8958879 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) 6894483 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 55.17565768775013 7650 cache_size 200 7650 class_weight null 7650 coef0 0.5392980008130204 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.025512781655979835 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking false 7650 tol 0.0014361242090324337 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 18833016 description https://api.openml.org/data/download/18833016/description.xml -1 18833017 predictions https://api.openml.org/data/download/18833017/predictions.arff area_under_roc_curve 0.9207702020202018 [0.842803,0.96875,1,0.9375,0.937184,0.905303,0.999684,1,0.905934,0.9375,0.967803,0.968434,1,0.90625,0.998422,0.96875,1,0.81029,0.714646,0.620581,0.874053,0.96875,0.967172,1,0.937184,0.746843,0.748422,0.936553,0.937184,1,0.999684,0.96875,0.999369,0.778093,0.905934,0.937184,0.74779,0.716856,0.968434,1,0.935606,0.936869,1,0.936553,0.968434,0.96875,0.968434,0.968119,0.873422,0.905303,0.904672,0.905619,0.937184,0.843119,0.874053,0.809975,1,0.968434,0.903725,0.873422,0.936869,0.968434,0.842803,0.968119,0.968119,0.810606,0.716856,1,0.968119,0.873422,0.874053,0.968434,0.93529,0.96875,0.937184,0.96875,0.968434,0.841856,1,1,0.905934,0.936553,0.905934,0.748737,0.905303,1,1,0.935922,0.937184,1,0.905619,0.999684,0.968434,0.937184,0.96875,0.936869,0.936553,0.905303,0.779987,0.937184] average_cost 0 f_measure 0.8440403116762387 [0.733333,0.967742,1,0.933333,0.903226,0.8125,0.969697,1,0.866667,0.933333,0.882353,0.9375,1,0.896552,0.864865,0.967742,1,0.606061,0.388889,0.235294,0.774194,0.967742,0.833333,1,0.903226,0.470588,0.551724,0.848485,0.903226,1,0.969697,0.967742,0.941176,0.514286,0.866667,0.903226,0.516129,0.482759,0.9375,1,0.777778,0.875,1,0.848485,0.9375,0.967742,0.9375,0.909091,0.727273,0.8125,0.764706,0.83871,0.903226,0.758621,0.774194,0.588235,1,0.9375,0.702703,0.727273,0.875,0.9375,0.733333,0.909091,0.909091,0.625,0.482759,1,0.909091,0.727273,0.774194,0.9375,0.756757,0.967742,0.903226,0.967742,0.9375,0.666667,1,1,0.866667,0.848485,0.866667,0.571429,0.8125,1,1,0.8,0.903226,1,0.83871,0.969697,0.9375,0.903226,0.967742,0.875,0.848485,0.8125,0.62069,0.903226] kappa 0.8415404040404041 kb_relative_information_score 1348.452216921983 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 0.8491212887989206 [0.785714,1,1,1,0.933333,0.8125,0.941176,1,0.928571,1,0.833333,0.9375,1,1,0.761905,1,1,0.588235,0.35,0.222222,0.8,1,0.75,1,0.933333,0.444444,0.615385,0.823529,0.933333,1,0.941176,1,0.888889,0.473684,0.928571,0.933333,0.533333,0.538462,0.9375,1,0.7,0.875,1,0.823529,0.9375,1,0.9375,0.882353,0.705882,0.8125,0.722222,0.866667,0.933333,0.846154,0.8,0.555556,1,0.9375,0.619048,0.705882,0.875,0.9375,0.785714,0.882353,0.882353,0.625,0.538462,1,0.882353,0.705882,0.8,0.9375,0.666667,1,0.933333,1,0.9375,0.647059,1,1,0.928571,0.823529,0.928571,0.666667,0.8125,1,1,0.736842,0.933333,1,0.866667,0.941176,0.9375,0.933333,1,0.875,0.823529,0.8125,0.692308,0.933333] predictive_accuracy 0.843125 prior_entropy 6.6438561897747395 recall 0.843125 [0.6875,0.9375,1,0.875,0.875,0.8125,1,1,0.8125,0.875,0.9375,0.9375,1,0.8125,1,0.9375,1,0.625,0.4375,0.25,0.75,0.9375,0.9375,1,0.875,0.5,0.5,0.875,0.875,1,1,0.9375,1,0.5625,0.8125,0.875,0.5,0.4375,0.9375,1,0.875,0.875,1,0.875,0.9375,0.9375,0.9375,0.9375,0.75,0.8125,0.8125,0.8125,0.875,0.6875,0.75,0.625,1,0.9375,0.8125,0.75,0.875,0.9375,0.6875,0.9375,0.9375,0.625,0.4375,1,0.9375,0.75,0.75,0.9375,0.875,0.9375,0.875,0.9375,0.9375,0.6875,1,1,0.8125,0.875,0.8125,0.5,0.8125,1,1,0.875,0.875,1,0.8125,1,0.9375,0.875,0.9375,0.875,0.875,0.8125,0.5625,0.875] 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 0.9148555051349417 [0.996855,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,1,1,0.5,1,0.75,0.496835,0.987421,0.5,1,0.996855,1,1,0.743671,0.75,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,1,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,1,1,1,1,0.996855,0.496855,1,1,1,1,1,1,0.75,1,1,1,0.5,1,1,0.5,0.75,1,1,1,1,1,0.75,0.5,1,1,1,1,1,0.996855,1,1,1,0.996835,0.75,1,0.996835,1,1,0.996835,1,0.75,0.496855,0.993671,0.746835,1] area_under_roc_curve 