10017169 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) 7941890 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 "gini" 8919 max_depth null 8919 max_features 0.15549324447266943 8919 max_leaf_nodes null 8919 min_impurity_decrease 0.0 8919 min_impurity_split null 8919 min_samples_leaf 8 8919 min_samples_split 4 8919 min_weight_fraction_leaf 0.0 8919 n_estimators 100 8919 n_jobs null 8919 oob_score false 8919 random_state 56287 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 20954861 description https://api.openml.org/data/download/20954861/description.xml -1 20954862 predictions https://api.openml.org/data/download/20954862/predictions.arff area_under_roc_curve 0.996445707070707 [0.992661,0.999803,1,1,1,0.996252,0.999645,0.999448,0.997317,0.999842,0.998777,1,0.999842,0.991477,1,0.999211,1,0.989149,0.991517,0.977904,0.999842,1,0.997475,1,0.998658,0.974116,0.987295,0.999369,0.998382,0.999921,0.999882,0.999961,0.999369,0.99057,0.999684,0.99779,0.994042,0.989781,0.999329,0.999803,0.999763,0.999487,0.999211,0.99929,0.999527,0.999882,0.998619,0.998658,0.99783,0.996804,0.999329,0.996291,0.99779,0.987176,0.998895,0.980824,1,0.999645,0.993016,0.987295,1,0.999724,0.993924,0.998146,0.998422,0.996015,0.985993,0.999763,0.994713,0.99349,0.995344,0.998737,0.995896,1,0.985401,0.99925,0.999724,0.994634,0.999921,0.999803,0.997238,0.997633,0.998619,0.995699,0.997001,0.996883,1,0.995423,0.998461,1,0.999921,0.999053,0.999014,0.994949,0.999645,0.998777,0.997159,0.996607,0.966343,0.999803] average_cost 0 f_measure 0.7905904842600348 [0.615385,0.820513,1,1,1,0.785714,0.9375,0.882353,0.848485,0.9375,0.8,0.967742,0.941176,0.714286,0.969697,0.933333,0.864865,0.580645,0.578947,0.296296,0.909091,0.969697,0.8,1,0.864865,0.434783,0.3125,0.83871,0.848485,0.941176,0.909091,0.896552,0.848485,0.666667,0.909091,0.833333,0.533333,0.540541,0.909091,0.969697,0.789474,0.875,0.736842,0.8,0.848485,0.933333,0.8125,0.827586,0.705882,0.727273,0.914286,0.705882,0.8,0.592593,0.75,0.37037,0.969697,0.866667,0.533333,0.571429,0.969697,0.842105,0.62069,0.857143,0.857143,0.756757,0.5625,0.888889,0.434783,0.642857,0.692308,0.83871,0.666667,1,0.785714,0.903226,0.909091,0.72,0.969697,0.933333,0.666667,0.777778,0.727273,0.571429,0.8,0.83871,0.969697,0.722222,0.823529,1,0.914286,0.857143,0.875,0.692308,0.848485,0.882353,0.787879,0.64,0.363636,0.909091] kappa 0.7960858585858586 kb_relative_information_score 1136.7201214090624 mean_absolute_error 0.013675211252841357 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 0.8038617913491208 [0.8,0.695652,1,1,1,0.916667,0.9375,0.833333,0.823529,0.9375,0.736842,1,0.888889,0.833333,0.941176,1,0.761905,0.6,0.5,0.363636,0.882353,0.941176,0.857143,1,0.761905,0.714286,0.3125,0.866667,0.823529,0.888889,0.882353,1,0.823529,0.647059,0.882353,0.75,0.571429,0.47619,0.882353,0.941176,0.681818,0.875,0.636364,0.736842,0.823529,1,0.8125,0.923077,0.666667,0.705882,0.842105,0.666667,0.736842,0.727273,0.75,0.454545,0.941176,0.928571,0.571429,0.666667,0.941176,0.727273,0.692308,0.789474,0.789474,0.666667,0.5625,0.8,0.714286,0.75,0.9,0.866667,0.6,1,0.916667,0.933333,0.882353,1,0.941176,1,1,0.7,0.705882,0.526316,0.857143,0.866667,0.941176,0.65,0.777778,1,0.842105,0.789474,0.875,0.9,0.823529,0.833333,0.764706,0.888889,0.666667,0.882353] predictive_accuracy 0.798125 prior_entropy 6.6438561897747395 recall 0.798125 [0.5,1,1,1,1,0.6875,0.9375,0.9375,0.875,0.9375,0.875,0.9375,1,0.625,1,0.875,1,0.5625,0.6875,0.25,0.9375,1,0.75,1,1,0.3125,0.3125,0.8125,0.875,1,0.9375,0.8125,0.875,0.6875,0.9375,0.9375,0.5,0.625,0.9375,1,0.9375,0.875,0.875,0.875,0.875,0.875,0.8125,0.75,0.75,0.75,1,0.75,0.875,0.5,0.75,0.3125,1,0.8125,0.5,0.5,1,1,0.5625,0.9375,0.9375,0.875,0.5625,1,0.3125,0.5625,0.5625,0.8125,0.75,1,0.6875,0.875,0.9375,0.5625,1,0.875,0.5,0.875,0.75,0.625,0.75,0.8125,1,0.8125,0.875,1,1,0.9375,0.875,0.5625,0.875,0.9375,0.8125,0.5,0.25,0.9375] relative_absolute_error 0.6906672349919865 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07401787037426845 root_relative_squared_error 0.7439075873109425 total_cost 0 area_under_roc_curve 0.9959278719847147 [0.993711,1,1,1,1,1,1,1,0.993671,1,0.996835,1,1,0.993671,1,0.993711,1,0.990506,0.981013,0.993711,1,1,1,1,1,0.993671,0.977848,1,1,1,0.996835,1,0.996835,0.990506,1,0.987421,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.987421,0.987342,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.993671,0.993671,1,0.996835,0.993671,1,1,1,1,1,1,0.996835,1,1,0.857595,1] area_under_roc_curve 0.9969983978186449 