8808012 1 Jan van Rijn 9954 Supervised Classification 7707 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1) 6783093 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "mean" 7660 strategy_nominal "most_frequent" 7660 verbose 0 7660 categorical_features [] 7661 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7661 handle_unknown "ignore" 7661 n_values "auto" 7661 sparse true 7661 threshold 0.0 7662 copy true 7663 with_mean false 7663 with_std true 7663 C 214.3114381918745 7708 cache_size 200 7708 class_weight null 7708 coef0 0.524866953041744 7708 decision_function_shape null 7708 degree 5 7708 gamma 0.004492543247341406 7708 kernel "sigmoid" 7708 max_iter -1 7708 probability true 7708 random_state 58226 7708 shrinking true 7708 tol 0.001266334504706969 7708 verbose false 7708 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18531987 description https://api.openml.org/data/download/18531987/description.xml -1 18531988 predictions https://api.openml.org/data/download/18531988/predictions.arff area_under_roc_curve 0.9965565814393937 [0.989268,0.999961,1,1,0.997909,0.994792,0.999842,0.999961,0.998264,0.999961,0.99858,0.999566,0.999763,0.983783,0.99925,0.999803,1,0.990096,0.983073,0.974669,0.998658,0.999448,0.997475,1,0.998146,0.975537,0.992898,0.999329,0.998737,1,0.999921,1,1,0.989702,0.999724,0.999171,0.993292,0.983546,0.999921,1,0.999369,0.999684,1,0.999171,0.997988,0.999763,0.999329,0.996015,0.997475,0.996409,0.994121,0.99925,0.998382,0.99057,0.99712,0.992266,1,0.999527,0.992622,0.995936,0.99929,0.999329,0.997317,0.999369,0.999448,0.99566,0.987098,1,0.998461,0.994121,0.996133,0.999605,0.996646,0.998185,0.987887,0.999605,0.999605,0.990767,1,1,0.99416,0.998067,0.998027,0.99562,0.99925,0.999724,1,0.999369,0.999605,1,0.999211,0.999527,0.998974,0.99779,0.997396,0.997554,0.997554,0.99854,0.969026,0.998698] average_cost 0 f_measure 0.8308011940168971 [0.742857,0.909091,1,1,0.933333,0.83871,0.969697,0.967742,0.866667,0.967742,0.8125,0.875,0.909091,0.666667,0.969697,0.933333,1,0.516129,0.368421,0.30303,0.774194,0.967742,0.857143,1,0.866667,0.5,0.516129,0.848485,0.933333,0.896552,0.967742,1,1,0.538462,0.933333,0.882353,0.529412,0.5,0.909091,1,0.848485,0.969697,1,0.875,0.903226,0.969697,0.866667,0.666667,0.764706,0.848485,0.75,0.827586,0.903226,0.727273,0.733333,0.5625,1,0.903226,0.684211,0.75,0.933333,0.882353,0.727273,0.9375,0.909091,0.742857,0.580645,1,0.866667,0.722222,0.787879,0.857143,0.666667,0.967742,0.777778,0.909091,0.9375,0.702703,1,1,0.774194,0.875,0.875,0.482759,0.848485,0.967742,0.933333,0.875,0.967742,0.967742,0.8125,0.785714,0.909091,0.866667,0.9375,0.777778,0.8,0.787879,0.5,0.903226] kappa 0.8276515151515151 kb_relative_information_score 984.6385001356574 mean_absolute_error 0.016178416422598332 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 0.8380740636212771 [0.684211,0.882353,1,1,1,0.866667,0.941176,1,0.928571,1,0.8125,0.875,0.882353,0.647059,0.941176,1,1,0.533333,0.318182,0.294118,0.8,1,0.789474,1,0.928571,0.5,0.533333,0.823529,1,1,1,1,1,0.7,1,0.833333,0.5,0.45,0.882353,1,0.823529,0.941176,1,0.875,0.933333,0.941176,0.928571,0.714286,0.722222,0.823529,0.75,0.923077,0.933333,0.705882,0.785714,0.5625,1,0.933333,0.590909,0.75,1,0.833333,0.705882,0.9375,0.882353,0.684211,0.6,1,0.928571,0.65,0.764706,1,0.714286,1,0.7,0.882353,0.9375,0.619048,1,1,0.8,0.875,0.875,0.538462,0.823529,1,1,0.875,1,1,0.8125,0.916667,0.882353,0.928571,0.9375,0.7,0.736842,0.764706,0.583333,0.933333] predictive_accuracy 0.829375 prior_entropy 6.6438561897747395 recall 0.829375 [0.8125,0.9375,1,1,0.875,0.8125,1,0.9375,0.8125,0.9375,0.8125,0.875,0.9375,0.6875,1,0.875,1,0.5,0.4375,0.3125,0.75,0.9375,0.9375,1,0.8125,0.5,0.5,0.875,0.875,0.8125,0.9375,1,1,0.4375,0.875,0.9375,0.5625,0.5625,0.9375,1,0.875,1,1,0.875,0.875,1,0.8125,0.625,0.8125,0.875,0.75,0.75,0.875,0.75,0.6875,0.5625,1,0.875,0.8125,0.75,0.875,0.9375,0.75,0.9375,0.9375,0.8125,0.5625,1,0.8125,0.8125,0.8125,0.75,0.625,0.9375,0.875,0.9375,0.9375,0.8125,1,1,0.75,0.875,0.875,0.4375,0.875,0.9375,0.875,0.875,0.9375,0.9375,0.8125,0.6875,0.9375,0.8125,0.9375,0.875,0.875,0.8125,0.4375,0.875] relative_absolute_error 0.8170917385150658 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08287184702315177 root_relative_squared_error 0.8328934007864401 total_cost 0 area_under_roc_curve 0.9959694192341374 [0.981132,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.990506,1,0.993711,1,0.981013,0.968354,0.987421,0.993711,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,0.987342,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,1,1,0.974684,1,1,0.968553,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.990506,0.987421,1,0.924051,1] area_under_roc_curve 0.9968777366451714 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.914557,1,1,1,0.981013,0.996835,0.993711,1,1,0.993711,1,1,0.977848,0.971519,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.977848,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.974843] area_under_roc_curve 0.9970352181354984 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,0.993671,1,1,0.990506,1,0.943038,1,1,0.996835,1,1,0.933544,1,1,1,1,1,1,1,0.984177,1,1,0.993711,1,1,1,1,0.993711,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,0.996835,1,1,0.981132,0.984177,1,1,1,0.993711,1,0.996835,1,1,1,0.984177,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1] area_under_roc_curve 0.9968404187564687 [1,1,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,0.996835,1,1,1,0.987342,0.943396,0.958861,1,1,1,1,0.993711,0.990506,1,1,1,1,1,1,1,0.993671,1,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.981013,1,1,0.993711,1,1,1,1,1,1,0.996835,0.990506,1,1,0.984177,0.974684,1,0.981132,1,0.993711,1,1,0.996835,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,1,1,1] area_under_roc_curve 0.9964844060982406 [1,1,1,1,1,0.993671,1,1,1,1,0.990506,1,1,0.968553,0.993711,1,1,0.990506,0.987421,0.987342,1,1,1,1,0.996835,0.993671,0.993671,1,1,1,1,1,1,0.974684,1,1,0.990506,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.990506,0.996835,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.946203,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,0.993711,1,1,1,1,1,0.996835,1,1,0.974684,0.993711,1,1,0.962264,1] area_under_roc_curve 0.9958880662367646 [0.920886,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.96519,1,1,1,1,0.993671,0.984177,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.968354,1,1,0.996835,1,1,1,1,1,1,0.996835,0.987342,0.996835,1,1,1,1,1,0.990506,1,1,0.993671,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.990506,1,1,1,1,1,0.993711,0.993671,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.962264,1] area_under_roc_curve 0.9956561977549555 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671,0.974684,1,1,0.990506,1,1,0.893082,0.974843,0.996835,1,1,0.996835,1,1,0.987421,1,1,0.971519,0.993671,1,1,1,1,1,1,0.974843,0.996835,1,0.987342,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.968354,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.901899,1] area_under_roc_curve 0.9977129109943477 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.987342,1,1,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.987342,0.993671,1,1,1,1,1,1,1,1,1,0.990506,1,0.987421,0.962264,1,0.996835,1,1,0.984177,1,1,0.996835,1,1,1,1,1,1,0.974843,0.949686,1,1,0.996835,1,1,1,1,1,1,1,0.981132,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1] area_under_roc_curve 0.9972750477668972 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.987342,0.981132,1,1,0.987421,1,1,0.987421,0.993711,1,1,1,1,1,1,0.993711,1,1,1,0.949367,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,0.990506,1,1,0.952532,0.996835,0.987421,1,1,1,1,1,1,1,1,1,1,0.984177,1,0.996835,1,1,0.993711,0.990506,1,1,1,1,0.993711,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] area_under_roc_curve 0.9976696222434518 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968354,0.987421,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,0.996835,1,1,1,0.984177,1,1,1,0.981013,1,0.993711,0.993711,0.993671,1,1,1,1,1,0.993671,0.990506,1,0.993671,1,1,1,1,1,0.968553,1,0.993711,1,1,1,0.993671,1,1,0.996835,1,1,0.990506,1,1,0.993711,0.993671,1,1] 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 0.8168252339031227 kappa 0.842047069973148 kappa 0.8862873613140126 kappa 0.8357678641926569 kappa 0.8168035375868604 kappa 0.7789270064348032 kappa 0.8231066887783305 kappa 0.8293906243829233 kappa 0.8547062539481995 kappa 0.7916008841174613 kb_relative_information_score 98.49178679161237 kb_relative_information_score 97.63983191801736 kb_relative_information_score 98.84271599345618 kb_relative_information_score 97.78011906880037 kb_relative_information_score 97.76046145191899 kb_relative_information_score 97.16265502791246 kb_relative_information_score 100.27799000211533 kb_relative_information_score 98.31131059773662 kb_relative_information_score 99.32848941018706 kb_relative_information_score 