5959249 1 Jan van Rijn 9954 Supervised Classification 6952 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1) 3993990 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 false 6948 threshold 0.0 6949 C 26469.36735465033 6953 cache_size 200 6953 class_weight null 6953 coef0 -0.7039307886873287 6953 decision_function_shape null 6953 degree 1 6953 gamma 0.30673401077557416 6953 kernel "poly" 6953 max_iter -1 6953 probability true 6953 random_state 57301 6953 shrinking true 6953 tol 0.00261821528812489 6953 verbose false 6953 openml-pimp openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 12841921 description https://api.openml.org/data/download/12841921/description.xml -1 12841922 predictions https://api.openml.org/data/download/12841922/predictions.arff area_under_roc_curve 0.9964701704545453 [0.994318,1,1,1,0.999645,0.996646,0.999605,0.999961,0.997514,0.999921,0.998935,1,0.999803,0.985006,0.999724,1,1,0.993411,0.979798,0.978417,0.997948,0.999882,0.995975,1,0.995265,0.97309,0.984612,0.998619,0.998974,1,0.999842,1,0.999882,0.9884,0.999803,0.999132,0.993174,0.983704,0.999961,1,0.999211,0.999921,1,0.999014,0.999448,0.999605,0.999803,0.99708,0.998224,0.997909,0.996686,0.998935,0.999132,0.98836,0.997514,0.990609,1,0.999763,0.993213,0.99416,0.998974,0.999487,0.995818,0.99858,0.999408,0.992858,0.988676,0.999921,0.994239,0.994871,0.995462,0.998185,0.993529,0.999961,0.988597,0.999724,0.999882,0.991951,1,0.999684,0.992779,0.997514,0.999132,0.994476,0.998619,1,1,0.999527,0.999448,1,0.997041,0.999448,0.999014,0.998422,0.999487,0.997514,0.999171,0.998777,0.970249,0.997041] average_cost 0 f_measure 0.826489669451056 [0.722222,0.967742,1,1,0.9375,0.827586,0.969697,0.967742,0.875,0.857143,0.9375,0.941176,0.967742,0.758621,1,0.9375,1,0.533333,0.333333,0.324324,0.8125,0.9375,0.6875,1,0.8,0.413793,0.5,0.8125,0.866667,0.896552,0.969697,1,0.875,0.5,0.941176,0.740741,0.580645,0.545455,0.903226,0.967742,0.882353,0.941176,0.967742,0.848485,0.967742,0.933333,0.9375,0.83871,0.722222,0.903226,0.823529,0.814815,0.909091,0.758621,0.8125,0.514286,1,0.9375,0.666667,0.722222,0.8,0.9375,0.722222,0.909091,0.903226,0.571429,0.533333,1,0.787879,0.764706,0.709677,0.827586,0.5,0.967742,0.764706,0.969697,0.933333,0.516129,1,0.9375,0.8125,0.896552,0.903226,0.592593,0.83871,0.866667,0.933333,0.848485,0.9375,1,0.866667,0.866667,0.875,0.967742,1,0.833333,0.882353,0.857143,0.4375,0.866667] kappa 0.82260101010101 kb_relative_information_score 983.1681846708555 mean_absolute_error 0.016163580486212463 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 0.8358607842311738 [0.65,1,1,1,0.9375,0.923077,0.941176,1,0.875,1,0.9375,0.888889,1,0.846154,1,0.9375,1,0.571429,0.357143,0.285714,0.8125,0.9375,0.6875,1,0.736842,0.461538,0.416667,0.8125,0.928571,1,0.941176,1,0.875,0.45,0.888889,0.909091,0.6,0.529412,0.933333,1,0.833333,0.888889,1,0.823529,1,1,0.9375,0.866667,0.65,0.933333,0.777778,1,0.882353,0.846154,0.8125,0.473684,1,0.9375,0.565217,0.65,0.857143,0.9375,0.65,0.882353,0.933333,0.526316,0.571429,1,0.764706,0.722222,0.733333,0.923077,0.5,1,0.722222,0.941176,1,0.533333,1,0.9375,0.8125,1,0.933333,0.727273,0.866667,0.928571,1,0.823529,0.9375,1,0.928571,0.928571,0.875,1,1,0.75,0.833333,0.789474,0.4375,0.928571] predictive_accuracy 0.824375 prior_entropy 6.6438561897747395 recall 0.824375 [0.8125,0.9375,1,1,0.9375,0.75,1,0.9375,0.875,0.75,0.9375,1,0.9375,0.6875,1,0.9375,1,0.5,0.3125,0.375,0.8125,0.9375,0.6875,1,0.875,0.375,0.625,0.8125,0.8125,0.8125,1,1,0.875,0.5625,1,0.625,0.5625,0.5625,0.875,0.9375,0.9375,1,0.9375,0.875,0.9375,0.875,0.9375,0.8125,0.8125,0.875,0.875,0.6875,0.9375,0.6875,0.8125,0.5625,1,0.9375,0.8125,0.8125,0.75,0.9375,0.8125,0.9375,0.875,0.625,0.5,1,0.8125,0.8125,0.6875,0.75,0.5,0.9375,0.8125,1,0.875,0.5,1,0.9375,0.8125,0.8125,0.875,0.5,0.8125,0.8125,0.875,0.875,0.9375,1,0.8125,0.8125,0.875,0.9375,1,0.9375,0.9375,0.9375,0.4375,0.8125] relative_absolute_error 0.8163424487986077 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08291669221965473 root_relative_squared_error 0.8333441119696222 total_cost 0 area_under_roc_curve 0.996680698192819 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,0.96519,0.987421,0.993711,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.962264,0.996835,1,1,1,1,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.984177,1,0.990506,0.924051,1] area_under_roc_curve 0.9968003642225934 