6024679 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) 4059364 bootstrap false 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features 0.4312688857604513 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 4 6902 min_samples_split 20 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 100 6902 n_jobs 1 6902 oob_score false 6902 random_state 16479 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 "most_frequent" 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 true 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 12972473 description https://api.openml.org/data/download/12972473/description.xml -1 12972474 predictions https://api.openml.org/data/download/12972474/predictions.arff area_under_roc_curve 0.9944203756313128 [0.981613,0.999527,0.999724,1,0.998935,0.993963,0.999329,0.998856,0.996567,0.999645,0.997199,1,0.997554,0.986979,0.999763,0.998619,1,0.983152,0.985756,0.969618,0.998895,0.999605,0.993608,1,0.99779,0.965988,0.987847,0.996962,0.99925,0.999724,0.999921,1,0.997554,0.986229,0.997988,0.998856,0.987295,0.990649,0.998974,0.999842,0.997199,0.999014,0.999882,0.998974,0.998737,0.999724,0.999053,0.997751,0.996409,0.99491,0.996094,0.993647,0.997869,0.976247,0.995028,0.973801,1,0.999842,0.995699,0.984612,1,0.998501,0.994239,0.997988,0.998027,0.994634,0.979088,1,0.991201,0.986821,0.994121,0.998422,0.993371,0.999014,0.964568,0.996212,0.999369,0.980903,1,0.999961,0.995739,0.995384,0.996646,0.994594,0.993726,0.995344,0.999921,0.990649,0.998146,1,0.999882,0.999684,0.99779,0.989662,0.999053,0.99929,0.99712,0.993687,0.962437,0.99858] average_cost 0 f_measure 0.7401505900142734 [0.592593,0.842105,0.941176,1,0.9375,0.645161,0.882353,0.857143,0.714286,0.967742,0.6875,0.882353,0.685714,0.48,0.9375,0.875,0.888889,0.444444,0.529412,0.16,0.769231,0.969697,0.705882,1,0.8,0.380952,0.3125,0.756757,0.882353,0.914286,0.9375,1,0.875,0.5625,0.810811,0.736842,0.387097,0.52381,0.909091,0.941176,0.727273,0.848485,0.909091,0.882353,0.848485,0.827586,0.83871,0.8,0.8125,0.83871,0.592593,0.6,0.742857,0.48,0.551724,0.105263,1,0.888889,0.5625,0.642857,0.941176,0.714286,0.709677,0.810811,0.777778,0.764706,0.387097,0.864865,0.434783,0.434783,0.6,0.8,0.648649,0.882353,0.8,0.896552,0.888889,0.521739,0.9375,0.967742,0.709677,0.666667,0.774194,0.580645,0.727273,0.875,0.9375,0.555556,0.774194,1,0.864865,0.780488,0.866667,0.315789,0.787879,0.787879,0.727273,0.645161,0.454545,0.774194] kappa 0.7518939393939394 kb_relative_information_score 1134.3565131621458 mean_absolute_error 0.013588146177212686 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 0.7567530773828254 [0.727273,0.727273,0.888889,1,0.9375,0.666667,0.833333,0.789474,0.833333,1,0.6875,0.833333,0.631579,0.666667,0.9375,0.875,0.8,0.545455,0.5,0.222222,0.652174,0.941176,0.666667,1,0.666667,0.8,0.3125,0.666667,0.833333,0.842105,0.9375,1,0.875,0.5625,0.714286,0.636364,0.4,0.423077,0.882353,0.888889,0.705882,0.823529,0.882353,0.833333,0.823529,0.923077,0.866667,0.857143,0.8125,0.866667,0.727273,0.642857,0.684211,0.666667,0.615385,0.333333,1,0.8,0.5625,0.75,0.888889,0.576923,0.733333,0.714286,0.7,0.722222,0.4,0.761905,0.714286,0.714286,0.642857,0.736842,0.571429,0.833333,0.857143,1,0.8,0.857143,0.9375,1,0.733333,0.714286,0.8,0.6,0.705882,0.875,0.9375,0.5,0.8,1,0.761905,0.64,0.928571,1,0.764706,0.764706,0.705882,0.666667,0.833333,0.8] predictive_accuracy 0.754375 prior_entropy 6.6438561897747395 recall 0.754375 [0.5,1,1,1,0.9375,0.625,0.9375,0.9375,0.625,0.9375,0.6875,0.9375,0.75,0.375,0.9375,0.875,1,0.375,0.5625,0.125,0.9375,1,0.75,1,1,0.25,0.3125,0.875,0.9375,1,0.9375,1,0.875,0.5625,0.9375,0.875,0.375,0.6875,0.9375,1,0.75,0.875,0.9375,0.9375,0.875,0.75,0.8125,0.75,0.8125,0.8125,0.5,0.5625,0.8125,0.375,0.5,0.0625,1,1,0.5625,0.5625,1,0.9375,0.6875,0.9375,0.875,0.8125,0.375,1,0.3125,0.3125,0.5625,0.875,0.75,0.9375,0.75,0.8125,1,0.375,0.9375,0.9375,0.6875,0.625,0.75,0.5625,0.75,0.875,0.9375,0.625,0.75,1,1,1,0.8125,0.1875,0.8125,0.8125,0.75,0.625,0.3125,0.75] relative_absolute_error 