8958696 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) 6894300 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 44.63691756528163 7650 cache_size 200 7650 class_weight null 7650 coef0 0.5240082749570865 7650 decision_function_shape "ovr" 7650 degree 3 7650 gamma 0.02108190356121224 7650 kernel "rbf" 7650 max_iter -1 7650 probability false 7650 random_state 1 7650 shrinking false 7650 tol 0.0012474785874141581 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 18832654 description https://api.openml.org/data/download/18832654/description.xml -1 18832655 predictions https://api.openml.org/data/download/18832655/predictions.arff area_under_roc_curve 0.9210858585858582 [0.842487,0.96875,1,0.96875,0.937184,0.936553,0.999684,1,0.905934,0.9375,0.967803,0.937184,1,0.90625,0.999053,0.96875,1,0.81029,0.714646,0.620896,0.874053,0.96875,0.998422,1,0.937184,0.715909,0.779356,0.936553,0.937184,1,0.999684,0.96875,0.999369,0.746528,0.937184,0.936869,0.747475,0.717172,0.968434,1,0.935606,0.936869,1,0.936553,0.968434,0.96875,0.968434,0.968119,0.873422,0.936553,0.904672,0.905619,0.937184,0.843119,0.874053,0.809975,1,0.968434,0.903725,0.842487,0.936869,0.968434,0.842803,0.968119,0.968119,0.810922,0.717172,1,0.936869,0.873422,0.874053,0.968434,0.93529,0.96875,0.936553,0.96875,0.968434,0.841856,1,1,0.905934,0.936553,0.905934,0.748737,0.905303,1,1,0.935922,0.937184,1,0.936869,0.999684,0.968434,0.937184,0.96875,0.936869,0.936553,0.905303,0.748737,0.937184] average_cost 0 f_measure 0.8440670220974889 [0.709677,0.967742,1,0.967742,0.903226,0.848485,0.969697,1,0.866667,0.933333,0.882353,0.903226,1,0.896552,0.914286,0.967742,1,0.606061,0.388889,0.242424,0.774194,0.967742,0.864865,1,0.903226,0.4375,0.580645,0.848485,0.903226,1,0.969697,0.967742,0.941176,0.457143,0.903226,0.875,0.5,0.5,0.9375,1,0.777778,0.875,1,0.848485,0.9375,0.967742,0.9375,0.909091,0.727273,0.848485,0.764706,0.83871,0.903226,0.758621,0.774194,0.588235,1,0.9375,0.702703,0.709677,0.875,0.9375,0.733333,0.909091,0.909091,0.645161,0.5,1,0.875,0.727273,0.774194,0.9375,0.756757,0.967742,0.848485,0.967742,0.9375,0.666667,1,1,0.866667,0.848485,0.866667,0.571429,0.8125,1,1,0.8,0.903226,1,0.875,0.969697,0.9375,0.903226,0.967742,0.875,0.848485,0.8125,0.571429,0.903226] 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 0.8484534852402501 [0.733333,1,1,1,0.933333,0.823529,0.941176,1,0.928571,1,0.833333,0.933333,1,1,0.842105,1,1,0.588235,0.35,0.235294,0.8,1,0.761905,1,0.933333,0.4375,0.6,0.823529,0.933333,1,0.941176,1,0.888889,0.421053,0.933333,0.875,0.5,0.583333,0.9375,1,0.7,0.875,1,0.823529,0.9375,1,0.9375,0.882353,0.705882,0.823529,0.722222,0.866667,0.933333,0.846154,0.8,0.555556,1,0.9375,0.619048,0.733333,0.875,0.9375,0.785714,0.882353,0.882353,0.666667,0.583333,1,0.875,0.705882,0.8,0.9375,0.666667,1,0.823529,1,0.9375,0.647059,1,1,0.928571,0.823529,0.928571,0.666667,0.8125,1,1,0.736842,0.933333,1,0.875,0.941176,0.9375,0.933333,1,0.875,0.823529,0.8125,0.666667,0.933333] predictive_accuracy 0.84375 prior_entropy 6.6438561897747395 recall 0.84375 [0.6875,0.9375,1,0.9375,0.875,0.875,1,1,0.8125,0.875,0.9375,0.875,1,0.8125,1,0.9375,1,0.625,0.4375,0.25,0.75,0.9375,1,1,0.875,0.4375,0.5625,0.875,0.875,1,1,0.9375,1,0.5,0.875,0.875,0.5,0.4375,0.9375,1,0.875,0.875,1,0.875,0.9375,0.9375,0.9375,0.9375,0.75,0.875,0.8125,0.8125,0.875,0.6875,0.75,0.625,1,0.9375,0.8125,0.6875,0.875,0.9375,0.6875,0.9375,0.9375,0.625,0.4375,1,0.875,0.75,0.75,0.9375,0.875,0.9375,0.875,0.9375,0.9375,0.6875,1,1,0.8125,0.875,0.8125,0.5,0.8125,1,1,0.875,0.875,1,0.875,1,0.9375,0.875,0.9375,0.875,0.875,0.8125,0.5,0.875] 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 0.9148555051349417 [0.996855,0.75,1,1,0.75,1,1,1,1,1,0.75,1,1,1,1,0.5,1,0.75,0.496835,0.987421,0.5,1,0.996855,1,1,0.743671,0.75,0.996835,1,1,1,1,0.996835,0.496835,1,0.996855,1,0.996855,1,1,0.996855,0.996855,1,1,1,1,1,0.996855,1,0.75,1,1,1,1,0.996855,0.496855,1,1,1,1,1,1,0.75,1,1,1,0.5,1,1,0.5,0.75,1,1,1,1,1,0.75,0.5,1,1,1,1,1,0.996855,1,1,1,0.996835,0.75,1,0.996835,1,1,0.996835,1,0.75,0.496855,0.993671,0.496835,1] area_under_roc_curve 0.9179006448531165 [1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.75,1,1,1,0.75,0.75,0.487421,1,1,0.996855,1,1,0.75,0.493671,1,0.996835,1,1,1,1,0.990506,0.75,0.5,0.496855,0.5,1,1,1,1,1,0.75,1,1,1,1,0.746835,0.996835,0.993671,1,1,0.75,1,0.496855,1,1,1,0.5,1,1,0.75,1,1,0.75,0.75,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.75,0.996835,0.996855,1,0.746835,1,1,0.996835,0.75,1,1,1,1,0.75,1,1,1,1,0.75,0.496855] area_under_roc_curve 0.9463816575113443 [1,1,1,1,1,0.996835,1,1,1,1,1,0.75,1,1,1,1,1,0.996835,1,0.5,1,1,1,1,1,0.746835,1,0.996855,1,1,1,1,1,0.75,0.996855,1,0.993711,1,1,1,0.746835,0.5,1,1,0.996835,1,0.75,1,0.75,1,1,1,1,1,1,1,1,1,0.993711,0.496835,1,1,0.75,0.5,1,1,1,1,1,0.75,1,0.996835,1,1,0.993711,1,1,0.996835,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.75,0.996855,1] area_under_roc_curve 0.9084865854629406 [0.5,1,1,1,0.75,0.75,1,1,0.746835,1,1,0.75,1,1,1,1,1,0.743671,0.493711,0.75,1,1,0.993671,1,0.5,0.75,1,1,1,1,1,1,1,0.746835,1,1,0.996855,0.5,1,1,0.996835,0.996855,1,0.996855,1,1,1,1,0.996835,1,0.996835,1,1,0.75,1,0.993671,1,1,0.996855,1,0.996855,1,1,1,0.75,0.5,0.493671,1,1,0.743671,0.5,1,0.990566,1,0.996855,1,1,0.75,1,1,1,1,0.75,0.75,0.5,1,1,1,1,1,0.75,1,1,1,1,0.996855,0.75,0.996835,0.996855,0.75] area_under_roc_curve 0.9115516280550912 [0.746835,1,1,1,1,0.75,1,1,1,1,0.996835,1,1,0.5,0.996855,1,1,0.746835,0.990566,0.743671,0.75,1,1,1,1,0.990506,0.75,1,1,1,1,0.5,1,0.496835,1,1,0.746835,0.5,1,1,0.746835,1,1,0.5,1,1,1,1,1,1,0.496855,0.496855,0.75,0.996855,0.496835,0.5,1,0.996835,0.993671,1,1,1,0.5,1,1,1,1,1,0.75,0.996835,0.996835,1,1,1,0.75,1,0.996835,0.996835,1,1,1,1,1,0.75,0.996855,1,1,1,1,1,1,1,1,1,0.75,1,0.996835,1,0.5,1] area_under_roc_curve 0.921025396067192 [0.75,1,1,1,1,1,1,1,1,0.75,1,0.996835,1,1,1,1,1,1,0.996855,0.5,0.75,0.75,0.996835,1,0.996835,0.743671,0.746835,0.5,1,1,1,1,1,0.746835,1,0.75,0.75,0.743671,1,1,0.996835,1,1,0.996855,1,1,1,1,0.75,0.75,1,1,1,1,0.996835,0.993671,1,1,0.75,0.996855,1,0.75,0.996855,1,1,0.75,1,1,0.996835,1,0.75,1,0.996855,1,0.75,1,1,0.746835,1,1,0.996855,0.746835,0.75,0.746835,1,1,1,0.996855,0.996835,1,1,1,1,1,1,1,1,1,0.5,1] area_under_roc_curve 