5987308 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) 4022041 bootstrap false 6902 class_weight null 6902 criterion "gini" 6902 max_depth null 6902 max_features 0.5790955780545199 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 16 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 33398 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 "median" 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 12897905 description https://api.openml.org/data/download/12897905/description.xml -1 12897906 predictions https://api.openml.org/data/download/12897906/predictions.arff area_under_roc_curve 0.9895422979797976 [0.976405,0.997199,0.997869,0.999684,0.999408,0.994318,0.994673,0.995541,0.994476,0.999053,0.992424,0.995107,0.998619,0.97964,0.997159,0.993924,0.99929,0.978456,0.983862,0.974274,0.998422,0.997199,0.993016,0.999961,0.997909,0.954033,0.961845,0.994003,0.991162,0.998698,0.994634,0.995068,0.993095,0.983586,0.998185,0.992661,0.987216,0.975221,0.992227,0.995739,0.998422,0.997909,0.996567,0.991832,0.995028,0.998974,0.99345,0.995502,0.99345,0.9869,0.990807,0.988242,0.991398,0.982757,0.991793,0.973879,0.999527,0.992069,0.985953,0.961411,0.999763,0.997317,0.985756,0.98619,0.994003,0.99345,0.974511,0.986269,0.944878,0.97968,0.971828,0.99712,0.984296,0.995423,0.960188,0.988755,0.997751,0.97242,0.997672,0.995344,0.983783,0.988755,0.994555,0.993766,0.992148,0.994121,0.999448,0.984336,0.994003,0.999053,0.997869,0.996843,0.993529,0.979127,0.987768,0.994279,0.991872,0.986742,0.962397,0.996094] average_cost 0 kappa 0.6029040404040404 kb_relative_information_score 1051.4886779014328 mean_absolute_error 0.014612595608359813 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] predictive_accuracy 0.606875 prior_entropy 6.6438561897747395 recall 0.606875 [0.3125,0.875,0.875,0.9375,1,0.5,0.8125,0.5625,0.75,1,0.6875,0.875,0.8125,0.375,0.875,0.625,0.9375,0.4375,0.1875,0.0625,0.875,0.4375,0.4375,1,0.75,0.0625,0.0625,0.4375,0.75,0.8125,0.8125,0.875,0.875,0.4375,0.8125,0.625,0.5,0.3125,0.4375,0.75,0.9375,0.875,0.8125,0.4375,0.5625,0.9375,0.875,0.75,0.75,0.6875,0.75,0.5625,0.625,0.1875,0.625,0.25,1,0.6875,0.0625,0.375,1,1,0.5,0.75,0.75,0.8125,0.375,0.3125,0,0.25,0,0.625,0.5625,0.6875,0.125,0.0625,0.875,0.5,0.9375,0.375,0.125,0.5625,0.5,0.6875,0.6875,0.625,0.875,0.5625,0.75,0.9375,0.875,0.75,0.6875,0.25,0.5625,0.75,0.5625,0.125,0.1875,0.8125] relative_absolute_error 0.7380098792100902 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08009055500786894 root_relative_squared_error 0.8049403642800627 total_cost 0 area_under_roc_curve 0.988376721598599 [1,0.993671,1,1,1,1,1,1,0.981013,1,0.971519,0.996835,0.993671,0.971519,1,0.987421,0.996835,0.971519,0.968354,0.968553,0.987421,1,0.987421,1,0.993711,0.990506,0.905063,1,0.990506,1,0.952532,0.996835,0.974684,0.96519,1,0.981132,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.955696,0.993671,1,0.987421,0.984177,1,1,1,1,0.987421,0.981013,1,1,1,0.974684,1,1,0.981013,0.962264,1,1,0.971519,1,0.958861,1,1,0.996835,1,0.977848,1,1,0.993671,0.996835,0.993711,1,0.984177,0.990506,1,1,0.984177,1,0.996835,1,0.993671,0.987342,1,0.981013,1,0.996835,0.857595,1] area_under_roc_curve 