8979105 1 Jan van Rijn 9954 Supervised Classification 8330 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)(2) 6914686 axis 0 7104 categorical_features [] 7104 copy true 7104 fill_empty 0 7104 missing_values "NaN" 7104 strategy "median" 7104 strategy_nominal "most_frequent" 7104 verbose 0 7104 categorical_features [] 7105 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7105 handle_unknown "ignore" 7105 n_values "auto" 7105 sparse true 7105 threshold 0.0 7106 C 4.473086749744791 7107 cache_size 200 7107 class_weight null 7107 coef0 -0.35993622262715275 7107 decision_function_shape null 7107 degree 5 7107 gamma 0.0036505689712527617 7107 kernel "rbf" 7107 max_iter -1 7107 probability true 7107 random_state 46965 7107 shrinking false 7107 tol 3.2883140610524516e-05 7107 verbose false 7107 copy true 7713 with_mean false 7713 with_std true 7713 openml-pimp openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18873271 description https://api.openml.org/data/download/18873271/description.xml -1 18873272 predictions https://api.openml.org/data/download/18873272/predictions.arff area_under_roc_curve 0.9966737689393945 [0.9884,0.999961,1,0.999842,0.998343,0.995581,0.999961,0.999961,0.997554,1,0.999448,0.999921,0.999763,0.981731,1,0.999645,1,0.988005,0.989268,0.969894,0.99779,0.999605,0.995423,1,0.999014,0.979324,0.987216,0.999448,0.99929,0.999961,0.999882,1,1,0.991793,0.999369,0.999171,0.994713,0.985677,0.999803,0.999921,0.998737,0.999448,1,0.99929,0.99854,0.999921,0.999014,0.999763,0.998422,0.996449,0.996252,0.998737,0.998303,0.993253,0.997593,0.987019,1,0.999724,0.994831,0.996173,0.998935,0.999448,0.997711,0.999566,0.999408,0.994989,0.989544,1,0.998737,0.994989,0.996449,0.999408,0.995147,0.998895,0.986111,0.999763,0.999684,0.98911,1,1,0.996015,0.997475,0.998777,0.997554,0.999527,0.999803,1,0.999605,0.999566,1,0.99779,0.999527,0.998698,0.998856,0.997869,0.998935,0.99783,0.998343,0.969066,0.998106] average_cost 0 f_measure 0.8287007861259182 [0.742857,0.967742,1,1,0.933333,0.827586,1,0.967742,0.875,0.967742,0.903226,0.875,0.903226,0.709677,1,0.933333,1,0.307692,0.451613,0.206897,0.866667,0.967742,0.756757,1,0.9375,0.424242,0.470588,0.875,0.903226,0.967742,0.967742,1,0.9375,0.5625,0.8125,0.857143,0.628571,0.432432,0.882353,0.967742,0.705882,0.909091,1,0.848485,0.9375,0.969697,0.827586,0.9375,0.742857,0.866667,0.823529,0.827586,0.903226,0.75,0.774194,0.482759,1,0.9375,0.648649,0.736842,0.83871,0.903226,0.6875,0.9375,0.875,0.647059,0.647059,1,0.866667,0.764706,0.866667,0.827586,0.666667,0.967742,0.742857,0.909091,0.9375,0.529412,0.967742,0.969697,0.83871,0.83871,0.933333,0.645161,0.875,0.903226,0.933333,0.83871,0.967742,0.967742,0.764706,0.827586,0.909091,0.903226,0.967742,0.823529,0.756757,0.833333,0.466667,0.903226] kappa 0.8257575757575758 kb_relative_information_score 983.2365613855925 mean_absolute_error 0.01624624728227458 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 0.8352046302510695 [0.684211,1,1,1,1,0.923077,1,1,0.875,1,0.933333,0.875,0.933333,0.733333,1,1,1,0.4,0.466667,0.230769,0.928571,1,0.666667,1,0.9375,0.411765,0.444444,0.875,0.933333,1,1,1,0.9375,0.5625,0.8125,0.789474,0.578947,0.380952,0.833333,1,0.666667,0.882353,1,0.823529,0.9375,0.941176,0.923077,0.9375,0.684211,0.928571,0.777778,0.923077,0.933333,0.75,0.8,0.538462,1,0.9375,0.571429,0.636364,0.866667,0.933333,0.6875,0.9375,0.875,0.611111,0.611111,1,0.928571,0.722222,0.928571,0.923077,0.647059,1,0.684211,0.882353,0.9375,0.5,1,0.941176,0.866667,0.866667,1,0.666667,0.875,0.933333,1,0.866667,1,1,0.722222,0.923077,0.882353,0.933333,1,0.777778,0.666667,0.75,0.5,0.933333] predictive_accuracy 0.8275 prior_entropy 6.6438561897747395 recall 0.8275 [0.8125,0.9375,1,1,0.875,0.75,1,0.9375,0.875,0.9375,0.875,0.875,0.875,0.6875,1,0.875,1,0.25,0.4375,0.1875,0.8125,0.9375,0.875,1,0.9375,0.4375,0.5,0.875,0.875,0.9375,0.9375,1,0.9375,0.5625,0.8125,0.9375,0.6875,0.5,0.9375,0.9375,0.75,0.9375,1,0.875,0.9375,1,0.75,0.9375,0.8125,0.8125,0.875,0.75,0.875,0.75,0.75,0.4375,1,0.9375,0.75,0.875,0.8125,0.875,0.6875,0.9375,0.875,0.6875,0.6875,1,0.8125,0.8125,0.8125,0.75,0.6875,0.9375,0.8125,0.9375,0.9375,0.5625,0.9375,1,0.8125,0.8125,0.875,0.625,0.875,0.875,0.875,0.8125,0.9375,0.9375,0.8125,0.75,0.9375,0.875,0.9375,0.875,0.875,0.9375,0.4375,0.875] relative_absolute_error 0.8205175395088156 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08305474394803074 root_relative_squared_error 0.83473158404442 total_cost 0 area_under_roc_curve 0.9964839085263912 