0.9242297587771674 [1,1,1,1,0.996855,1,1,1,1,1,1,1,1,1,1,1,1,1,0.496835,0.5,0.993671,1,1,1,1,0.5,0.493711,0.75,1,1,0.996835,1,1,0.996855,1,1,0.493671,0.746835,0.996835,1,1,0.75,1,1,0.5,0.75,1,0.996835,0.996855,0.996855,1,1,0.996835,0.5,1,0.75,1,1,0.5,1,0.996835,0.996835,0.996855,0.996835,1,0.496855,0.996855,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.75,1,1,0.996835,1,1,1,0.5,1,1,1,1,0.75,1,1,0.75,1,1,0.5,1] area_under_roc_curve 0.9305588727012181 [0.75,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996855,0.984177,0.5,0.75,1,1,1,0.75,0.5,1,1,0.5,1,1,1,0.996855,1,1,1,0.5,0.746835,1,1,1,1,1,1,1,1,1,0.75,0.996855,1,0.996855,0.75,0.75,0.996855,1,0.746835,1,1,0.996835,0.993711,1,1,1,1,1,0.496855,0.5,1,1,0.746835,1,1,0.996835,1,1,1,1,0.5,1,1,0.75,1,0.75,0.75,0.75,1,1,0.996855,1,1,1,1,0.996835,1,1,0.996835,1,1,0.996835,1] 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 0.8991521375686651 [0.996855,1,1,1,1,0.996855,1,1,0.75,0.5,0.993711,1,1,0.75,0.996835,1,1,0.5,0.5,0.993711,1,1,1,1,1,0.496855,0.5,0.996835,0.75,1,1,1,1,0.493711,0.75,0.996835,0.746835,1,1,1,0.996855,1,1,1,1,1,1,1,0.996855,0.996855,0.75,0.75,1,0.75,0.75,0.996855,1,1,0.996835,0.75,0.75,1,0.746835,1,0.993711,0.993711,0.5,1,0.75,0.996855,1,0.5,0.746835,0.75,1,0.75,1,1,1,1,1,0.496855,1,0.496855,0.996835,1,1,0.746835,1,1,0.996835,0.996835,1,0.75,1,1,0.996855,0.5,1,1] 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 0.8357224657426056 kappa 0.8926047301299009 kappa 0.8168107702633345 kappa 0.8230717586193278 kappa 0.8420533070088845 kappa 0.8483591991470205 kappa 0.8610014215763703 kappa 0.8736426456071076 kappa 0.7978920775273359 kb_relative_information_score 132.94107512706694 kb_relative_information_score 133.94325752976818 kb_relative_information_score 142.96289915407914 kb_relative_information_score 130.93671032166446 kb_relative_information_score 131.9388927243657 kb_relative_information_score 134.94543993246936 kb_relative_information_score 135.9476223351706 kb_relative_information_score 137.951987140573 kb_relative_information_score 139.95635194597548 kb_relative_information_score 127.93016311356081 mean_absolute_error 0.003375000000000001 mean_absolute_error 0.0032500000000000007 mean_absolute_error 0.002125 mean_absolute_error 0.003625000000000001 mean_absolute_error 0.003500000000000001 mean_absolute_error 0.0031250000000000006 mean_absolute_error 0.003000000000000001 mean_absolute_error 0.0027500000000000007 mean_absolute_error 0.0025000000000000005 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] predictive_accuracy 0.83125 predictive_accuracy 0.8375 predictive_accuracy 0.89375 predictive_accuracy 0.81875 predictive_accuracy 0.825 predictive_accuracy 0.84375 predictive_accuracy 0.85 predictive_accuracy 0.8625 predictive_accuracy 0.875 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 0.83125 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1] recall 0.8375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0] recall 0.89375 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1] recall 0.81875 [0,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5] recall 