8958984 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) 6894588 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 135.16427227649365 7650 cache_size 200 7650 class_weight null 7650 coef0 0.6039285023429594 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.05714270105100835 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking false 7650 tol 0.0026044379165579205 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 18833224 description https://api.openml.org/data/download/18833224/description.xml -1 18833225 predictions https://api.openml.org/data/download/18833225/predictions.arff area_under_roc_curve 0.9210858585858585 [0.874053,0.96875,1,0.935606,0.9375,0.842487,1,1,0.874684,0.9375,0.968434,0.968434,0.96875,0.90625,0.966225,0.96875,1,0.747159,0.746528,0.651515,0.905619,0.96875,0.967172,1,0.937184,0.746528,0.74779,0.967172,0.936869,1,0.968434,0.96875,0.999369,0.778725,0.936869,0.936553,0.77904,0.685606,0.968119,0.999684,0.936237,0.968119,1,0.936553,0.937184,0.96875,0.936869,0.999369,0.935922,0.905934,0.873737,0.905303,0.905934,0.843119,0.936553,0.748106,1,0.968434,0.841856,0.873422,0.999369,0.968434,0.842803,0.999369,0.936869,0.842487,0.717172,1,0.968119,0.905303,0.874369,0.968434,0.93529,0.968434,0.905619,0.96875,0.968434,0.811237,0.96875,0.968434,0.937184,0.936553,0.90625,0.810922,0.905619,0.968434,1,0.936869,0.937184,0.96875,0.936869,0.999684,0.9375,0.968434,0.96875,0.937184,0.936553,0.967803,0.810922,0.937184] average_cost 0 f_measure 0.8445386869699503 [0.774194,0.967742,1,0.777778,0.933333,0.709677,1,1,0.827586,0.933333,0.9375,0.9375,0.967742,0.896552,0.769231,0.967742,1,0.484848,0.457143,0.277778,0.83871,0.967742,0.833333,1,0.903226,0.457143,0.516129,0.833333,0.875,1,0.9375,0.967742,0.941176,0.545455,0.875,0.848485,0.5625,0.428571,0.909091,0.969697,0.823529,0.909091,1,0.848485,0.903226,0.967742,0.875,0.941176,0.8,0.866667,0.75,0.8125,0.866667,0.758621,0.848485,0.533333,1,0.9375,0.666667,0.727273,0.941176,0.9375,0.733333,0.941176,0.875,0.709677,0.5,1,0.909091,0.8125,0.8,0.9375,0.756757,0.9375,0.83871,0.967742,0.9375,0.666667,0.967742,0.9375,0.903226,0.848485,0.896552,0.645161,0.83871,0.9375,1,0.875,0.903226,0.967742,0.875,0.969697,0.933333,0.9375,0.967742,0.903226,0.848485,0.882353,0.645161,0.903226] kappa 0.8421717171717171 kb_relative_information_score 1349.4543993246841 mean_absolute_error 0.0031249999999999885 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 0.8498606633640959 [0.8,1,1,0.7,1,0.733333,1,1,0.923077,1,0.9375,0.9375,1,1,0.652174,1,1,0.470588,0.421053,0.25,0.866667,1,0.75,1,0.933333,0.421053,0.533333,0.75,0.875,1,0.9375,1,0.888889,0.529412,0.875,0.823529,0.5625,0.5,0.882353,0.941176,0.777778,0.882353,1,0.823529,0.933333,1,0.875,0.888889,0.736842,0.928571,0.75,0.8125,0.928571,0.846154,0.823529,0.571429,1,0.9375,0.647059,0.705882,0.888889,0.9375,0.785714,0.888889,0.875,0.733333,0.583333,1,0.882353,0.8125,0.857143,0.9375,0.666667,0.9375,0.866667,1,0.9375,0.714286,1,0.9375,0.933333,0.823529,1,0.666667,0.866667,0.9375,1,0.875,0.933333,1,0.875,0.941176,1,0.9375,1,0.933333,0.823529,0.833333,0.666667,0.933333] predictive_accuracy 0.84375 prior_entropy 6.6438561897747395 recall 0.84375 [0.75,0.9375,1,0.875,0.875,0.6875,1,1,0.75,0.875,0.9375,0.9375,0.9375,0.8125,0.9375,0.9375,1,0.5,0.5,0.3125,0.8125,0.9375,0.9375,1,0.875,0.5,0.5,0.9375,0.875,1,0.9375,0.9375,1,0.5625,0.875,0.875,0.5625,0.375,0.9375,1,0.875,0.9375,1,0.875,0.875,0.9375,0.875,1,0.875,0.8125,0.75,0.8125,0.8125,0.6875,0.875,0.5,1,0.9375,0.6875,0.75,1,0.9375,0.6875,1,0.875,0.6875,0.4375,1,0.9375,0.8125,0.75,0.9375,0.875,0.9375,0.8125,0.9375,0.9375,0.625,0.9375,0.9375,0.875,0.875,0.8125,0.625,0.8125,0.9375,1,0.875,0.875,0.9375,0.875,1,0.875,0.9375,0.9375,0.875,0.875,0.9375,0.625,0.875] relative_absolute_error 0.15782828282828196 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.05590169943749464 root_relative_squared_error 0.5618332187193669 total_cost 0 area_under_roc_curve 0.9053618342488656 [0.996855,0.75,1,0.996855,0.75,0.996855,1,1,0.75,1,0.75,1,1,1,1,0.5,1,0.75,0.496835,0.490566,0.5,1,0.996855,1,1,0.743671,0.743671,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,1,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,0.996835,1,1,1,0.996855,0.496855,1,1,0.5,1,1,1,0.75,1,1,1,0.5,1,1,0.5,0.75,1,1,1,1,1,0.75,0.5,1,1,1,0.996835,1,0.996855,1,1,1,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.5,0.993671,0.746835,1] area_under_roc_curve 0.9242894673990923 [1,1,1,0.990566,1,1,1,1,1,1,1,1,1,0.75,0.75,1,1,0.75,0.746835,0.487421,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.996835,1,0.496855,0.496855,0.5,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.496855,1,1,1,0.75,1,1,0.75,1,1,0.996835,0.75,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.75,0.75,0.996835,1,1,0.75,1,1,0.996835,0.75,1,0.75,1,0.75,1,1,1,1,1,0.75,0.496855] area_under_roc_curve 0.940052543587294 [1,1,1,0.993711,1,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.743671,1,0.746835,1,1,0.75,1,1,0.746835,1,0.996855,1,1,1,1,1,0.75,0.993711,1,0.996855,0.5,1,1,0.75,1,1,1,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.996855,0.496835,1,1,0.75,1,1,1,1,1,1,0.75,1,0.996835,0.996855,1,0.996855,1,1,0.746835,0.75,0.996835,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1] 