6005829 1 Jan van Rijn 9954 Supervised Classification 6969 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1) 4040560 bootstrap true 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features 0.3463505634201669 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 5 6902 min_samples_split 15 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 100 6902 n_jobs 1 6902 oob_score false 6902 random_state 31032 6902 verbose 0 6902 warm_start false 6902 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "mean" 6947 strategy_nominal "most_frequent" 6947 verbose 0 6947 categorical_features [] 6948 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 6948 handle_unknown "ignore" 6948 n_values "auto" 6948 sparse false 6948 threshold 0.0 6949 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 12934749 description https://api.openml.org/data/download/12934749/description.xml -1 12934750 predictions https://api.openml.org/data/download/12934750/predictions.arff area_under_roc_curve 0.9953914141414141 [0.989347,0.999566,1,1,0.999527,0.995344,0.999763,0.999092,0.997041,0.999882,0.997475,0.999961,0.999605,0.985598,0.999882,0.998816,0.999961,0.985283,0.987926,0.973919,0.999053,1,0.994555,1,0.998816,0.962753,0.991477,0.998382,0.99858,0.999645,1,1,0.999369,0.991398,0.999171,0.998777,0.994555,0.993845,0.99783,0.999961,0.998619,0.999645,1,0.999053,0.998895,0.999882,0.999605,0.997988,0.99779,0.99858,0.997199,0.994003,0.998106,0.983152,0.997633,0.979561,1,0.999842,0.995068,0.985638,1,0.999053,0.994081,0.998619,0.99858,0.996094,0.978969,0.999961,0.995107,0.987966,0.99633,0.998619,0.994397,0.999448,0.968119,0.99925,0.999487,0.984691,1,0.999487,0.993766,0.99708,0.997869,0.995739,0.997159,0.983704,0.999921,0.994871,0.997909,1,0.999882,0.999408,0.998698,0.992779,0.999605,0.999605,0.996094,0.996212,0.965554,0.998619] average_cost 0 f_measure 0.7908395814092273 [0.615385,0.888889,1,1,0.933333,0.6,0.969697,0.857143,0.75,0.967742,0.709677,0.9375,0.857143,0.521739,0.969697,0.83871,0.888889,0.615385,0.540541,0.518519,0.833333,1,0.727273,1,0.888889,0.210526,0.451613,0.769231,0.903226,0.969697,0.909091,1,0.848485,0.733333,0.882353,0.810811,0.628571,0.578947,0.9375,0.941176,0.727273,0.882353,1,0.888889,0.9375,0.903226,0.903226,0.83871,0.774194,0.903226,0.848485,0.62069,0.833333,0.538462,0.689655,0.307692,1,0.969697,0.666667,0.714286,0.941176,0.833333,0.666667,0.882353,0.789474,0.75,0.470588,0.914286,0.56,0.518519,0.62069,0.848485,0.717949,0.914286,0.740741,0.9375,0.888889,0.47619,0.941176,0.967742,0.714286,0.727273,0.774194,0.709677,0.75,0.83871,0.969697,0.717949,0.882353,1,0.888889,0.789474,0.758621,0.740741,0.914286,0.909091,0.787879,0.625,0.48,0.774194] kappa 0.7986111111111112 kb_relative_information_score 1100.5813985440375 mean_absolute_error 0.014409128093272924 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 0.8043327451291168 [0.8,0.8,1,1,1,0.642857,0.941176,0.789474,0.75,1,0.733333,0.9375,0.789474,0.857143,0.941176,0.866667,0.8,0.8,0.47619,0.636364,0.75,1,0.705882,1,0.8,0.666667,0.466667,0.652174,0.933333,0.941176,0.882353,1,0.823529,0.785714,0.833333,0.714286,0.578947,0.5,0.9375,0.888889,0.705882,0.833333,1,0.8,0.9375,0.933333,0.933333,0.866667,0.8,0.933333,0.823529,0.692308,0.75,0.7,0.769231,0.4,1,0.941176,0.647059,0.833333,0.888889,0.75,0.714286,0.833333,0.681818,0.75,0.444444,0.842105,0.777778,0.636364,0.692308,0.823529,0.608696,0.842105,0.909091,0.9375,0.8,1,0.888889,1,0.833333,0.705882,0.8,0.733333,0.75,0.866667,0.941176,0.608696,0.833333,1,0.8,0.681818,0.846154,0.909091,0.842105,0.882353,0.764706,0.625,0.666667,0.8] predictive_accuracy 0.800625 prior_entropy 6.6438561897747395 recall 0.800625 [0.5,1,1,1,0.875,0.5625,1,0.9375,0.75,0.9375,0.6875,0.9375,0.9375,0.375,1,0.8125,1,0.5,0.625,0.4375,0.9375,1,0.75,1,1,0.125,0.4375,0.9375,0.875,1,0.9375,1,0.875,0.6875,0.9375,0.9375,0.6875,0.6875,0.9375,1,0.75,0.9375,1,1,0.9375,0.875,0.875,0.8125,0.75,0.875,0.875,0.5625,0.9375,0.4375,0.625,0.25,1,1,0.6875,0.625,1,0.9375,0.625,0.9375,0.9375,0.75,0.5,1,0.4375,0.4375,0.5625,0.875,0.875,1,0.625,0.9375,1,0.3125,1,0.9375,0.625,0.75,0.75,0.6875,0.75,0.8125,1,0.875,0.9375,1,1,0.9375,0.6875,0.625,1,0.9375,0.8125,0.625,0.375,0.75] relative_absolute_error 0.7277337420844898 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.0766440552292637 root_relative_squared_error 0.770301738202255 total_cost 0 area_under_roc_curve 0.9936748666507444 [1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.990506,1,1,1,0.971519,0.971519,0.993711,1,1,0.987421,1,1,0.987342,0.990506,1,0.993671,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.984177,1,1,1,1,1,1,1,0.987421,0.990506,1,1,0.993671,1,1,1,0.993671,1,1,0.943396,0.990506,1,1,1,1,1,1,0.981013,1,1,0.996835,1,0.993711,1,1,0.876582,1,0.996835,0.993671,1,1,1,1,0.977848,1,1,1,1,0.882911,1] area_under_roc_curve 0.995614650505533 [0.981132,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,1,1,0.984177,1,0.987421,1,1,1,1,1,0.955696,0.993671,0.996835,1,1,1,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.968354,1,1,1,0.955975,1,1,1,0.927215,1,1,1,0.996835,1,1,0.996835,1,0.968553,1,1,1,1,1,1,1,1,0.984177,1,0.993671,1,0.996835,0.987421,0.993711,1,1,1,1,0.996835,1,1,1,1,0.987342,1,1,1,0.990506,0.996835,1] area_under_roc_curve 0.9948648097285249 [1,1,1,1,1,0.990506,1,1,0.990506,1,1,1,1,0.996835,1,1,1,0.993671,0.981132,0.920886,0.993711,1,0.962025,1,1,0.977848,1,1,1,1,1,1,1,0.974684,1,1,0.993711,0.987421,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,0.993671,1,1,1,1,0.987421,0.984177,1,0.987421,1,1,1,0.981013,0.974684,1,0.993711,0.974684,1,1,0.993711,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835,0.899371,1] area_under_roc_curve 0.9953763135896819 [1,0.993671,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,0.996835,1,1,1,0.984177,0.937107,0.993671,1,1,1,1,0.993711,0.955696,0.990506,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.936709,1,0.993671,1,1,1,0.993671,1,1,1,1,0.996835,1,0.958861,1,1,0.974684,0.993671,0.996835,0.981132,1,1,0.996835,1,0.981013,1,1,0.987421,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,1,1,0.993711,0.993671,0.993671,1,0.993671] area_under_roc_curve 0.9932467060743573 [1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.91195,1,1,1,1,1,0.993671,0.993671,1,1,1,1,0.93038,0.990506,1,1,1,1,1,1,0.974684,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.996835,0.993711,0.984177,0.968354,1,0.996835,0.996835,1,1,1,0.993711,1,0.996835,1,0.893082,1,0.990506,0.981013,1,1,1,1,0.857595,1,1,0.971519,1,1,1,1,1,0.993671,0.981132,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,1,0.981132,0.996835] area_under_roc_curve 0.9953753184459835 [0.96519,1,1,1,1,0.987342,1,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.924051,1,1,0.990506,1,1,0.993671,0.977848,0.993711,1,1,1,1,0.984177,0.993671,0.993711,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,0.996835,0.974684,1,1,0.993671,1,1,1,0.987421,1,1,1,0.993711,1,0.996835,1,0.990506,0.993671,1,1,0.971519,1,1,0.987342,1,1,0.987421,0.996835,1,0.993671,0.993711,1,1,0.987421,0.993671,1,1,1,1,1,1,1,1,0.987421,0.993711,0.993671] area_under_roc_curve 0.9962532839742057 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,0.993711,0.996835,0.981013,0.996835,1,1,1,0.993671,0.861635,0.993711,0.996835,1,1,1,1,1,0.993711,1,1,0.987342,1,1,1,1,1,1,1,1,0.996835,1,0.987342,1,1,1,1,1,1,0.996835,0.990506,1,1,0.996835,0.993711,1,1,1,0.987342,1,0.993711,0.968553,1,0.996835,1,1,1,1,1,0.996835,1,1,0.981132,1,1,0.990506,1,0.996835,1,1,1,1,0.937107,1,1,1,1,1,1,1,1,1,1,0.962025,1] area_under_roc_curve 