0.825 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0,1,1,0.5,0,1,1,0.5,1,1,0,1,1,1,1,1,1,0,0,0.5,1,0,0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1] recall 0.84375 [0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1] 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 0.8625 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.8 [1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0.5,1,1,1,0,1,0.5,1,1,0,0.5,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.17045454545454522 relative_absolute_error 0.1641414141414139 relative_absolute_error 0.10732323232323215 relative_absolute_error 0.18308080808080782 relative_absolute_error 0.17676767676767655 relative_absolute_error 0.1578282828282826 relative_absolute_error 0.1515151515151513 relative_absolute_error 0.1388888888888887 relative_absolute_error 0.12626262626262608 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 0.058094750193111264 root_mean_squared_error 0.0570087712549569 root_mean_squared_error 0.046097722286464436 root_mean_squared_error 0.06020797289396149 root_mean_squared_error 0.05916079783099617 root_mean_squared_error 0.05590169943749475 root_mean_squared_error 0.05477225575051662 root_mean_squared_error 0.052440442408507586 root_mean_squared_error 0.05 root_mean_squared_error 0.0632455532033676 root_relative_squared_error 0.5838742081211419 root_relative_squared_error 0.5729597091269403 root_relative_squared_error 0.46329954095214076 root_relative_squared_error 0.6051128953853288 root_relative_squared_error 0.5945883900105629 root_relative_squared_error 0.5618332187193681 root_relative_squared_error 0.5504818825631801 root_relative_squared_error 0.5270462766947296 root_relative_squared_error 0.5025189076296057 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 550.5077549998987 usercpu_time_millis 581.1549830000331 usercpu_time_millis 582.3424620000424 usercpu_time_millis 579.7333689997686 usercpu_time_millis 571.1598240000058 usercpu_time_millis 581.8568639999739 usercpu_time_millis 586.7639890000191 usercpu_time_millis 589.2100269998082 usercpu_time_millis 582.9429480002091 usercpu_time_millis 578.7769450000724 usercpu_time_millis_testing 54.99158899988288 usercpu_time_millis_testing 60.525802000029216 usercpu_time_millis_testing 55.1568060000136 usercpu_time_millis_testing 59.201922999818635 usercpu_time_millis_testing 55.76758500001233 usercpu_time_millis_testing 57.725571999981184 usercpu_time_millis_testing 55.879220999941026 usercpu_time_millis_testing 56.063226999867766 usercpu_time_millis_testing 55.5136930001936 usercpu_time_millis_testing 57.03793600014251 usercpu_time_millis_training 495.5161660000158 usercpu_time_millis_training 520.6291810000039 usercpu_time_millis_training 527.1856560000288 usercpu_time_millis_training 520.5314459999499 usercpu_time_millis_training 515.3922389999934 usercpu_time_millis_training 524.1312919999928 usercpu_time_millis_training 530.8847680000781 usercpu_time_millis_training 533.1467999999404 usercpu_time_millis_training 527.4292550000155 usercpu_time_millis_training 521.7390089999299