0.75,1,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,0.75,1,0.996855,0.75,0.5,0.75,1,1,1,0.75,0.5,0.996855,1,0.5,1,1,1,0.996855,1,1,1,0.993671,0.993671,1,0.996855,0.5,1,1,1,1,1,1,0.75,0.996855,0.5,0.496855,0.75,0.75,0.996855,1,0.743671,1,1,1,0.990566,1,0.996835,1,1,1,0.496855,0.496855,1,1,0.746835,1,1,0.996835,1,0.996835,1,1,0.493711,1,1,0.75,1,0.75,1,0.75,0.75,1,0.996855,1,1,0.5,1,1,0.996855,1,1,1,1,0.75,1] area_under_roc_curve 0.930598678449168 [0.996855,1,1,1,1,0.996855,1,1,0.75,1,1,1,1,1,1,1,1,0.496855,0.75,0.496855,1,1,0.5,1,1,0.493711,0.996855,0.996835,1,1,1,1,1,0.996855,0.996835,1,1,0.496835,0.75,1,0.5,1,1,0.996835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.5,0.5,1,0.5,0.993671,1,1,1,1,0.996835,1,1,0.743671,1,0.996835,0.996855,1,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1] area_under_roc_curve 0.8991521375686649 [0.996855,1,1,1,1,1,1,1,1,1,0.996855,1,1,0.75,1,1,1,0.493711,0.5,0.490566,1,1,1,1,1,0.493711,0.5,0.996835,0.75,1,1,1,1,0.996855,0.75,1,0.746835,0.75,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,1,0.75,1,0.746835,0.75,0.996855,1,1,0.993671,0.75,0.75,0.75,0.496835,1,0.993711,0.993711,0.5,1,0.75,1,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,1,0.996835,1,0.746835,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,0.75,1,1] 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 f_measure 0.8699999999999998 [1,1,1,1,1,1,1,1,1,1,0.666667,1,1,0.666667,1,1,1,0.5,0.666667,0,1,1,0.666667,1,1,0.5,0,1,1,1,1,1,1,1,0.666667,0.666667,0.666667,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.666667,1,1,0.666667,1,0,1,1,0.666667,0.666667,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,0.666667,0.666667,1,1,0.5,1,1,0.8,1,1,0.666667,1,1,1,1,1,1,0.8,0.666667,0] kappa 0.8041847611527833 kappa 0.8673195387774444 kappa 0.8610014215763703 kappa 0.8294579763925625 kappa 0.8420283559101142 kappa 0.8420533070088845 kappa 0.8483472216737096 kappa 0.8294445102451735 kappa 0.8609904430929627 kappa 0.7979000552617037 kb_relative_information_score 128.93234551626205 kb_relative_information_score 138.95416954327428 kb_relative_information_score 137.951987140573 kb_relative_information_score 132.94107512706694 kb_relative_information_score 134.94543993246936 kb_relative_information_score 134.94543993246936 kb_relative_information_score 135.94762233517056 kb_relative_information_score 132.94107512706694 kb_relative_information_score 137.951987140573 kb_relative_information_score 127.93016311356081 mean_absolute_error 0.0038750000000000013 mean_absolute_error 0.0026250000000000006 mean_absolute_error 0.0027500000000000007 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.0027500000000000007 mean_absolute_error 0.004000000000000002 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 0.9041666666666666 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,1,0.666667,1,0] predictive_accuracy 0.80625 predictive_accuracy 0.86875 predictive_accuracy 0.8625 predictive_accuracy 0.83125 predictive_accuracy 0.84375 predictive_accuracy 0.84375 predictive_accuracy 0.85 predictive_accuracy 0.83125 predictive_accuracy 0.8625 predictive_accuracy 0.8 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 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0.5,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1] recall 0.86875 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0] recall 0.8625 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0,1,0,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.83125 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5] recall 0.84375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,0,1,1,0.5,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1] recall 0.84375 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1] recall 0.85 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1] recall 0.83125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1] recall 0.8625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1] recall 0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,0.5,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1] relative_absolute_error 0.19570707070707044 relative_absolute_error 0.13257575757575737 relative_absolute_error 0.1388888888888887 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.1388888888888887 relative_absolute_error 0.2020202020202018 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 0.051234753829797995 root_mean_squared_error 0.052440442408507586 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.052440442408507586 root_mean_squared_error 0.0632455532033676 root_relative_squared_error 0.6256309946079566 root_relative_squared_error 0.5149286505444369 root_relative_squared_error 0.5270462766947296 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5270462766947296 root_relative_squared_error 0.6356417261637279 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 427.8048190000163 usercpu_time_millis 427.2934409999891 usercpu_time_millis 427.69632499999943 usercpu_time_millis 421.16390999999 usercpu_time_millis 418.56884999998556 usercpu_time_millis 427.04482400000643 usercpu_time_millis 422.8968209999948 usercpu_time_millis 421.6919940000139 usercpu_time_millis 417.3211070000207 usercpu_time_millis 418.84369299998525 usercpu_time_millis_testing 54.475192000012385 usercpu_time_millis_testing 53.98639500000968 usercpu_time_millis_testing 53.22764400000324 usercpu_time_millis_testing 53.52263300000004 usercpu_time_millis_testing 53.54534699998226 usercpu_time_millis_testing 57.42090200001826 usercpu_time_millis_testing 53.11616099999128 usercpu_time_millis_testing 52.76343900001734 usercpu_time_millis_testing 52.73782200001165 usercpu_time_millis_testing 53.59882999999854 usercpu_time_millis_training 373.32962700000394 usercpu_time_millis_training 373.30704599997944 usercpu_time_millis_training 374.4686809999962 usercpu_time_millis_training 367.64127699998994 usercpu_time_millis_training 365.0235030000033 usercpu_time_millis_training 369.62392199998817 usercpu_time_millis_training 369.78066000000354 usercpu_time_millis_training 368.92855499999655 usercpu_time_millis_training 364.583285000009 usercpu_time_millis_training 365.2448629999867