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 0.9147559907650665 [0.75,1,1,1,1,0.75,1,1,1,1,0.996835,1,1,0.5,0.993711,1,1,0.746835,0.993711,0.746835,1,1,1,1,1,0.987342,0.5,1,0.996855,1,1,0.5,1,0.496835,1,1,0.746835,0.5,1,1,0.996835,1,1,0.5,0.5,1,1,1,1,1,0.496855,0.496855,0.75,0.996855,0.746835,0.5,1,0.996835,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.996835,1,1,1,0.75,1,0.996835,1,1,1,1,1,1,0.746835,0.996855,1,1,1,1,1,1,1,1,1,0.75,1,0.996835,1,0.496855,1] 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 0.9242098559031922 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,1,1,1,0.746835,1,1,0.996855,1,1,0.493671,0.746835,0.993671,1,1,0.75,1,1,0.5,0.75,0.75,0.996835,1,1,1,0.996835,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,1,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,0.996855,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1] 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 [1,1,1,1,1,0.996855,1,1,0.75,0.5,1,1,1,0.75,0.993671,1,1,0.5,0.5,0.990566,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.996835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.75,1,1,1,1,0.996835,0.75,1,1,0.496835,1,0.993711,0.996855,0.5,1,0.75,0.996855,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,0.996835,1,1,1,1,1,0.996835,1,0.75,1,1,0.996855,1,1,1] 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 kappa 0.8104864181933038 kappa 0.8483771618099976 kappa 0.8799747315224258 kappa 0.8294445102451735 kappa 0.8294040990403981 kappa 0.8357354392892399 kappa 0.8483532106468683 kappa 0.8357224657426056 kappa 0.8736326659558504 kappa 0.8294512435846823 kb_relative_information_score 129.93452791896325 kb_relative_information_score 135.9476223351706 kb_relative_information_score 140.95853434867666 kb_relative_information_score 132.94107512706694 kb_relative_information_score 132.94107512706694 kb_relative_information_score 133.94325752976815 kb_relative_information_score 135.9476223351706 kb_relative_information_score 133.94325752976815 kb_relative_information_score 139.95635194597548 kb_relative_information_score 132.94107512706694 mean_absolute_error 0.003750000000000001 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.0023750000000000004 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.003000000000000001 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] predictive_accuracy 0.8125 predictive_accuracy 0.85 predictive_accuracy 0.88125 predictive_accuracy 0.83125 predictive_accuracy 0.83125 predictive_accuracy 0.8375 predictive_accuracy 0.85 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 0.8125 [1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.85 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0] recall 0.88125 [1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1] 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 0.83125 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0,1,0,1,1,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1] 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 0.85 [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 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.18939393939393911 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.11994949494949476 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.1515151515151513 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 0.061237243569579464 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.048733971724044825 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.05 root_mean_squared_error 0.058094750193111264 root_relative_squared_error 0.6154574548966634 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.48979484470438195 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.5504818825631801 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 706.8456019999303 usercpu_time_millis 706.7553830001998 usercpu_time_millis 727.9493390001335 usercpu_time_millis 712.8584980000596 usercpu_time_millis 710.5878019999636 usercpu_time_millis 706.7195499998888 usercpu_time_millis 696.1252130001867 usercpu_time_millis 713.0338839997421 usercpu_time_millis 712.5925279999592 usercpu_time_millis 705.0336680001692 usercpu_time_millis_testing 55.726106000065556 usercpu_time_millis_testing 59.84483000020191 usercpu_time_millis_testing 57.73563100001411 usercpu_time_millis_testing 55.69603799995093 usercpu_time_millis_testing 53.76749300012307 usercpu_time_millis_testing 55.53740300001664 usercpu_time_millis_testing 54.67416500005129 usercpu_time_millis_testing 55.29351399991356 usercpu_time_millis_testing 54.52881299993351 usercpu_time_millis_testing 57.46072000010827 usercpu_time_millis_training 651.1194959998647 usercpu_time_millis_training 646.9105529999979 usercpu_time_millis_training 670.2137080001194 usercpu_time_millis_training 657.1624600001087 usercpu_time_millis_training 656.8203089998406 usercpu_time_millis_training 651.1821469998722 usercpu_time_millis_training 641.4510480001354 usercpu_time_millis_training 657.7403699998285 usercpu_time_millis_training 658.0637150000257 usercpu_time_millis_training 647.5729480000609