99.04313987390101 mean_absolute_error 0.01608239809899581 mean_absolute_error 0.016284027956715397 mean_absolute_error 0.016167661945464297 mean_absolute_error 0.0162768436913663 mean_absolute_error 0.01631281461231198 mean_absolute_error 0.016316785248032234 mean_absolute_error 0.015943743116387625 mean_absolute_error 0.01616655210831063 mean_absolute_error 0.016101639915445314 mean_absolute_error 0.0161316975329536 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 0.81875 predictive_accuracy 0.84375 predictive_accuracy 0.8875 predictive_accuracy 0.8375 predictive_accuracy 0.81875 predictive_accuracy 0.78125 predictive_accuracy 0.825 predictive_accuracy 0.83125 predictive_accuracy 0.85625 predictive_accuracy 0.79375 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 0.81875 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0.5,0,1,0,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1] recall 0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,0] recall 0.8875 [1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1] recall 0.8375 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5] recall 0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0,0,1,1,0.5,1,1,0,0,0.5,1,0,0.5,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.78125 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,0,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,1] recall 0.825 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0,0,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.83125 [0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1] recall 0.85625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1] recall 0.79375 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,0,0,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.8122423282321103 relative_absolute_error 0.8224256543795642 relative_absolute_error 0.816548583104256 relative_absolute_error 0.8220628126952663 relative_absolute_error 0.8238795258743411 relative_absolute_error 0.8240800630319296 relative_absolute_error 0.8052395513327071 relative_absolute_error 0.8164925307227577 relative_absolute_error 0.8132141371437014 relative_absolute_error 0.8147321986340188 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 0.08250345289576638 root_mean_squared_error 0.08327429300321681 root_mean_squared_error 0.08274533479545768 root_mean_squared_error 0.08333939423386788 root_mean_squared_error 0.08350336985491416 root_mean_squared_error 0.08358620587122312 root_mean_squared_error 0.0817624678249983 root_mean_squared_error 0.08281098309209198 root_mean_squared_error 0.08243829731656735 root_mean_squared_error 0.08273728358277412 root_relative_squared_error 0.829190900497023 root_relative_squared_error 0.8369381350720846 root_relative_squared_error 0.831621905057188 root_relative_squared_error 0.8375924270583269 root_relative_squared_error 0.8392404440576483 root_relative_squared_error 0.8400729773462075 root_relative_squared_error 0.8217437203303786 root_relative_squared_error 0.8322816952634361 root_relative_squared_error 0.8285360622873216 root_relative_squared_error 0.8315409873251312 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 1675.756167000145 usercpu_time_millis 1507.8462889996445 usercpu_time_millis 1471.8534170006023 usercpu_time_millis 1503.7993140012986 usercpu_time_millis 1469.6238540000195 usercpu_time_millis 1513.479629998983 usercpu_time_millis 1478.0662640005175 usercpu_time_millis 1492.1937459985202 usercpu_time_millis 1490.282450999075 usercpu_time_millis 1479.7354679994896 usercpu_time_millis_testing 151.57276900026773 usercpu_time_millis_testing 159.50439799962624 usercpu_time_millis_testing 151.2084700007108 usercpu_time_millis_testing 158.73798500069825 usercpu_time_millis_testing 150.46259500013548 usercpu_time_millis_testing 159.70380699945963 usercpu_time_millis_testing 150.34590499999467 usercpu_time_millis_testing 151.64983999966353 usercpu_time_millis_testing 150.40985099949467 usercpu_time_millis_testing 151.81584899983136 usercpu_time_millis_training 1524.1833979998773 usercpu_time_millis_training 1348.3418910000182 usercpu_time_millis_training 1320.6449469998915 usercpu_time_millis_training 1345.0613290006004 usercpu_time_millis_training 1319.161258999884 usercpu_time_millis_training 1353.7758229995234 usercpu_time_millis_training 1327.7203590005229 usercpu_time_millis_training 1340.5439059988566 usercpu_time_millis_training 1339.8725999995804 usercpu_time_millis_training 1327.9196189996583