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.939873,1,1,1,0.990506,0.996835,0.993711,1,1,1,1,1,0.971519,0.993671,1,1,1,1,1,1,0.996835,1,0.993711,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,0.993671,1,0.949686,1,0.996835,1,0.996835,1,1,1,1,0.993671,1,0.993671,0.974684,0.996835,0.993711,0.987421,0.996835,1,1,1,1,1,0.993671,1,1,0.990506,1,1,1,0.993671,1,0.987421] area_under_roc_curve 0.9968409163283181 [1,1,1,1,1,0.993671,0.993711,1,0.993671,1,1,1,1,1,0.996835,1,1,0.993671,0.993711,0.962025,0.993711,1,0.987342,1,1,0.952532,0.996835,0.993711,1,1,1,1,1,0.990506,1,1,0.993711,0.968553,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.987342,1,1,1,0.974843,0.996835,0.996835,1,1,0.987421,0.996835,0.993671,0.996835,1,1,0.993711,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1] area_under_roc_curve 0.9958161671045298 [1,1,1,1,1,0.990506,1,1,0.993671,1,1,1,1,1,1,1,1,0.996835,0.90566,0.974684,1,1,1,1,0.974843,0.990506,0.981013,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,0.993671,1,1,1,1,1,0.993671,0.974684,1,1,0.990506,0.977848,0.996835,0.987421,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.949686,0.987342] area_under_roc_curve 0.9966030769843165 [1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.949686,1,1,1,0.996835,1,0.984177,1,1,1,1,0.993671,1,0.977848,1,1,1,1,1,1,0.974684,1,1,0.993671,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.981132,1,1,0.990506,0.996835,1,0.996835,1,0.993711,1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.946203,1,0.996835,0.984177,1,1,1,1,1,0.996835,0.987421,1,1,1,1,1,1,0.996835,1,1,0.996835,0.993711,1,1,0.987421,1] area_under_roc_curve 0.9968767415014728 [0.958861,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.962025,1,1,0.993671,1,0.993671,0.990506,0.993671,1,1,1,1,1,1,0.993671,1,1,0.996835,0.984177,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.977848,1,1,1,1,0.993671,1,0.987421,1,1,0.987342,1,1,1,1,1,0.987342,1,1,0.987342,1,1,0.996835,1,1,0.987421,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671] area_under_roc_curve 0.9956586856142025 [1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.968354,0.996835,1,1,1,1,0.886792,0.930818,0.993671,1,1,0.996835,1,1,0.987421,1,1,0.971519,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,1,0.993671,1,1,1,1,0.996835,1,0.981132,0.987421,1,1,1,1,1,0.993671,1,1,1,1,0.993711,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.933544,1] area_under_roc_curve 0.997514131040522 [1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993711,0.974684,1,1,1,1,1,0.993671,0.974843,0.987421,1,0.993711,1,1,1,1,0.993711,1,1,0.987342,0.993671,1,1,1,1,1,1,1,1,1,0.984177,1,1,0.974843,1,1,1,1,0.981013,1,1,0.993671,1,1,1,1,1,1,0.981132,0.949686,1,0.993671,1,1,1,0.996835,1,1,1,1,1,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1] area_under_roc_curve 0.9971173174906456 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.993711,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993711,1,1,0.996835,0.96519,1,1,1,1,1,0.993671,1,0.990506,1,1,0.993711,1,0.993671,1,1,0.936709,0.996835,0.955975,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.996835,1,1,0.987421,0.974684,1,1,1,1,0.987421,1,0.996835,1,0.993711,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1] area_under_roc_curve 0.9978691485550513 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.981132,0.974684,0.968553,0.996835,1,1,1,1,0.993711,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.990506,0.990506,1,1,0.987342,1,0.993711,0.993711,0.993671,1,1,0.993711,1,1,0.993671,1,0.996835,1,1,1,1,1,1,0.981132,1,0.993711,1,1,1,0.996835,1,1,1,1,1,0.996835,1,1,0.993711,1,0.996835,1] 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 0.8357937949001343 kappa 0.8357354392892399 kappa 0.86101788605046 kappa 0.7978840991631139 kappa 0.7851755321249456 kappa 0.8230787457546797 kappa 0.8167963043392427 kappa 0.8230927183699257 kappa 0.8420533070088845 kappa 0.8042156785347754 kb_relative_information_score 99.15708330851318 kb_relative_information_score 97.29137839318032 kb_relative_information_score 98.7267392933553 kb_relative_information_score 97.13732475656258 kb_relative_information_score 97.26968911875296 kb_relative_information_score 96.20189757252749 kb_relative_information_score 100.71003756378002 kb_relative_information_score 98.71896286066944 kb_relative_information_score 98.9786670409667 kb_relative_information_score 98.9764047625472 mean_absolute_error 0.016051443487241353 mean_absolute_error 0.016280875272180587 mean_absolute_error 0.01614327086713402 mean_absolute_error 0.01627590884455137 