10015042 6892 Scikit-learn Bot 9954 Supervised Classification 8918 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2) 7939763 n_jobs null 8891 remainder "passthrough" 8891 sparse_threshold 0.3 8891 transformer_weights null 8891 memory null 8892 axis 0 8893 copy true 8893 missing_values "NaN" 8893 strategy "mean" 8893 verbose 0 8893 copy true 8894 with_mean true 8894 with_std true 8894 memory null 8895 copy true 8896 fill_value -1 8896 missing_values NaN 8896 strategy "constant" 8896 verbose 0 8896 categorical_features null 8897 categories null 8897 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 8897 handle_unknown "ignore" 8897 n_values null 8897 sparse true 8897 threshold 0.0 8898 memory null 8918 bootstrap false 8919 class_weight null 8919 criterion "entropy" 8919 max_depth null 8919 max_features 0.025990570524802692 8919 max_leaf_nodes null 8919 min_impurity_decrease 0.0 8919 min_impurity_split null 8919 min_samples_leaf 5 8919 min_samples_split 11 8919 min_weight_fraction_leaf 0.0 8919 n_estimators 100 8919 n_jobs null 8919 oob_score false 8919 random_state 64473 8919 verbose 0 8919 warm_start false 8919 openml-python Sklearn_0.20.2. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 20950607 description https://api.openml.org/data/download/20950607/description.xml -1 20950608 predictions https://api.openml.org/data/download/20950608/predictions.arff area_under_roc_curve 0.9961726641414146 [0.991951,0.999763,1,1,0.999171,0.993805,0.999803,0.999014,0.995896,0.999842,0.99854,0.999961,0.999961,0.986506,1,0.999053,1,0.986506,0.990215,0.977943,0.998935,0.999645,0.994634,1,0.998619,0.980074,0.993726,0.999961,0.998146,0.999211,1,1,0.999842,0.989741,0.999605,0.998106,0.994042,0.982323,0.999408,0.999882,0.99712,0.999092,0.999882,0.996054,0.999132,0.993292,0.998974,0.998698,0.998264,0.998264,0.996449,0.989978,0.993411,0.994003,0.994752,0.98761,1,0.999763,0.993056,0.98911,0.998935,0.999842,0.991398,0.999921,0.998935,0.995344,0.988123,1,0.998303,0.992779,0.998895,0.99783,0.996252,0.999369,0.975339,0.999803,0.999921,0.992069,1,0.999369,0.992977,0.996686,0.996686,0.99566,0.995028,0.999369,1,0.997593,0.997356,0.999132,1,0.999842,0.999684,0.995778,0.999724,0.999645,0.994673,0.996409,0.978496,0.999369] average_cost 0 f_measure 0.8061637069962757 [0.72,0.9375,0.969697,1,0.875,0.727273,0.9375,0.909091,0.827586,0.969697,0.75,0.9375,1,0.666667,0.969697,0.866667,0.888889,0.413793,0.5,0.384615,0.702703,0.967742,0.774194,1,0.823529,0.347826,0.571429,0.914286,0.933333,0.810811,0.888889,1,1,0.666667,0.9375,0.75,0.631579,0.296296,0.833333,0.914286,0.787879,0.882353,0.969697,0.848485,0.875,0.933333,0.8125,0.866667,0.857143,0.875,0.827586,0.56,0.875,0.666667,0.689655,0.518519,1,0.941176,0.62069,0.64,0.941176,0.941176,0.538462,0.903226,0.888889,0.774194,0.606061,0.969697,0.823529,0.733333,0.909091,0.764706,0.666667,0.875,0.56,0.903226,0.820513,0.692308,0.969697,0.967742,0.62069,0.727273,0.903226,0.634146,0.75,0.909091,0.941176,0.787879,0.848485,0.909091,0.941176,0.888889,0.9375,0.875,0.9375,0.882353,0.709677,0.764706,0.454545,0.882353] kappa 0.8118686868686869 kb_relative_information_score 918.5237824881976 mean_absolute_error 0.016736558499367435 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 0.8167635558504547 [1,0.9375,0.941176,1,0.875,0.705882,0.9375,0.882353,0.923077,0.941176,0.75,0.9375,1,0.818182,0.941176,0.928571,0.8,0.461538,0.45,0.5,0.619048,1,0.8,1,0.777778,0.571429,0.666667,0.842105,1,0.714286,0.8,1,1,0.647059,0.9375,0.625,0.545455,0.363636,0.75,0.842105,0.764706,0.833333,0.941176,0.823529,0.875,1,0.8125,0.928571,0.789474,0.875,0.923077,0.777778,0.875,0.714286,0.769231,0.636364,1,0.888889,0.692308,0.888889,0.888889,0.888889,0.7,0.933333,0.8,0.8,0.588235,0.941176,0.777778,0.785714,0.882353,0.722222,0.565217,0.875,0.777778,0.933333,0.695652,0.9,0.941176,1,0.692308,0.705882,0.933333,0.52,0.75,0.882353,0.888889,0.764706,0.823529,0.882353,0.888889,0.8,0.9375,0.875,0.9375,0.833333,0.733333,0.722222,0.833333,0.833333] predictive_accuracy 