0.9147360878910913 [1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.75,1,1,1,0.75,0.75,0.487421,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.990506,0.75,0.5,0.496855,0.5,1,1,1,1,1,0.75,1,1,1,1,0.746835,0.996835,0.993671,1,1,0.75,1,0.496855,1,1,1,0.5,1,1,0.75,1,1,0.75,0.75,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.75,0.996835,0.996855,1,0.746835,1,1,0.996835,0.75,1,0.75,1,1,0.75,1,1,1,1,0.75,0.496855] area_under_roc_curve 0.9463617546373694 [1,1,1,1,1,0.746835,1,1,1,1,1,0.75,1,1,0.996835,1,1,0.996835,1,0.5,1,1,1,1,1,0.746835,1,0.996855,1,1,1,1,1,0.75,0.996855,1,0.993711,1,1,1,0.746835,0.5,1,1,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.993711,0.746835,1,1,0.75,0.5,1,1,1,1,1,0.75,1,0.996835,1,1,0.996855,1,1,0.996835,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,0.996855,1] area_under_roc_curve 0.9147957965130162 [0.5,1,1,1,0.75,0.75,1,1,0.746835,1,1,1,1,1,1,1,1,0.743671,0.493711,0.75,1,1,0.993671,1,0.5,1,1,1,1,1,1,1,1,0.75,1,1,1,0.496855,1,1,0.996835,0.996855,1,0.996855,1,1,1,1,0.996835,1,0.996835,1,1,0.75,1,0.993671,1,1,0.996855,1,0.996855,1,1,1,0.75,0.5,0.493671,1,1,0.743671,0.5,1,0.990566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996855,0.75,0.996835,0.996855,0.75] area_under_roc_curve 0.9146962821431416 [0.75,1,1,1,1,0.75,1,1,1,1,0.996835,1,1,0.5,0.996855,1,1,0.746835,0.990566,0.740506,0.75,1,1,1,1,0.990506,0.5,1,1,1,1,0.5,1,0.496835,1,1,0.746835,0.5,1,1,0.746835,1,1,0.5,1,1,1,1,1,1,0.996855,0.496855,0.75,1,0.496835,0.5,1,0.996835,0.993671,1,1,1,0.5,1,1,1,1,1,1,0.996835,0.996835,1,1,1,0.75,1,0.996835,0.996835,1,1,1,1,1,0.75,0.996855,1,1,1,1,1,1,1,1,1,0.75,1,0.996835,1,0.5,1] area_under_roc_curve 0.9147161850171164 [0.75,1,1,0.75,1,1,1,1,1,0.75,1,0.996835,1,1,0.996855,1,1,1,0.996855,0.5,0.75,0.75,0.746835,1,0.996835,0.743671,0.746835,0.5,1,1,1,1,1,0.746835,1,0.75,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,1,1,0.75,0.75,1,1,1,1,0.996835,0.993671,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.746835,1,1,0.996835,1,0.75,1,0.996855,1,0.75,1,1,0.746835,1,1,0.996855,0.746835,0.75,0.746835,1,1,1,0.996855,0.996835,1,1,1,1,1,1,1,1,1,0.5,1] area_under_roc_curve 0.9242297587771674 [1,1,1,1,0.996855,1,1,1,1,1,1,1,1,1,1,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,0.75,1,1,0.996835,1,1,0.996855,1,1,0.493671,0.746835,0.996835,1,1,0.75,1,1,0.5,0.75,1,0.996835,0.996855,0.996855,1,1,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,0.996835,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.75,1,1,0.996835,1,1,1,0.5,1,1,1,1,0.75,1,1,0.75,1,1,0.5,1] area_under_roc_curve 0.9242496616511426 [0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996855,0.984177,0.5,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.5,0.746835,1,1,1,1,1,1,1,1,1,0.75,0.996855,0.5,0.496855,0.75,0.75,0.996855,1,0.746835,1,1,0.996835,0.990566,1,1,1,1,1,0.496855,0.5,1,1,0.743671,1,1,0.996835,1,1,1,1,0.5,1,1,0.75,1,0.75,0.75,0.75,1,1,0.996855,1,1,1,1,0.996835,1,1,0.996835,1,1,0.996835,1] area_under_roc_curve 0.9369277923732187 [0.996855,1,1,0.75,1,0.996855,1,1,0.75,1,1,1,1,1,0.996835,1,1,0.493711,0.75,0.496855,0.996835,1,1,1,1,0.5,0.996855,1,1,1,1,1,1,0.996855,1,1,1,0.5,0.75,1,1,1,1,0.996835,1,1,0.996855,1,0.5,1,0.75,0.996835,1,0.75,0.75,1,1,0.5,0.996835,1,0.75,1,1,0.996835,1,0.996855,0.743671,1,0.996835,1,0.996855,1,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.5,1,1,1,1,1,1,0.75,1,1,1,1,1,1,1,0.75,1] area_under_roc_curve 0.9022967916567153 [0.996855,1,1,1,1,0.996855,1,1,0.75,0.5,0.993711,1,1,0.75,0.996835,1,1,0.5,0.5,0.993711,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.746835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.746835,0.75,0.996855,1,1,0.996835,0.75,0.75,1,0.746835,1,0.993711,0.993711,0.5,1,0.75,1,1,0.5,0.746835,0.75,1,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,1,1,0.746835,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,0.5,1,1] 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 0.8294512435846823 kappa 0.8294040990403981 kappa 0.892600489615415 kappa 0.8294377763739735 