8958186 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) 6893794 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 169.3839088640749 7650 cache_size 200 7650 class_weight null 7650 coef0 0.620207650889114 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.0700115965229378 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking true 7650 tol 0.003025733011574462 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 18831640 description https://api.openml.org/data/download/18831640/description.xml -1 18831641 predictions https://api.openml.org/data/download/18831641/predictions.arff area_under_roc_curve 0.9160353535353535 [0.874369,0.96875,0.9375,0.964015,0.9375,0.841856,1,0.96875,0.874684,0.9375,0.968434,0.968434,0.96875,0.90625,0.933396,0.968434,1,0.747159,0.746528,0.651515,0.905619,0.9375,0.967172,0.96875,0.937184,0.747475,0.71654,0.967172,0.936869,1,0.968434,0.96875,0.999369,0.809975,0.936869,0.936553,0.778725,0.71654,0.968119,0.968434,0.904987,0.968119,1,0.967803,0.9375,0.96875,0.936869,0.999369,0.936237,0.905934,0.874053,0.905303,0.874684,0.843119,0.936237,0.748106,0.96875,0.968434,0.81029,0.873422,0.999369,0.968434,0.842803,0.999369,0.936869,0.842487,0.717172,1,0.968119,0.936553,0.874053,0.936869,0.93529,0.9375,0.905619,0.96875,0.968434,0.810922,0.96875,0.968434,0.937184,0.905619,0.875,0.810922,0.905619,0.968434,1,0.936869,0.937184,0.96875,0.905619,0.968434,0.9375,0.937184,0.96875,0.937184,0.873737,0.967803,0.810922,0.937184] average_cost 0 f_measure 0.8361291147481726 [0.8,0.967742,0.933333,0.652174,0.933333,0.666667,1,0.967742,0.827586,0.933333,0.9375,0.9375,0.967742,0.896552,0.651163,0.9375,1,0.484848,0.457143,0.277778,0.83871,0.933333,0.833333,0.967742,0.903226,0.5,0.466667,0.833333,0.875,1,0.9375,0.967742,0.941176,0.588235,0.875,0.848485,0.545455,0.466667,0.909091,0.9375,0.787879,0.909091,1,0.882353,0.933333,0.967742,0.875,0.941176,0.823529,0.866667,0.774194,0.8125,0.827586,0.758621,0.823529,0.533333,0.967742,0.9375,0.606061,0.727273,0.941176,0.9375,0.733333,0.941176,0.875,0.709677,0.5,1,0.909091,0.848485,0.774194,0.875,0.756757,0.933333,0.83871,0.967742,0.9375,0.645161,0.967742,0.9375,0.903226,0.83871,0.857143,0.645161,0.83871,0.9375,1,0.875,0.903226,0.967742,0.83871,0.9375,0.933333,0.903226,0.967742,0.903226,0.75,0.882353,0.645161,0.903226] kappa 0.8320707070707071 kb_relative_information_score 1333.419480881466 mean_absolute_error 0.0033249999999999842 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 0.8449973598526229 [0.857143,1,1,0.5,1,0.647059,1,1,0.923077,1,0.9375,0.9375,1,1,0.518519,0.9375,1,0.470588,0.421053,0.25,0.866667,1,0.75,1,0.933333,0.5,0.5,0.75,0.875,1,0.9375,1,0.888889,0.555556,0.875,0.823529,0.529412,0.5,0.882353,0.9375,0.764706,0.882353,1,0.833333,1,1,0.875,0.888889,0.777778,0.928571,0.8,0.8125,0.923077,0.846154,0.777778,0.571429,1,0.9375,0.588235,0.705882,0.888889,0.9375,0.785714,0.888889,0.875,0.733333,0.583333,1,0.882353,0.823529,0.8,0.875,0.666667,1,0.866667,1,0.9375,0.666667,1,0.9375,0.933333,0.866667,1,0.666667,0.866667,0.9375,1,0.875,0.933333,1,0.866667,0.9375,1,0.933333,1,0.933333,0.75,0.833333,0.666667,0.933333] predictive_accuracy 0.83375 prior_entropy 6.6438561897747395 recall 0.83375 [0.75,0.9375,0.875,0.9375,0.875,0.6875,1,0.9375,0.75,0.875,0.9375,0.9375,0.9375,0.8125,0.875,0.9375,1,0.5,0.5,0.3125,0.8125,0.875,0.9375,0.9375,0.875,0.5,0.4375,0.9375,0.875,1,0.9375,0.9375,1,0.625,0.875,0.875,0.5625,0.4375,0.9375,0.9375,0.8125,0.9375,1,0.9375,0.875,0.9375,0.875,1,0.875,0.8125,0.75,0.8125,0.75,0.6875,0.875,0.5,0.9375,0.9375,0.625,0.75,1,0.9375,0.6875,1,0.875,0.6875,0.4375,1,0.9375,0.875,0.75,0.875,0.875,0.875,0.8125,0.9375,0.9375,0.625,0.9375,0.9375,0.875,0.8125,0.75,0.625,0.8125,0.9375,1,0.875,0.875,0.9375,0.8125,0.9375,0.875,0.875,0.9375,0.875,0.75,0.9375,0.625,0.875] relative_absolute_error 0.16792929292929182 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.05766281297335384 root_relative_squared_error 0.5795330757244004 total_cost 0 area_under_roc_curve 0.9022370830347904 [0.996855,0.75,1,0.996855,0.75,0.996855,1,1,0.75,1,0.75,1,1,1,1,0.5,1,0.75,0.496835,0.490566,0.5,1,0.996855,1,1,0.746835,0.743671,0.996835,1,1,1,1,0.996835,0.496835,0.75,0.996855,0.996855,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,0.996835,1,1,1,0.996855,0.496855,1,1,0.496855,1,1,1,0.5,1,1,1,0.5,1,1,0.5,0.75,0.996855,1,1,1,1,0.75,0.5,1,1,1,0.996835,1,1,1,1,1,1,0.75,1,0.996835,1,1,0.996835,1,0.75,0.5,0.993671,0.746835,1] area_under_roc_curve 0.9211647161850168 [1,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.75,0.75,0.996855,1,0.75,0.746835,0.490566,1,1,0.996855,1,1,0.746835,0.493671,1,0.996835,1,1,1,1,0.996835,1,0.496855,0.496855,0.496855,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.75,1,0.496855,1,1,1,0.75,1,1,1,1,1,0.996835,0.75,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.75,0.75,0.75,1,1,0.75,1,1,0.996835,0.75,1,0.75,1,0.75,1,1,1,0.5,1,0.75,0.496855] area_under_roc_curve 0.930618581323143 [1,1,0.75,0.984277,1,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.743671,1,0.743671,1,0.5,0.75,1,1,0.746835,0.75,0.996855,1,1,1,1,1,0.75,0.993711,1,0.996855,0.5,1,1,0.75,1,1,1,1,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.996855,0.496835,1,1,0.75,1,1,1,1,1,1,0.75,1,0.996835,0.996855,1,0.996855,1,1,0.746835,0.75,0.996835,1,1,1,0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,1,1] area_under_roc_curve 0.9116909481729161 [0.75,1,0.75,0.996855,0.75,0.746835,1,1,0.746835,1,1,1,1,1,1,1,1,0.743671,0.493711,0.746835,1,1,0.993671,1,0.5,1,1,1,1,1,1,1,1,0.75,1,1,1,0.496855,1,1,1,0.996855,1,0.996855,1,1,1,1,1,1,0.996835,1,1,0.75,0.996855,0.743671,1,1,0.996855,1,0.996855,1,1,1,0.75,0.5,0.496835,1,1,0.996835,0.5,1,0.990566,1,1,1,1,0.75,1,1,1,1,0.75,0.75,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996855,0.5,0.996835,0.993711,0.75] area_under_roc_curve 0.911631239550991 [0.75,1,1,1,1,0.75,1,0.75,1,1,0.996835,1,1,0.5,0.984277,1,1,0.746835,0.993711,0.746835,1,1,1,1,1,0.993671,0.5,1,0.996855,1,1,0.5,1,0.746835,1,1,0.746835,0.75,1,1,0.996835,1,1,1,0.5,1,1,1,1,1,0.5,0.496855,0.75,0.996855,0.746835,0.5,1,0.996835,0.746835,1,1,1,1,1,0.75,1,1,1,1,1,0.996835,1,1,0.5,0.75,1,0.996835,1,1,1,1,1,0.75,0.746835,0.996855,1,1,1,1,1,1,1,1,0.5,0.75,1,0.993671,1,0.496855,1] area_under_roc_curve 0.9115715309290662 [0.75,1,1,0.75,1,0.75,1,1,1,0.75,1,0.996835,0.5,1,0.990566,1,1,0.996835,0.996855,0.75,0.75,0.75,0.996835,1,0.996835,0.746835,0.746835,0.5,1,1,1,1,1,0.746835,1,0.746835,0.5,0.743671,1,1,0.996835,1,1,0.996855,1,1,0.996835,1,0.75,0.75,1,1,0.75,1,0.996835,0.996835,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.75,1,1,0.996835,1,0.75,1,1,1,0.75,1,1,0.746835,1,1,0.996855,0.75,0.75,0.996835,1,1,1,0.996855,0.996835,0.75,1,1,1,1,1,1,0.996835,1,1,1] area_under_roc_curve 