0.9871992178170526 [0.993711,1,1,1,1,0.993711,1,1,1,1,1,0.993671,1,0.96519,1,1,0.996835,0.946203,0.990506,0.987421,1,1,1,1,1,0.936709,0.981013,0.974684,1,1,1,1,1,0.990506,0.996835,0.974843,0.968553,1,0.987421,1,1,1,1,0.971519,1,1,1,1,0.990506,0.996835,1,0.958861,1,1,0.993711,0.962264,1,1,1,0.844937,1,0.987421,1,0.958861,0.993711,0.990506,0.993671,1,0.786164,0.993711,0.949367,1,0.996835,0.993671,1,0.996835,0.987342,0.927215,1,0.987342,0.968354,0.987342,0.987421,0.993711,0.977848,1,1,0.996835,1,1,0.987342,1,1,0.984177,1,1,1,0.96519,0.990506,1] area_under_roc_curve 0.990515285407213 [1,1,1,1,1,0.996835,0.993711,1,0.990506,1,1,1,1,0.993671,1,1,1,0.987342,0.974843,0.911392,1,1,0.981013,1,1,0.993671,1,0.993711,1,1,0.993711,1,1,0.974684,1,1,0.993711,0.930818,1,1,0.990506,1,1,1,0.987342,1,0.981013,1,0.977848,1,1,1,1,1,1,0.993671,1,1,0.962264,0.914557,1,0.987421,0.993671,1,0.987342,0.984177,0.984177,0.974684,0.981132,0.955696,0.962025,0.996835,1,1,1,1,1,0.924051,0.993671,1,0.993711,1,1,1,1,1,1,0.990506,0.993671,1,1,1,1,0.974684,1,1,0.996835,0.977848,0.968553,0.990506] area_under_roc_curve 0.9924538253323781 [0.958861,1,1,1,1,0.993671,1,1,0.996835,1,0.993671,1,1,0.990506,1,1,1,1,0.949686,0.981013,1,1,1,1,0.987421,0.974684,0.93038,0.993711,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,0.993671,0.981013,0.993671,1,0.937107,0.946203,1,0.981013,1,0.974684,1,0.993671,1,1,0.990506,1,0.990506,1,0.96519,0.993671,0.993711,0.984177,0.987342,1,0.987421,1,0.987421,0.984177,1,0.984177,1,1,0.955975,0.990506,1,0.996835,1,1,1,1,1,1,1,1,0.993711,0.996835,1,0.993711,0.993671,0.993671,0.993711,0.993671] area_under_roc_curve 0.9875529914019582 [0.977848,1,1,1,1,0.996835,1,0.996835,1,1,0.981013,0.968354,1,0.886792,1,1,1,0.996835,1,0.987342,1,1,1,1,1,0.952532,0.981013,1,1,0.996835,1,0.937107,1,0.968354,1,1,0.987342,0.905063,0.990506,1,1,1,1,1,1,1,1,0.996835,0.990506,1,1,0.968553,0.996835,0.993711,0.974684,0.968354,1,0.990506,0.993671,0.987421,1,1,0.993711,1,0.987342,1,0.949686,0.968354,0.908228,0.971519,0.990506,1,1,1,0.882911,1,1,0.981013,1,1,1,1,0.987342,0.993671,0.981132,0.974843,1,1,1,1,1,0.987342,1,1,0.93038,1,0.993671,1,0.987421,0.993671] area_under_roc_curve 0.9927680419552585 [0.968354,1,1,1,1,0.987342,1,0.993671,1,1,0.996835,1,1,0.993711,1,1,1,0.987342,1,0.955696,1,1,0.996835,1,1,0.987342,0.974684,1,1,1,1,1,0.968354,1,1,0.996835,0.987342,0.990506,0.990506,0.987342,1,0.993671,1,0.987421,0.993711,1,1,0.993671,1,0.996835,1,1,0.990506,1,0.996835,0.987342,1,1,0.987342,1,1,1,0.987421,1,1,1,1,1,0.958861,0.993671,0.987342,0.990506,1,1,0.93038,0.968553,0.996835,0.990506,1,1,0.993711,0.984177,1,0.996835,0.993711,1,1,0.993711,0.974684,1,1,0.993671,0.937107,1,1,1,1,0.981132,0.993711,0.996835] area_under_roc_curve 0.9932183444789426 [1,0.993711,0.987421,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,0.958861,1,1,0.993671,0.981013,0.996835,0.996835,0.996835,1,0.990506,0.962264,0.937107,0.993671,1,0.996835,1,0.993711,1,0.962264,1,1,0.993671,0.996835,1,1,1,1,1,0.996835,0.968553,0.993671,0.977848,0.990506,1,1,0.899371,1,1,0.993711,0.990506,1,1,0.996835,0.987342,0.993711,1,1,1,0.996835,0.996835,1,0.968553,0.993671,0.987342,0.984177,1,1,1,1,1,0.987421,1,1,1,1,0.981013,1,1,1,1,0.996835,1,0.918239,1,1,1,1,0.987342,0.91195,0.974843,1,1,0.993711,0.990506,1] area_under_roc_curve 0.9901729559748425 [0.993671,1,1,0.996835,1,0.996835,1,1,0.993711,1,0.987421,1,1,1,0.981132,0.996835,1,0.993711,0.981013,0.993671,1,1,1,1,1,0.955975,0.981132,1,0.91195,0.996835,1,1,1,1,1,1,0.990506,0.971519,0.996835,1,1,1,1,0.996835,0.993711,0.996835,1,1,0.993711,1,0.993711,0.993671,0.977848,1,0.996835,0.927215,1,1,0.974684,0.968553,1,0.993671,0.993711,1,1,0.943396,1,0.987342,0.927215,0.971519,0.962264,0.996835,0.993671,1,0.984177,1,1,0.993711,1,0.993711,0.974684,0.962264,0.984177,1,0.993671,0.984177,1,0.924528,1,1,1,0.996835,1,0.924528,1,0.984177,0.981013,0.974843,0.971519,0.996835] area_under_roc_curve 0.9903281983918477 [0.955975,0.996835,1,1,0.996835,0.993711,0.968354,0.981132,0.996835,1,0.993711,1,1,1,0.996835,0.993711,1,1,1,1,1,0.993671,0.955975,1,1,0.855346,0.981132,0.996835,1,1,0.996835,1,1,1,1,1,1,0.962025,0.993671,0.974843,1,0.987342,0.996835,0.990506,1,1,1,1,0.987421,1,1,0.977848,1,0.968354,0.993671,0.949686,1,0.993711,1,0.977848,1,1,1,0.990506,1,1,0.968354,1,0.958861,0.993711,1,0.987421,0.96519,0.974684,0.96519,0.974684,1,0.993711,1,0.996835,0.987342,0.962264,1,0.993711,1,0.996835,1,0.981013,0.981132,1,1,1,1,0.977848,0.981132,0.987342,0.993711,0.996835,0.962025,1] area_under_roc_curve 0.9908504000477667 [0.874214,1,0.968553,1,1,0.993711,1,0.974843,0.996835,1,0.993711,1,1,0.984177,1,0.987421,1,0.962264,0.993671,1,1,1,1,1,1,0.867925,0.993711,0.996835,0.993671,1,1,1,1,0.993711,0.993671,0.993671,0.987342,0.990506,0.987342,1,0.993711,1,1,0.996835,1,1,1,0.987342,0.993711,0.993711,0.981013,0.996835,0.993671,0.977848,1,0.955975,1,0.981132,0.993671,0.993671,1,1,0.968354,0.981013,1,1,0.990506,0.993711,0.943038,0.974843,0.90566,1,0.984177,1,0.962025,0.990506,1,0.993711,1,0.993671,1,0.987421,0.993711,0.968553,0.996835,1,0.993671,0.987342,1,0.996835,1,0.996835,0.993671,1,1,1,0.955975,1,1,1] 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 0.5768532407041921 kappa 0.6526681401957689 kappa 0.6210925165772024 kappa 0.6336793905182962 kappa 0.5830701200252685 kappa 0.6210925165772024 kappa 0.6147165640296858 kappa 0.6272752398625973 kappa 0.5642563940637828 kappa 0.5327545382794001 kb_relative_information_score 103.62785974698733 kb_relative_information_score 105.31669508168201 kb_relative_information_score 105.33599928551001 kb_relative_information_score 105.85812333791837 kb_relative_information_score 104.12870495777399 kb_relative_information_score 106.74120902689782 kb_relative_information_score 107.54066853694938 kb_relative_information_score 105.23429920293842 kb_relative_information_score 104.32488397810546 kb_relative_information_score 103.38023474667195 mean_absolute_error 0.014715414446463626 mean_absolute_error 0.014430208169602873 mean_absolute_error 0.014572816500257286 