0.6862700089501343 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07413262827572041 root_relative_squared_error 0.7450609476165326 total_cost 0 area_under_roc_curve 0.9936748666507442 [1,1,1,1,0.996835,1,1,1,0.993671,1,0.990506,1,1,0.971519,1,1,1,0.974684,0.977848,0.974843,1,1,0.993711,1,1,0.987342,0.984177,1,0.996835,1,0.996835,1,0.996835,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,0.987342,1,0.993711,0.993671,1,1,1,1,0.993711,0.993671,1,1,1,1,1,1,0.993671,1,1,0.955975,0.993671,1,0.984177,1,1,1,1,0.981013,1,1,1,0.996835,1,1,0.996835,0.958861,1,0.993671,0.993671,1,1,1,0.996835,0.984177,1,1,1,0.993671,0.863924,1] area_under_roc_curve 0.9944296831462461 [1,1,1,1,1,0.993711,0.996835,1,1,1,0.996835,1,0.990506,0.968354,1,1,1,0.96519,0.996835,0.987421,1,0.993711,1,1,1,0.936709,0.987342,0.993671,1,1,1,1,1,0.996835,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.971519,1,1,0.993711,0.943396,1,1,1,0.933544,1,1,1,0.990506,1,1,1,1,0.974843,0.993711,1,1,1,1,1,1,1,0.987342,1,0.996835,0.996835,0.993671,0.974843,0.987421,0.996835,1,1,0.993671,0.993671,1,1,1,1,0.993671,0.996835,1,1,0.977848,1,1] area_under_roc_curve 0.9943881358968236 [0.993671,1,1,1,0.993671,0.990506,1,1,0.993671,1,1,1,1,0.996835,1,1,1,0.990506,0.987421,0.908228,1,1,0.968354,1,0.987421,0.974684,1,1,1,1,1,1,1,0.962025,0.993711,1,0.993711,0.974843,1,1,1,1,1,1,0.996835,1,0.996835,1,0.984177,1,0.993671,1,1,0.990506,1,0.993671,1,1,0.987421,0.981013,1,0.993711,1,1,1,0.984177,0.990506,1,0.981132,0.987342,0.996835,1,0.993711,0.996835,1,1,1,0.990506,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,0.993711,0.981013,1,1,0.996835,0.993671,0.981132,1] area_under_roc_curve 0.9935972454422417 [0.996835,0.993671,1,1,1,0.984177,1,1,0.990506,1,1,1,1,1,1,1,1,0.987342,0.918239,0.987342,1,1,1,1,0.993711,0.96519,0.977848,1,1,1,1,1,1,0.981013,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.939873,1,0.990506,1,1,1,0.987342,1,1,1,1,0.996835,0.996835,0.952532,1,1,0.958861,0.987342,0.993671,0.981132,1,1,0.984177,1,0.939873,1,1,0.987421,1,1,0.987342,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.987342,0.996835,1,0.993671] area_under_roc_curve 0.992415512299976 [0.987342,1,1,1,1,0.996835,1,1,1,0.996835,0.996835,1,1,0.943396,1,1,1,0.996835,1,0.990506,0.984177,1,1,1,1,0.96519,0.984177,1,1,1,1,1,0.996835,0.974684,1,1,1,0.987342,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993711,0.981132,0.996835,0.968553,0.971519,0.952532,1,0.996835,0.993671,0.993711,1,1,1,1,0.996835,1,0.918239,1,0.971519,0.984177,1,1,1,1,0.863924,1,0.996835,0.968354,1,1,1,1,0.993671,0.993671,0.981132,1,1,1,1,1,1,0.996835,1,0.987421,0.996835,1,0.996835,1,0.974843,0.996835] area_under_roc_curve 0.9928819859087652 [0.917722,1,1,1,1,0.987342,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.933544,1,1,0.971519,1,0.993671,0.990506,0.977848,1,1,1,1,1,0.977848,0.993671,0.993711,1,0.949367,1,1,1,1,0.990506,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.987342,0.958861,1,1,0.993671,1,1,0.993671,0.987421,1,1,1,1,1,0.996835,1,0.984177,0.993671,1,1,0.927215,1,1,0.987342,1,1,1,0.990506,1,0.996835,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.981132,0.993711,0.990506] area_under_roc_curve 0.9954633886633231 [0.996835,1,1,1,1,1,1,1,0.987421,1,1,1,0.987342,1,1,1,1,0.993711,0.993671,0.977848,0.996835,1,1,1,0.993671,0.886792,0.987421,0.981013,1,1,1,1,1,0.993711,1,1,0.993671,1,1,1,1,0.996835,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.996835,0.987342,1,1,0.996835,0.993711,1,1,1,0.984177,1,0.987421,0.968553,1,0.996835,1,0.993711,1,1,1,0.996835,1,1,0.993711,1,1,0.990506,1,0.996835,1,1,1,1,0.924528,1,1,1,1,0.993671,0.981132,0.993711,1,1,1,0.949367,1] area_under_roc_curve 0.9963281685375366 [1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,1,1,0.996835,1,0.993711,0.987342,0.987342,1,1,1,1,1,0.974843,0.981132,0.996835,1,0.996835,1,1,1,1,1,1,0.984177,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.987342,1,1,0.977848,1,1,0.993671,1,1,0.993671,0.993711,1,1,0.955975,0.981132,1,0.990506,0.987342,1,1,1,1,0.993671,1,1,0.993711,1,1,0.996835,0.993711,0.993671,0.996835,0.971519,0.996835,1,0.987421,1,1,1,1,1,0.981132,1,1,0.993671,1,0.981013,1] area_under_roc_curve 0.9967234893718651 [0.962264,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.996835,0.968354,1,1,0.981132,1,1,0.990506,1,1,1,1,0.993711,1,1,0.996835,1,0.946203,1,0.955975,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,0.993671,0.993711,1,0.993711,0.987342,1,1,0.987342,1,1,1,1,1,1,1,0.987421,0.996835,1,1,0.987342,1,1,1,1,1,0.996835,1,1,0.993711,1,0.987342,1] area_under_roc_curve 0.9970033735371386 [0.930818,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.987342,1,1,1,0.987421,0.993671,1,1,1,1,1,1,0.930818,1,1,1,1,1,1,1,0.993711,1,1,0.990506,1,0.993671,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,0.993671,1,0.981013,1,0.993711,1,1,0.996835,0.996835,1,1,0.977848,1,0.987421,1,0.996835,1,1,0.993711,0.993711,1,0.993671,1,0.993671,0.996835,1,1,1,1,1,0.974843,1,0.981132,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.7473351756810107 kappa 0.7536809694864406 kappa 0.7158306034652879 kappa 0.7157745144481289 kappa 0.7662481244570797 kappa 0.7472952696833294 kappa 0.7536031589338598 kappa 0.7599305061991629 kappa 0.7915103652517275 kappa 0.7662481244570797 kb_relative_information_score 113.39363968109457 kb_relative_information_score 112.64282707297185 kb_relative_information_score 112.59897644382758 kb_relative_information_score 112.7631424970498 kb_relative_information_score 112.34036621016989 kb_relative_information_score 111.94944197537745 kb_relative_information_score 114.7879020339534 kb_relative_information_score 114.28724419100469 kb_relative_information_score 114.87149350917576 kb_relative_information_score 114.72147954751964 mean_absolute_error 0.013533292325348792 mean_absolute_error 0.01366636301772775 mean_absolute_error 0.013652366431664082 mean_absolute_error 0.013652342635601517 mean_absolute_error 0.013591415492977238 mean_absolute_error 0.013750927008057304 mean_absolute_error 0.0134002484079663 mean_absolute_error 0.013481452517893216 mean_absolute_error 0.013543973849147895 mean_absolute_error 0.01360908008574252 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 0.75 predictive_accuracy 0.75625 predictive_accuracy 0.71875 predictive_accuracy 0.71875 predictive_accuracy 0.76875 predictive_accuracy 0.75 predictive_accuracy 0.75625 predictive_accuracy 0.7625 predictive_accuracy 0.79375 predictive_accuracy 0.76875 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 0.75 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0,1,0.5,1,1,1,0.5,0,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,0,1,1,0,1,1,1,1,1,1,1,0.5,1,0,0,0.5,1,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,1,0.5,0.5,0.5,1,1,0,1] recall 0.75625 [1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0,1,0,0,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,0,1,1,0.5,0.5,0.5,1,1,1,1,0,0.5,1,1,0,0.5,1] recall 0.71875 [0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,0,0,0.5,1,0,1,1,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,0.5,0,0.5] recall 0.71875 [0.5,1,1,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,1,0.5,0,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,0.5,1,1,0,0.5,1,1,1,1,0.5,1,0,1,0,0.5,0,0.5,0,1,1,0.5,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0,0,1,0,0.5,1,0,0.5] recall 0.76875 [0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,0,0.5,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,0.5,0,0,0.5,0,0,0,1,1,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,0.5] recall 