mean_absolute_error 0.01629974090364054 mean_absolute_error 0.01640507375910049 mean_absolute_error 0.01586979677442186 mean_absolute_error 0.01612994338779992 mean_absolute_error 0.0160795417185101 mean_absolute_error 0.016100209847544372 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 0.8375 predictive_accuracy 0.8375 predictive_accuracy 0.8625 predictive_accuracy 0.8 predictive_accuracy 0.7875 predictive_accuracy 0.825 predictive_accuracy 0.81875 predictive_accuracy 0.825 predictive_accuracy 0.84375 predictive_accuracy 0.80625 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 0.8375 [1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1] recall 0.8375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,1,1,1,0.5,1,1,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0] recall 0.8625 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.8 [1,1,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,0.5,0,0.5,0,0.5,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,0.5] recall 0.7875 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0.5,1,0,0.5,1,1,0.5,0,1,1,0.5,0.5,1,1,1,1,1,0,1,0,1,0.5,1,1,0,0,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,0,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1] recall 0.825 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,0,1,0.5,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5] recall 0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0.5,0.5,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,1] recall 0.825 [0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1] recall 0.84375 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1] recall 0.80625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,1,0,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] relative_absolute_error 0.810678964002087 relative_absolute_error 0.8222664278879072 relative_absolute_error 0.8153167104613126 relative_absolute_error 0.8220155982096636 relative_absolute_error 0.8232192375576016 relative_absolute_error 0.8285390787424477 relative_absolute_error 0.8015048875970622 relative_absolute_error 0.814643605444439 relative_absolute_error 0.8120980665914179 relative_absolute_error 0.8131419114921385 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 0.08239874529757661 root_mean_squared_error 0.08333916119401606 root_mean_squared_error 0.08276295974037585 root_mean_squared_error 0.0835088689401401 root_mean_squared_error 0.08355061026197852 root_mean_squared_error 0.08414381885221021 root_mean_squared_error 0.08157396230844523 root_mean_squared_error 0.08269252100095476 root_mean_squared_error 0.08250914802499118 root_mean_squared_error 0.08265809206918787 root_relative_squared_error 0.8281385495397662 root_relative_squared_error 0.8375900849196916 root_relative_squared_error 0.8317990424185342 root_relative_squared_error 0.8392957119436624 root_relative_squared_error 0.8397152280127274 root_relative_squared_error 0.8456771986679219 root_relative_squared_error 0.8198491686051704 root_relative_squared_error 0.8310911064507601 root_relative_squared_error 0.8292481386993602 root_relative_squared_error 0.8307450826671133 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 1043.4135059999994 usercpu_time_millis 1047.938919 usercpu_time_millis 1047.5915630000006 usercpu_time_millis 1039.3956710000002 usercpu_time_millis 1048.7919440000005 usercpu_time_millis 1042.2184030000024 usercpu_time_millis 1049.4749669999983 usercpu_time_millis 1051.3488669999997 usercpu_time_millis 1059.063929999999 usercpu_time_millis 1052.784172999999 usercpu_time_millis_testing 136.84193399999955 usercpu_time_millis_testing 132.06702000000004 usercpu_time_millis_testing 132.53725100000048 usercpu_time_millis_testing 132.33068999999986 usercpu_time_millis_testing 130.04074000000008 usercpu_time_millis_testing 130.0757500000014 usercpu_time_millis_testing 127.00535499999965 usercpu_time_millis_testing 131.11116199999984 usercpu_time_millis_testing 133.64231499999946 usercpu_time_millis_testing 130.88650499999943 usercpu_time_millis_training 906.5715719999998 usercpu_time_millis_training 915.8718989999999 usercpu_time_millis_training 915.0543120000001 usercpu_time_millis_training 907.0649810000004 usercpu_time_millis_training 918.7512040000004 usercpu_time_millis_training 912.142653000001 usercpu_time_millis_training 922.4696119999986 usercpu_time_millis_training 920.2377049999999 usercpu_time_millis_training 925.4216149999994 usercpu_time_millis_training 921.8976679999997