mean_absolute_error 0.014698709305632506 mean_absolute_error 0.014526503940923405 mean_absolute_error 0.014574561415733805 mean_absolute_error 0.014378555791445785 mean_absolute_error 0.014588130975477115 mean_absolute_error 0.014785218837587655 mean_absolute_error 0.014855836700473992 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 0.58125 predictive_accuracy 0.65625 predictive_accuracy 0.625 predictive_accuracy 0.6375 predictive_accuracy 0.5875 predictive_accuracy 0.625 predictive_accuracy 0.61875 predictive_accuracy 0.63125 predictive_accuracy 0.56875 predictive_accuracy 0.5375 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 0.58125 [1,1,1,1,1,1,1,1,0,1,0,0.5,0.5,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0.5,0,0.5,1,0.5,0,1,0,1,1,1,0.5,1,1,1,0,0.5,1,1,1,1,0.5,1,0.5,1,0,1,1,1,1,0,0,1,1,0.5,0.5,1,1,0.5,0,0,1,0,0,0,1,0,0,0.5,0.5,1,0.5,0,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,0,0,1] recall 0.65625 [1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,0,0,1,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,0,0,1,1,0,0.5,1,1,1,0.5,1,0,0.5,1,0,0,0,1,1,0.5,0,0,0.5,0.5,1,0,0,0.5,0,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,1,1,0.5,0.5,1] recall 0.625 [0,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,0,0,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,0.5,1,1,0,1,0,1,1,0,0.5,1,1,0.5,1,0.5,1,0.5,0,0,0,0,1,0,0.5,0,0.5,1,0.5,0.5,0,0,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0,0,0.5] recall 0.6375 [0,1,1,1,1,0,1,0.5,0.5,1,0.5,1,1,0,1,1,1,1,0,0,1,1,1,1,0,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0,1,0,1,0.5,0,0.5,1,1,0.5,1,0.5,1,0,0.5,0,0,0,0.5,1,0.5,0,0,1,0.5,1,0.5,0,0.5,1,1,1,0,1,1,1,1,1,1,0,0.5,1,1,0,0,0,0.5] recall 0.5875 [0,1,1,1,1,1,1,0.5,0,1,0.5,0.5,0,0,1,1,1,0.5,0,0,1,0.5,0.5,1,0.5,0,0,0,1,0.5,1,0,1,0.5,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,0.5,0,1,0.5,0,1,1,1,0,1,0.5,1,0,0,0,0.5,0,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0,1,0,1,1,1,0,1,0,0,1,0.5,0,0,0.5] recall 0.625 [0,1,1,1,1,0.5,1,0,1,1,1,1,0,1,1,1,1,0.5,0,0,1,0.5,0.5,1,1,0,0,0,0,1,0,1,0.5,1,1,0.5,0.5,0.5,0,0,1,0.5,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0,1,1,1,1,1,0,0,0,0,1,1,0.5,0,1,0,1,1,0,0.5,0.5,0.5,0,1,1,1,0,1,1,1,0,1,0.5,1,1,0,0,1] recall 0.61875 [0.5,0,0,0.5,1,0,0.5,1,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,0,0,1,0.5,0,0,0.5,0,1,0.5,0,1,1,1,1,0,0.5,0.5,1,1,1,1,0.5,0,0.5,0.5,1,1,1,0,1,1,0,0.5,0.5,1,0.5,0,0,1,1,1,1,0.5,1,0,0.5,0,0.5,0,1,1,1,0,0,1,1,1,1,0,1,0.5,1,1,0.5,1,0,1,1,1,1,0.5,0,0,0.5,1,1,0,1] recall 0.63125 [1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,0,0,0.5,1,1,0,0,0.5,0.5,0.5,1,1,0,0,0.5,0,0.5,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,0,1,1,1,1,1,0,0.5,0.5,1,1,0.5,1,1,0.5,0,1,1,0,1,1,0,1,0,0,0,0,1,0.5,1,0.5,0,1,1,1,1,0,0,0.5,1,0,0.5,1,0,1,1,0,0.5,1,0,1,0.5,0,0,0,1] recall 0.56875 [0,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,0,1,1,0.5,0,1,0.5,0,1,1,0,0,1,1,1,1,1,1,1,0.5,0.5,0,0,0.5,0,1,0.5,0.5,0.5,0,1,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,0,0,1,1,1,0.5,1,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0,0,0,1,0,1,1,1,0.5,0,1,1,1,0.5,0,0,0.5,1,0,0,1] recall 