0.81375 prior_entropy 6.6438561897747395 recall 0.81375 [0.5625,0.9375,1,1,0.875,0.75,0.9375,0.9375,0.75,1,0.75,0.9375,1,0.5625,1,0.8125,1,0.375,0.5625,0.3125,0.8125,0.9375,0.75,1,0.875,0.25,0.5,1,0.875,0.9375,1,1,1,0.6875,0.9375,0.9375,0.75,0.25,0.9375,1,0.8125,0.9375,1,0.875,0.875,0.875,0.8125,0.8125,0.9375,0.875,0.75,0.4375,0.875,0.625,0.625,0.4375,1,1,0.5625,0.5,1,1,0.4375,0.875,1,0.75,0.625,1,0.875,0.6875,0.9375,0.8125,0.8125,0.875,0.4375,0.875,1,0.5625,1,0.9375,0.5625,0.75,0.875,0.8125,0.75,0.9375,1,0.8125,0.875,0.9375,1,1,0.9375,0.875,0.9375,0.9375,0.6875,0.8125,0.3125,0.9375] relative_absolute_error 0.8452807322912831 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08602367802753776 root_relative_squared_error 0.8645704942535835 total_cost 0 area_under_roc_curve 0.9957333213916091 [0.987421,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.984177,1,1,1,0.987342,0.987342,1,1,1,1,1,0.987421,0.987342,0.993671,1,1,0.993711,1,1,0.996835,0.984177,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.996835,1,1,0.996835,0.993711,1,1,1,1,0.993671,1,1,0.996835,1,1,0.996835,0.993671,1,1,0.943396,1,1,1,1,1,1,1,0.955696,1,1,1,1,0.993711,1,0.981013,1,1,1,0.990506,1,1,1,1,0.996835,1,1,0.981132,0.993671,0.927215,1] area_under_roc_curve 0.9966426339463418 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.939873,1,1,1,0.990506,0.993671,0.937107,1,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.990506,1,1,1,1,0.996835,1,1,0.968354,1,0.993671,1,0.981132,1,1,1,0.987342,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.984177,1,1,0.993671,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.993671,0.996835,1,0.981132,0.990506,1,1] area_under_roc_curve 0.9966421363744924 [1,1,1,1,1,0.993671,1,1,0.996835,1,0.996835,1,1,0.993671,1,1,1,0.993671,1,0.962025,1,1,0.971519,1,1,0.990506,1,1,1,1,1,1,1,0.968354,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.952532,1,1,1,1,1,1,1,1,0.974843,0.990506,1,0.996835,0.993711,1,0.968553,1,1,0.993671,1,1,1,1,0.990506,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.962264,1] area_under_roc_curve 0.9964050433882654 [1,1,1,1,0.996835,0.984177,1,1,0.987342,1,0.993671,1,1,1,1,1,1,0.987342,0.918239,0.990506,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.968354,1,1,1,1,1,1,1,1,1,0.981013,0.977848,1,1,0.990506,1,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,0.996835,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987342,0.990506,0.993711,1] area_under_roc_curve 0.9949847245442239 [0.993671,1,1,1,1,1,1,1,0.987421,1,1,1,1,0.924528,1,1,1,0.996835,0.987421,0.993671,0.996835,1,0.996835,1,1,0.968354,0.993671,1,1,1,1,1,1,0.990506,1,1,0.996835,0.977848,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.987421,0.981013,1,0.968354,0.996835,1,0.996835,1,1,1,1,0.981132,1,1,1,0.993711,1,1,0.996835,0.996835,1,1,0.993711,0.876582,1,1,1,1,1,0.993711,1,1,0.990506,1,1,1,1,1,1,1,0.996835,1,0.987421,1,1,1,1,0.955975,1] area_under_roc_curve 0.9964826645967679 [0.971519,1,1,1,1,0.971519,1,1,1,1,0.996835,1,1,0.993711,1,1,1,0.984177,1,0.968354,0.996835,0.996835,0.996835,1,1,1,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.968354,1,1,0.996835,1,1,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.993671,0.987342,1,1,0.987342,1,1,1,0.993711,1,1,0.990506,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,0.981132,0.968553,0.996835] area_under_roc_curve 0.9975526928588485 