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.984177,0.974843,0.993711,1,1,1,1,0.987342,0.990506,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,0.993711,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.990506,1,1,0.962264,0.993671,1,1,1,1,1,1,0.962025,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,0.990506,0.993711,0.993671,0.917722,1] area_under_roc_curve 0.9967190112252211 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.911392,1,1,1,0.971519,0.993671,0.993711,1,1,1,1,1,0.981013,0.971519,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.981132,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.990506,1,1,0.984177,0.996835,1,1,0.996835,1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,0.996835,1,0.974843] area_under_roc_curve 0.9970364620651221 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.933544,1,1,0.996835,1,1,0.968354,0.996835,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,0.996835,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.981132,0.984177,1,1,1,0.993711,1,0.990506,1,1,1,0.987342,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.955975,1] area_under_roc_curve 0.9969190351086699 [1,1,1,1,0.996835,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.993671,0.949686,0.958861,1,1,0.996835,1,0.993711,0.977848,1,1,1,1,1,1,1,0.993671,1,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.981013,1,1,1,1,1,1,1,1,1,0.996835,0.981013,1,1,0.984177,0.984177,1,0.981132,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,0.993711,1] area_under_roc_curve 0.9966811957646684 [1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,0.955975,1,1,1,0.996835,1,0.990506,1,1,1,1,1,0.996835,0.987342,1,1,1,1,1,1,0.981013,1,1,1,0.971519,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.993671,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.93038,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.974684,0.993711,1,1,0.981132,1] area_under_roc_curve 0.99600723469469 [0.927215,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.958861,1,1,0.993671,1,0.993671,0.984177,0.971519,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.981013,1,1,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,0.996835,0.990506,1,1,0.996835,1,0.993671,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.943396,1] area_under_roc_curve 0.9964077800334366 [0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993711,0.990506,0.977848,1,1,0.993671,1,1,0.90566,0.949686,1,1,1,0.996835,1,1,1,1,1,0.971519,0.993671,1,1,1,1,1,1,0.981132,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.971519,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.93038,1] area_under_roc_curve 0.997080497173792 [0.981013,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.981132,0.993671,0.968354,0.996835,1,0.996835,1,0.996835,0.993711,0.993711,1,0.987421,1,1,1,1,1,1,1,0.990506,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.968553,0.996835,1,1,1,0.984177,1,1,0.993671,1,1,1,1,1,1,0.974843,0.955975,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835] area_under_roc_curve 0.997749731311201 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,0.968553,1,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.990506,1,1,0.971519,0.996835,0.968553,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.996835,1,1,0.993711,0.977848,1,1,1,1,0.993711,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1] area_under_roc_curve 0.9975912546771752 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.987421,0.971519,0.987421,1,1,1,1,1,0.993711,0.993711,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,0.981013,1,1,1,0.993671,1,0.993711,0.993711,0.984177,1,1,1,1,1,0.981013,0.993671,1,0.993671,1,1,1,1,1,0.968553,1,0.987421,1,0.996835,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993711,1,1,1] 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 f_measure 0.8617261904761901 [1,1,1,1,1,1,1,1,1,1,0.666667,1,1,0.666667,1,1,1,0.666667,0.5,0,1,1,0.666667,1,1,0.5,0,1,1,1,1,1,1,1,0.666667,0.666667,0,0.666667,1,1,1,1,1,0.666667,1,1,1,1,0.5,1,0.8,0.666667,0.666667,0.666667,1,0,1,1,0.666667,0.8,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,0.571429,1,1,0.666667,0.666667,1,1,0.5,1,1,0.8,1,1,0.666667,1,1,1,1,1,1,0.8,0.666667,0] kappa 0.7789357334596557 kappa 0.8610069101678184 kappa 0.8483771618099976 kappa 0.8168469250809189 kappa 0.8420283559101142 kappa 0.8168035375868604 kappa 0.8167890705204138 kappa 0.8230927183699257 kappa 0.8546890424481737 kappa 0.7979080323662917 kb_relative_information_score 98.47830371815951 kb_relative_information_score 97.62855549072525 kb_relative_information_score 98.10099558380689 kb_relative_information_score 98.20083977475238 kb_relative_information_score 98.17732577034343 kb_relative_information_score 97.28510778893335 kb_relative_information_score 