0.9966055648435634 [1,1,1,1,1,1,1,1,0.993711,1,0.993711,1,0.996835,1,1,0.993671,1,0.968553,0.993671,0.993671,1,1,1,1,1,0.993711,0.987421,0.996835,1,0.996835,1,1,1,1,1,1,0.984177,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,1,0.96519,1,1,0.996835,1,1,0.996835,0.993711,1,1,0.955975,0.987421,1,1,0.996835,1,1,1,1,0.990506,1,1,0.962264,1,1,0.971519,1,0.993671,1,0.996835,0.996835,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.974684,1,1,1] area_under_roc_curve 0.9968429066157153 [0.955975,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.987421,1,1,0.993671,0.996835,1,1,1,0.993711,1,1,0.996835,1,0.971519,1,0.955975,1,1,0.993671,0.996835,1,1,1,1,1,1,0.990506,1,1,0.993711,1,0.987421,0.981013,1,1,0.996835,1,1,1,0.996835,0.987342,0.993711,1,0.987421,0.996835,1,1,0.993671,1,1,1,0.996835,1,0.996835,1,1,0.981132,1,0.981013,1] area_under_roc_curve 0.9976728564604729 [0.955975,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.987342,1,1,1,0.993711,0.990506,0.981132,1,1,1,1,1,0.981132,1,1,1,1,1,1,1,0.987421,1,1,0.996835,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,1,1,1,1,0.990506,1,1,1,0.984177,1,0.993711,1,0.996835,1,1,0.993711,1,1,0.987342,1,0.993671,0.996835,1,1,1,1,1,0.981132,1,0.974843,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.7726106351900833 kappa 0.8547062539481995 kappa 0.7852603323727944 kappa 0.7914938988271532 kappa 0.8167963043392427 kappa 0.7409571947559628 kappa 0.8104564839677776 kappa 0.7851755321249456 kappa 0.8041383667667036 kappa 0.8230857323381905 kb_relative_information_score 109.6942010614935 kb_relative_information_score 109.78712159863142 kb_relative_information_score 109.42488572633503 kb_relative_information_score 109.5444488404042 kb_relative_information_score 109.03342941300635 kb_relative_information_score 108.99768324433263 kb_relative_information_score 111.57193841310624 kb_relative_information_score 110.67612609242939 kb_relative_information_score 110.17195912713404 kb_relative_information_score 111.679605027165 mean_absolute_error 0.01435447815110905 mean_absolute_error 0.014469442673816956 mean_absolute_error 0.014446224538791866 mean_absolute_error 0.014495464169558695 mean_absolute_error 0.014445290663004077 mean_absolute_error 0.01453816488498988 mean_absolute_error 0.014163938008516294 mean_absolute_error 0.014330298901577753 mean_absolute_error 0.01452484493615904 mean_absolute_error 0.01432313400520561 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 0.775 predictive_accuracy 0.85625 predictive_accuracy 0.7875 predictive_accuracy 0.79375 predictive_accuracy 0.81875 predictive_accuracy 0.74375 predictive_accuracy 0.8125 predictive_accuracy 0.7875 predictive_accuracy 0.80625 predictive_accuracy 0.825 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 0.775 [0,1,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0.5,1,0,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0,0,0.5,1,1,1,0,1,1,0.5,1,1,1,1,0,1,1,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1] recall 0.85625 [0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,0.5,1] recall 0.7875 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,0.5,0.5,1,0.5,1,0.5,1,1,0,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0,1] recall 0.79375 [1,1,1,1,0.5,0,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,0,0.5,1,1,1,1,0.5,1,0.5,1,0,0.5,0.5,0.5,0,1,1,1,1,0,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0.5,1,0] recall 0.81875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,0.5,0,1,1,1,1,1,1,1,1,1,1,0,1,0,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5] recall 0.74375 [0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,1,0,0,1,1,0.5,1,1,0.5,0,1,1,0.5,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,0,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0.5] recall 0.8125 [1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,0,1,0.5,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,0.5,1,0.5,1,1,0,1,1,0,1,0,1,1,0.5,0,1,1,1,0.5,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,1,0.5,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.7875 [0.5,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,0.5,1,0,0.5,0.5,1,1,0.5,1,1,0,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,1,1,1,0.5,1,1,0,1,1,0,0,0,1,0,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1] recall 0.80625 [0,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0.5,1,0.5,0,0,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0,0.5,0.5,1] recall 0.825 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0,0,1,1,1,0,0.5,1,1,1,1,1,0,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1] relative_absolute_error 0.7249736439954053 relative_absolute_error 0.7307799330210573 relative_absolute_error 0.7296072999389819 relative_absolute_error 0.7320941499777106 relative_absolute_error 0.7295601344951541 relative_absolute_error 0.7342507517671644 relative_absolute_error 0.7153504044705187 relative_absolute_error 0.723752469776653 relative_absolute_error 0.7335780270787382 relative_absolute_error 0.7233906063235144 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 0.07637399024061411 root_mean_squared_error 0.07673599876592037 root_mean_squared_error 0.07697584959572151 root_mean_squared_error 0.0770871296443844 root_mean_squared_error 0.07685058142525394 root_mean_squared_error 0.07743169876299016 root_mean_squared_error 0.07552800152991627 root_mean_squared_error 0.0762852978905446 root_mean_squared_error 0.07698901985139305 root_mean_squared_error 0.07616488984067395 root_relative_squared_error 0.7675874829405516 root_relative_squared_error 0.7712258055143415 root_relative_squared_error 0.7736363970540561 root_relative_squared_error 0.7747548036239569 root_relative_squared_error 0.7723774045703736 root_relative_squared_error 0.7782178535656502 root_relative_squared_error 0.7590849764852143 root_relative_squared_error 0.7666960912831107 root_relative_squared_error 0.7737687631039212 root_relative_squared_error 0.7654859448492946 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 4350.693070000034 usercpu_time_millis 4329.778818999785 usercpu_time_millis 4286.774668000135 usercpu_time_millis 4272.129350000341 usercpu_time_millis 4313.749946999906 usercpu_time_millis 4319.927604999975 usercpu_time_millis 4309.580618999917 usercpu_time_millis 4330.564353000227 usercpu_time_millis 4314.602693000097 usercpu_time_millis 4336.344815999837 usercpu_time_millis_testing 39.685799999915616 usercpu_time_millis_testing 37.71422999989227 usercpu_time_millis_testing 37.82836000004863 usercpu_time_millis_testing 40.61602800015862 usercpu_time_millis_testing 38.02927600008843 usercpu_time_millis_testing 37.77404200013734 usercpu_time_millis_testing 38.26338899989423 usercpu_time_millis_testing 37.78559200009113 usercpu_time_millis_testing 37.974752000081935 usercpu_time_millis_testing 37.776484999994864 usercpu_time_millis_training 4311.007270000118 usercpu_time_millis_training 4292.064588999892 usercpu_time_millis_training 4248.946308000086 usercpu_time_millis_training 4231.513322000183 usercpu_time_millis_training 4275.720670999817 usercpu_time_millis_training 4282.153562999838 usercpu_time_millis_training 4271.317230000022 usercpu_time_millis_training 4292.778761000136 usercpu_time_millis_training 4276.627941000015 usercpu_time_millis_training 4298.568330999842