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.955975,1,0.993671,1,1,1,1,1,0.968553,0.993711,1,1,1,1,1,1,0.993711,1,1,0.984177,0.984177,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.993711,0.977848,1,1,0.996835,0.993671,1,1,0.987342,1,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,0.977848,1] area_under_roc_curve 0.9970401938539923 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993671,1,1,1,1,1,1,0.993711,1,0.993711,0.996835,1,1,1,1,1,1,0.993671,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.993671,1,0.993711,1,0.977848,1,1,0.990506,1,1,1,1,1,1,0.962264,0.918239,1,1,0.996835,1,1,1,1,0.984177,1,1,0.993711,1,1,0.977848,1,1,1,0.990506,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1] area_under_roc_curve 0.9952633647798741 [1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,0.996835,1,1,1,0.974843,1,0.893082,1,1,1,1,1,1,0.993711,1,0.996835,1,1,1,1,0.993711,1,1,0.987342,0.96519,1,1,0.987421,1,1,0.993671,1,0.93038,1,1,0.993711,1,0.990506,0.993671,0.996835,0.977848,0.996835,0.962264,1,1,0.996835,0.981013,0.993671,1,1,1,1,1,0.996835,1,1,0.993711,1,0.987421,0.990506,1,1,1,1,1,1,0.996835,0.993671,0.993711,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,0.996835,0.968553,1,0.996835,1] area_under_roc_curve 0.9981846091075548 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.987421,1,1,1,1,0.996835,0.987421,0.993711,1,0.993671,1,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.996835,0.996835,0.996835,1,1,1,1,0.987342,1,1,1,0.981013,1,1,1,0.993671,1,1,1,1,0.993711,0.987342,1,1,0.993671,1,1,1,1,1,0.962264,1,0.987421,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1] 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 0.7852942337293286 kappa 0.8357743476372824 kappa 0.8294445102451735 kappa 0.8294781716270625 kappa 0.8104639684106614 kappa 0.8231346229767075 kappa 0.8104714522624971 kappa 0.8168107702633345 kappa 0.7978521794061908 kappa 0.7789270064348032 kb_relative_information_score 90.87406394356621 kb_relative_information_score 91.78916606432136 kb_relative_information_score 92.15925543317714 kb_relative_information_score 91.92467006178987 kb_relative_information_score 90.90650612622215 kb_relative_information_score 91.78410762289168 kb_relative_information_score 93.25995151464191 kb_relative_information_score 92.17046964999471 kb_relative_information_score 91.43409591479059 kb_relative_information_score 92.22149615680257 mean_absolute_error 0.016784155672253272 mean_absolute_error 0.016743818788593115 mean_absolute_error 0.016717186056220544 mean_absolute_error 0.01678078173782897 mean_absolute_error 0.016791020222670393 mean_absolute_error 0.016756197609224314 mean_absolute_error 0.01659197508169803 mean_absolute_error 0.016687618966402674 mean_absolute_error 0.016749609917889798 mean_absolute_error 0.01676322094089295 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 0.7875 predictive_accuracy 0.8375 predictive_accuracy 0.83125 predictive_accuracy 0.83125 predictive_accuracy 0.8125 predictive_accuracy 0.825 predictive_accuracy 0.8125 predictive_accuracy 0.81875 predictive_accuracy 0.8 predictive_accuracy 0.78125 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 0.7875 [1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,0,0,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,0,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,0,1,1,0,1,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0,1] recall 0.8375 [1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0,1,1,0,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1] recall 0.83125 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0,1,1,1,0,1,0,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.83125 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5] recall 0.8125 