0.5375 [0,0.5,0,1,1,1,1,0,1,1,1,1,1,0.5,0.5,0,1,0,0,0,1,0.5,1,1,1,0,0,0,1,1,1,1,1,1,0.5,0,0.5,0,0,1,1,1,0.5,0,0.5,1,1,0.5,1,1,0.5,0.5,0,0,0.5,0,1,0,0,0.5,1,1,0,0.5,1,1,0.5,0,0,0,0,0,0.5,1,0,0,1,1,1,0,0.5,0,1,0,1,0.5,0,0,1,0.5,1,0.5,0.5,0,0,1,1,0,1,1] relative_absolute_error 0.7432027498213941 relative_absolute_error 0.7287983924041843 relative_absolute_error 0.7360008333463264 relative_absolute_error 0.7423590558400244 relative_absolute_error 0.7336618151981505 relative_absolute_error 0.736088960390595 relative_absolute_error 0.7261896864366545 relative_absolute_error 0.7367742916907623 relative_absolute_error 0.7467282241205874 relative_absolute_error 0.7502947828522205 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 0.08100591516257748 root_mean_squared_error 0.07920106111379754 root_mean_squared_error 0.0796660463665645 root_mean_squared_error 0.08025269358644253 root_mean_squared_error 0.08009786736236839 root_mean_squared_error 0.07955570350763826 root_mean_squared_error 0.07888831596427129 root_mean_squared_error 0.07959867371632416 root_mean_squared_error 0.08090187442101399 root_mean_squared_error 0.08169346984761178 root_relative_squared_error 0.8141400799806989 root_relative_squared_error 0.7960006142802237 root_relative_squared_error 0.8006738919059103 root_relative_squared_error 0.8065699183078513 root_relative_squared_error 0.8050138562079683 root_relative_squared_error 0.7995649044472635 root_relative_squared_error 0.7928574072620959 root_relative_squared_error 0.7999967712938526 root_relative_squared_error 0.8130944311847098 root_relative_squared_error 0.82105026456588 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 2445.575469000005 usercpu_time_millis 2328.117020999997 usercpu_time_millis 2415.2841220000173 usercpu_time_millis 2313.7862129999858 usercpu_time_millis 2353.1524410000056 usercpu_time_millis 2359.375531000012 usercpu_time_millis 2336.5916119999924 usercpu_time_millis 2315.3954010000034 usercpu_time_millis 2317.155005999993 usercpu_time_millis 2341.561685000002 usercpu_time_millis_testing 39.475471000002926 usercpu_time_millis_testing 33.71020299999827 usercpu_time_millis_testing 32.55555600000548 usercpu_time_millis_testing 31.847697999992874 usercpu_time_millis_testing 32.743216000000075 usercpu_time_millis_testing 32.20671900000127 usercpu_time_millis_testing 32.51347299999452 usercpu_time_millis_testing 32.228699000000915 usercpu_time_millis_testing 31.981418000000872 usercpu_time_millis_testing 32.11845399999902 usercpu_time_millis_training 2406.099998000002 usercpu_time_millis_training 2294.4068179999986 usercpu_time_millis_training 2382.7285660000116 usercpu_time_millis_training 2281.938514999993 usercpu_time_millis_training 2320.409225000006 usercpu_time_millis_training 2327.168812000011 usercpu_time_millis_training 2304.078138999998 usercpu_time_millis_training 2283.1667020000027 usercpu_time_millis_training 2285.1735879999924 usercpu_time_millis_training 2309.443231000003