0.75 [0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0.5,0,1,1,0.5,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0,0.5] recall 0.75625 [0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,1,0.5,1,1,0.5,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,0.5,0.5,0.5,1,1,0,1,1,0,0.5,0,1,1,0.5,0,1,1,1,0.5,1,1,0,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,1,0.5,0,1,0.5,1,0,0.5,1] recall 0.7625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1,1,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,1,0,1,1,0,1,0,1,1,0,1,1,1,1,1,1,0,0,0,1,0.5,0.5,1,0,1,1,1,1,1,0,1,1,0.5,1,0,1] recall 0.79375 [0,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,0.5,0.5,0,1,1,0.5,0,1,1,0.5,1,1,1,0.5,1,0.5,0,1,0,0.5,1,1,0.5,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,0,1,1,0,1,0.5,1] recall 0.76875 [0,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,1,1,0,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,1,0.5,1,1,1,0.5,0.5,1,0.5,1,1,1,1,0.5,0.5,0.5,0,0.5,0,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0,0,1,1,1,0,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1] relative_absolute_error 0.6834996123913519 relative_absolute_error 0.6902203544306933 relative_absolute_error 0.6895134561446495 relative_absolute_error 0.6895122543233079 relative_absolute_error 0.6864351259079402 relative_absolute_error 0.694491263033196 relative_absolute_error 0.6767802226245595 relative_absolute_error 0.680881440297636 relative_absolute_error 0.6840390832902966 relative_absolute_error 0.6873272770577019 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 0.0738376266657378 root_mean_squared_error 0.07450882447820445 root_mean_squared_error 0.07457693892459373 root_mean_squared_error 0.07436195449880906 root_mean_squared_error 0.07431359427328245 root_mean_squared_error 0.07513028985886674 root_mean_squared_error 0.07336603051204953 root_mean_squared_error 0.07369856007752328 root_mean_squared_error 0.07352629812348468 root_mean_squared_error 0.0739881488025819 root_relative_squared_error 0.7420960698805841 root_relative_squared_error 0.7488418617110665 root_relative_squared_error 0.7495264376549333 root_relative_squared_error 0.7473657628788795 root_relative_squared_error 0.7468797243247924 root_relative_squared_error 0.7550878237954673 root_relative_squared_error 0.737356350200709 root_relative_squared_error 0.7406983980806373 root_relative_squared_error 0.7389671003012451 root_relative_squared_error 0.7436088742762036 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 6380.089317 usercpu_time_millis 6249.009655000009 usercpu_time_millis 6248.352689999991 usercpu_time_millis 6256.9468709999965 usercpu_time_millis 6210.612087999976 usercpu_time_millis 6572.055060000025 usercpu_time_millis 6521.172407999984 usercpu_time_millis 6255.49873899999 usercpu_time_millis 6261.098086999994 usercpu_time_millis 6259.348325000019 usercpu_time_millis_testing 44.01249899999016 usercpu_time_millis_testing 33.91370200000665 usercpu_time_millis_testing 34.481673000016144 usercpu_time_millis_testing 33.64409499999965 usercpu_time_millis_testing 33.43411399998786 usercpu_time_millis_testing 37.30346900002246 usercpu_time_millis_testing 34.09503699998595 usercpu_time_millis_testing 34.65043399998535 usercpu_time_millis_testing 37.29082499998526 usercpu_time_millis_testing 33.65642599999319 usercpu_time_millis_training 6336.07681800001 usercpu_time_millis_training 6215.095953000002 usercpu_time_millis_training 6213.871016999974 usercpu_time_millis_training 6223.302775999997 usercpu_time_millis_training 6177.177973999988 usercpu_time_millis_training 6534.751591000003 usercpu_time_millis_training 6487.077370999998 usercpu_time_millis_training 6220.848305000004 usercpu_time_millis_training 6223.807262000008 usercpu_time_millis_training 6225.691899000026