0.921065201815142 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.496855,1,1,1,0.746835,1,1,0.996855,1,1,0.493671,0.746835,0.993671,0.5,1,0.75,1,1,0.5,0.75,0.75,0.996835,1,1,1,0.996835,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,1,0.996855,0.996835,1,0.496855,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.5,1,1,0.996855,1,0.75,1,1,0.75,1,1,0.5,1] area_under_roc_curve 0.9116909481729161 [0.75,1,1,1,1,0.75,1,1,1,1,1,1,1,1,0.990566,1,1,0.496855,0.990506,0.496835,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.746835,0.746835,1,0.996855,0.75,1,1,1,1,1,1,1,0.993711,0.5,0.496855,0.75,0.75,0.996855,1,0.746835,0.75,1,0.996835,0.990566,1,0.996835,1,1,1,0.496855,0.5,1,1,0.996835,1,1,0.996835,1,0.996835,1,1,0.496855,1,1,0.75,1,0.75,0.75,0.75,0.75,1,0.5,1,1,1,0.75,1,1,1,1,1,1,0.996835,1] area_under_roc_curve 0.9243093702730674 [0.996855,1,1,0.993671,1,0.993711,1,1,0.75,1,1,1,1,1,0.75,1,1,0.493711,1,0.496855,1,1,1,0.75,1,0.496855,0.996855,0.993671,1,1,1,1,1,0.996855,1,1,1,0.5,0.75,1,0.5,1,1,0.746835,1,1,0.996855,1,1,1,0.75,0.996835,0.75,0.75,0.75,0.5,1,0.5,0.746835,1,1,1,1,0.996835,1,1,0.743671,1,0.996835,1,0.996855,0.5,0.75,1,1,1,1,0.996855,1,1,1,1,1,0.496855,1,1,1,1,1,1,0.75,1,1,1,1,1,1,1,0.75,1] area_under_roc_curve 0.9148953108828914 [1,1,1,0.993671,1,0.996855,1,1,0.75,0.5,1,1,1,0.75,1,1,1,0.5,0.5,0.990566,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.993711,0.75,1,0.996835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.75,1,1,1,1,0.996835,0.75,1,1,0.496835,1,0.993711,0.996855,0.5,1,0.75,0.996855,1,0.5,0.746835,0.75,0.75,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,0.996835,1,1,1,1,1,0.996835,1,0.75,1,1,0.996855,1,1,1] 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 0.8041847611527833 kappa 0.8420657796027955 kappa 0.8610343466245559 kappa 0.8231416051478425 kappa 0.8230927183699257 kappa 0.8231136731551308 kappa 0.8420345944238212 kappa 0.8231066887783305 kappa 0.8483711747285291 kappa 0.8294512435846823 kb_relative_information_score 128.93234551626202 kb_relative_information_score 134.9454399324694 kb_relative_information_score 137.951987140573 kb_relative_information_score 131.9388927243657 kb_relative_information_score 131.9388927243657 kb_relative_information_score 131.93889272436567 kb_relative_information_score 134.94543993246936 kb_relative_information_score 131.93889272436567 kb_relative_information_score 135.9476223351706 kb_relative_information_score 132.94107512706694 mean_absolute_error 0.0038750000000000013 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.0027500000000000007 mean_absolute_error 0.003500000000000001 mean_absolute_error 0.003500000000000001 mean_absolute_error 0.003500000000000001 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.003500000000000001 mean_absolute_error 0.003000000000000001 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 0.80625 predictive_accuracy 0.84375 predictive_accuracy 0.8625 predictive_accuracy 0.825 predictive_accuracy 0.825 predictive_accuracy 0.825 predictive_accuracy 0.84375 predictive_accuracy 0.825 predictive_accuracy 0.85 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 