kappa 0.8293906243829233 kappa 0.8294243070362474 kappa 0.8483591991470205 kappa 0.8483591991470205 kappa 0.8736426456071076 kappa 0.8042079501046067 kb_relative_information_score 132.94107512706694 kb_relative_information_score 132.94107512706694 kb_relative_information_score 142.96289915407914 kb_relative_information_score 132.94107512706694 kb_relative_information_score 132.94107512706694 kb_relative_information_score 132.94107512706694 kb_relative_information_score 135.9476223351706 kb_relative_information_score 135.9476223351706 kb_relative_information_score 139.95635194597548 kb_relative_information_score 128.93234551626205 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.002125 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.0025000000000000005 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 0.83125 predictive_accuracy 0.83125 predictive_accuracy 0.89375 predictive_accuracy 0.83125 predictive_accuracy 0.83125 predictive_accuracy 0.83125 predictive_accuracy 0.85 predictive_accuracy 0.85 predictive_accuracy 0.875 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 0.83125 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.83125 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,1,0.5,0] recall 0.89375 [1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1] recall 0.83125 [0,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5] recall 0.83125 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0,1,0,1,1,0.5,0,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0,0.5,1,0,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1] recall 0.83125 [0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.85 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1] recall 0.85 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] recall 0.875 [1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,0.5,0.5,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1] recall 0.80625 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0.5,1,1,1,0,1,0.5,1,1,0,0.5,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.17045454545454522 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.10732323232323215 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.17045454545454522 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.12626262626262608 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 0.058094750193111264 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.046097722286464436 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.058094750193111264 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.05 root_mean_squared_error 0.06224949798994367 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.46329954095214076 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.5025189076296057 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 619.9918309998793 usercpu_time_millis 611.1137479999798 usercpu_time_millis 618.3157770001344 usercpu_time_millis 613.3744869998736 usercpu_time_millis 628.5277420001876 usercpu_time_millis 642.7619660000801 usercpu_time_millis 606.3306499997907 usercpu_time_millis 612.8415420000692 usercpu_time_millis 618.3809159999782 usercpu_time_millis 612.939403999917 usercpu_time_millis_testing 55.176130000063495 usercpu_time_millis_testing 56.81996899988917 usercpu_time_millis_testing 57.696982000152275 usercpu_time_millis_testing 54.47556399985842 usercpu_time_millis_testing 55.942204000075435 usercpu_time_millis_testing 58.2032300001174 usercpu_time_millis_testing 55.17468299990469 usercpu_time_millis_testing 55.91144799996073 usercpu_time_millis_testing 57.93328599997949 usercpu_time_millis_testing 54.95494100000542 usercpu_time_millis_training 564.8157009998158 usercpu_time_millis_training 554.2937790000906 usercpu_time_millis_training 560.6187949999821 usercpu_time_millis_training 558.8989230000152 usercpu_time_millis_training 572.5855380001121 usercpu_time_millis_training 584.5587359999627 usercpu_time_millis_training 551.155966999886 usercpu_time_millis_training 556.9300940001085 usercpu_time_millis_training 560.4476299999988 usercpu_time_millis_training 557.9844629999116