100.30496188028634 kb_relative_information_score 97.67518938493671 kb_relative_information_score 99.15160666349166 kb_relative_information_score 98.2336753301571 mean_absolute_error 0.016159381766135926 mean_absolute_error 0.01634451873694534 mean_absolute_error 0.01630268243610223 mean_absolute_error 0.016283945801548835 mean_absolute_error 0.016284174430868822 mean_absolute_error 0.016351049184798024 mean_absolute_error 0.015997611729639725 mean_absolute_error 0.016305078593573667 mean_absolute_error 0.0161713831975974 mean_absolute_error 0.016262646945535697 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] precision 0.8977083333333333 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0.666667,1,0.5,1,1,0,1,1,0.5,0.666667,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.4,1,1,1,1,1,1,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,1,0.666667,1,0] predictive_accuracy 0.78125 predictive_accuracy 0.8625 predictive_accuracy 0.85 predictive_accuracy 0.81875 predictive_accuracy 0.84375 predictive_accuracy 0.81875 predictive_accuracy 0.81875 predictive_accuracy 0.825 predictive_accuracy 0.85625 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 0.78125 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0,1,0,0,0,0,1,1,1,1,0,0.5,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1] recall 0.8625 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0] recall 0.85 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,1] recall 0.81875 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1] recall 0.84375 [1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.81875 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,1] recall 0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.825 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5] recall 0.85625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1] recall 0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1] relative_absolute_error 0.8161303922290858 relative_absolute_error 0.8254807442901673 relative_absolute_error 0.8233677998031415 relative_absolute_error 0.8224215051287277 relative_absolute_error 0.8224330520640806 relative_absolute_error 0.8258105648887877 relative_absolute_error 0.8079601883656413 relative_absolute_error 0.8234888178572546 relative_absolute_error 0.8167365251311806 relative_absolute_error 0.8213458053300843 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 0.08274843630961712 root_mean_squared_error 0.08342334994099754 root_mean_squared_error 0.08326265011395587 root_mean_squared_error 0.08323216607288657 root_mean_squared_error 0.08317657078729267 root_mean_squared_error 0.08361792213877806 root_mean_squared_error 0.08192111905133745 root_mean_squared_error 0.08331044659589282 root_mean_squared_error 0.0826312809553767 root_mean_squared_error 0.08321031090426131 root_relative_squared_error 0.831653076447336 root_relative_squared_error 0.8384362136630484 root_relative_squared_error 0.8368211196322233 root_relative_squared_error 0.8365147434918577 root_relative_squared_error 0.8359559898481378 root_relative_squared_error 0.8403917378287236 root_relative_squared_error 0.8233382251494595 root_relative_squared_error 0.8373014923500532 root_relative_squared_error 0.8304756208346188 root_relative_squared_error 0.8362950907825853 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 2307.5377539998954 usercpu_time_millis 2106.5578769998865 usercpu_time_millis 2103.406448999749 usercpu_time_millis 2118.5336799999277 usercpu_time_millis 2115.2560759999233 usercpu_time_millis 2110.75055099991 usercpu_time_millis 2129.1872460001287 usercpu_time_millis 2109.573353000087 usercpu_time_millis 2120.4523119999976 usercpu_time_millis 2124.5645899998635 usercpu_time_millis_testing 176.05030899994745 usercpu_time_millis_testing 178.7929909999093 usercpu_time_millis_testing 171.84185199994317 usercpu_time_millis_testing 173.35764499989637 usercpu_time_millis_testing 172.54472399986298 usercpu_time_millis_testing 172.29894999991302 usercpu_time_millis_testing 172.58616100002655 usercpu_time_millis_testing 172.83739499998774 usercpu_time_millis_testing 173.17722000007052 usercpu_time_millis_testing 173.3433059998788 usercpu_time_millis_training 2131.487444999948 usercpu_time_millis_training 1927.7648859999772 usercpu_time_millis_training 1931.5645969998059 usercpu_time_millis_training 1945.1760350000313 usercpu_time_millis_training 1942.7113520000603 usercpu_time_millis_training 1938.451600999997 usercpu_time_millis_training 1956.601085000102 usercpu_time_millis_training 1936.7359580000993 usercpu_time_millis_training 1947.275091999927 usercpu_time_millis_training 1951.2212839999847