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0,0.5,1,0,0.5,1,1,0.5,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,1] recall 0.825 [0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0.5,0.5,0.5,0.5,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0,1] recall 0.8125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0,0.5,0,0.5,1,1,1,0,1,1,0.5,0,1,1,0,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,0.5,1,0,1] recall 0.81875 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0,1,1,0,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1] recall 0.8 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,0,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,1,1,0.5,0.5,1,1,1,1,0.5,0,0.5,0.5,1,1,1,1,0.5,0,1,1,0.5,1,1,1,0.5,1,1,0,1,0,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0,0.5,0.5,1] recall 0.78125 [1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,0.5,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,0,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,0,0,1,0,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1] relative_absolute_error 0.84768462991178 relative_absolute_error 0.8456474135653075 relative_absolute_error 0.8443023260717432 relative_absolute_error 0.8475142291832799 relative_absolute_error 0.8480313243772912 relative_absolute_error 0.8462726065264792 relative_absolute_error 0.8379785394796971 relative_absolute_error 0.8428090387072044 relative_absolute_error 0.8459398948429177 relative_absolute_error 0.8466273202471173 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 0.08626584936522821 root_mean_squared_error 0.08601958610637646 root_mean_squared_error 0.08592036420959716 root_mean_squared_error 0.08620808668905389 root_mean_squared_error 0.08621614913191981 root_mean_squared_error 0.08616341319974559 root_mean_squared_error 0.08543821601444251 root_mean_squared_error 0.0857776477838832 root_mean_squared_error 0.08612366020040946 root_mean_squared_error 0.0861004416364478 root_relative_squared_error 0.867004407775092 root_relative_squared_error 0.8645293688985421 root_relative_squared_error 0.8635321513148927 root_relative_squared_error 0.8664238710364341 root_relative_squared_error 0.8665049016360704 root_relative_squared_error 0.8659748855754902 root_relative_squared_error 0.8586863796279988 root_relative_squared_error 0.8620977972678813 root_relative_squared_error 0.8655753528994623 root_relative_squared_error 0.8653419975514873 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 556.1663019989282 usercpu_time_millis 545.7897440001034 usercpu_time_millis 531.9234159996995 usercpu_time_millis 537.8636389996245 usercpu_time_millis 530.2874249991874 usercpu_time_millis 531.8622669992692 usercpu_time_millis 532.0883829990635 usercpu_time_millis 533.0508280012509 usercpu_time_millis 532.3963689997981 usercpu_time_millis 530.4377799993745 usercpu_time_millis_testing 41.408096999475674 usercpu_time_millis_testing 41.53244899953279 usercpu_time_millis_testing 40.933696999672975 usercpu_time_millis_testing 40.855635999832884 usercpu_time_millis_testing 41.02368099938758 usercpu_time_millis_testing 41.60891699939384 usercpu_time_millis_testing 41.19714899934479 usercpu_time_millis_testing 41.290189000392274 usercpu_time_millis_testing 41.11322500011738 usercpu_time_millis_testing 41.34369599978527 usercpu_time_millis_training 514.7582049994526 usercpu_time_millis_training 504.2572950005706 usercpu_time_millis_training 490.9897190000265 usercpu_time_millis_training 497.00800299979164 usercpu_time_millis_training 489.2637439997998 usercpu_time_millis_training 490.25334999987535 usercpu_time_millis_training 490.8912339997187 usercpu_time_millis_training 491.76063900085865 usercpu_time_millis_training 491.28314399968076 usercpu_time_millis_training 489.0940839995892