0.80625 [1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,0,1,0.5,0] recall 0.8625 [1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,0,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1] recall 0.825 [0.5,1,0.5,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5] recall 0.825 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1] recall 0.825 [0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1] recall 0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,0.5,1,0,1,0.5,1,1,0,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0,1] recall 0.825 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,0,0.5,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1] recall 0.85 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1] 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 0.19570707070707044 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.1388888888888887 relative_absolute_error 0.17676767676767655 relative_absolute_error 0.17676767676767655 relative_absolute_error 0.17676767676767655 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.17676767676767655 relative_absolute_error 0.1515151515151513 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 0.06224949798994367 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.052440442408507586 root_mean_squared_error 0.05916079783099617 root_mean_squared_error 0.05916079783099617 root_mean_squared_error 0.05916079783099617 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.05916079783099617 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.058094750193111264 root_relative_squared_error 0.6256309946079566 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.5270462766947296 root_relative_squared_error 0.5945883900105629 root_relative_squared_error 0.5945883900105629 root_relative_squared_error 0.5945883900105629 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.5945883900105629 root_relative_squared_error 0.5504818825631801 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 710.2111199998262 usercpu_time_millis 710.4735310000478 usercpu_time_millis 713.2256250000637 usercpu_time_millis 729.7495349999963 usercpu_time_millis 704.6213339999667 usercpu_time_millis 734.6384840000155 usercpu_time_millis 744.7043349999376 usercpu_time_millis 740.4356010000583 usercpu_time_millis 730.7175299999926 usercpu_time_millis 728.085845999999 usercpu_time_millis_testing 53.08420299991212 usercpu_time_millis_testing 55.02969600001961 usercpu_time_millis_testing 52.82073600005788 usercpu_time_millis_testing 57.010015999935604 usercpu_time_millis_testing 53.7075280000181 usercpu_time_millis_testing 56.07348199998796 usercpu_time_millis_testing 56.07502899999872 usercpu_time_millis_testing 55.87577699998292 usercpu_time_millis_testing 54.29441399996904 usercpu_time_millis_testing 54.59722199998396 usercpu_time_millis_training 657.1269169999141 usercpu_time_millis_training 655.4438350000282 usercpu_time_millis_training 660.4048890000058 usercpu_time_millis_training 672.7395190000607 usercpu_time_millis_training 650.9138059999486 usercpu_time_millis_training 678.5650020000276 usercpu_time_millis_training 688.6293059999389 usercpu_time_millis_training 684.5598240000754 usercpu_time_millis_training 676.4231160000236 usercpu_time_millis_training 673.4886240000151