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,0.984177,0.996835,0.981132,1,1,1,1,1,0.96519,0.984177,1,1,1,1,1,1,1,1,0.993711,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.977848,1,1,1,0.943396,1,1,1,0.958861,1,1,1,0.996835,1,1,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993671,0.996835,1] area_under_roc_curve 0.9968013593662924 [1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,1,1,0.987342,0.987421,0.946203,1,1,0.996835,1,1,0.990506,1,1,1,1,1,1,1,0.977848,1,1,0.993711,0.974843,1,1,0.996835,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.981132,0.962025,1,1,1,1,1,1,1,1,0.981132,0.996835,1,1,0.993711,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,1,0.949686,1] area_under_roc_curve 0.9968389260409205 [1,1,1,1,1,0.993671,1,1,0.987342,1,1,1,1,1,1,1,1,1,0.968553,0.987342,1,1,1,1,1,0.984177,0.974684,1,1,1,1,1,1,0.987342,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.949367,1,0.990506,1,1,1,1,1,1,0.996835,1,0.996835,1,0.962025,0.996835,1,1,0.996835,1,0.981132,1,0.993711,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,0.996835,1,0.987421,0.996835,1,0.993711,1] area_under_roc_curve 0.9948250039805748 [1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,0.943396,1,1,1,0.993671,1,0.987342,1,1,1,1,1,0.977848,0.990506,1,1,1,1,1,1,0.987342,1,1,0.993671,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.996835,1,0.993671,0.981013,1,0.996835,1,1,1,1,0.993711,1,0.993671,1,0.968553,1,0.984177,0.949367,1,1,0.993711,1,0.93038,1,1,0.987342,1,1,1,1,1,0.993671,0.987421,0.993711,1,1,1,1,1,0.993671,1,0.968553,0.996835,1,0.996835,1,0.993711,1] area_under_roc_curve 0.9957318286760609 [0.949367,1,1,1,1,0.990506,1,1,1,1,0.993671,1,1,1,1,1,1,0.996835,1,0.936709,1,1,0.996835,1,1,1,0.974684,1,1,1,1,1,0.990506,0.993671,1,0.993671,0.987342,1,1,1,1,0.996835,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,0.990506,1,1,0.993671,1,1,1,0.987421,1,1,0.993671,1,1,0.996835,1,0.987342,1,1,1,0.971519,0.993711,1,1,1,1,1,0.996835,1,0.993671,1,1,1,0.993711,0.996835,1,1,0.996835,1,1,1,1,1,0.987421,0.937107,1] area_under_roc_curve 0.9971611038133904 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.977848,1,1,0.996835,1,0.993671,0.880503,0.987421,0.996835,1,1,1,1,1,0.981132,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,1,1,1,1,0.996835,1,1,1,0.990506,0.993711,1,1,1,1,1,1,0.968553,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.962264,1,1,1,1,0.993671,1,1,1,1,1,0.96519,1] area_under_roc_curve 0.9964876403152614 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987421,0.981013,0.996835,1,1,1,1,1,0.993711,0.993711,1,0.968553,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.993671,1,1,0.955696,1,1,0.993671,0.987421,1,1,1,1,1,0.937107,0.981132,1,0.993671,0.990506,1,1,1,1,0.993671,1,1,0.993711,1,1,0.993671,0.993711,0.993671,0.996835,0.996835,0.987342,1,0.981132,0.993711,1,1,1,1,0.987421,1,1,0.987342,1,0.981013,1] area_under_roc_curve 0.9979084567311519 [0.987421,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.962264,0.993711,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993711,1,1,0.993671,1,0.971519,1,0.981132,1,1,0.996835,0.996835,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,0.987421,0.990506,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1] area_under_roc_curve 0.9975544343603214 [1,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.993671,1,1,1,0.993711,0.996835,0.987421,1,1,1,1,1,0.930818,0.987421,1,0.996835,1,1,1,1,1,0.996835,0.993671,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.987342,1,1,1,1,0.996835,1,1,1,0.977848,0.990506,0.993711,1,0.996835,1,0.996835,1,1,1,0.993671,1,1,0.996835,1,1,1,0.996835,1,0.981132,1,0.974843,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1] 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 0.7789357334596557 kappa 0.8357484107869073 kappa 0.8420844848006317 kappa 0.759958940345059 kappa 0.7472753119570368 kappa 0.7788746298124383 kappa 0.8294243070362474 kappa 0.7852179406190777 kappa 0.8230927183699257 kappa 0.7789095503178175 kb_relative_information_score 112.71827995139594 kb_relative_information_score 114.71807236168563 kb_relative_information_score 113.85956883215393 kb_relative_information_score 113.25387229357192 kb_relative_information_score 113.0408894286251 kb_relative_information_score 111.61341180476609 kb_relative_information_score 115.88265012883316 kb_relative_information_score 113.43593724854145 kb_relative_information_score 114.21581688124058 kb_relative_information_score 113.98162247824713 mean_absolute_error 0.013741528228409669 mean_absolute_error 0.013518652229714725 mean_absolute_error 0.01363539765442891 mean_absolute_error 0.013779095104752271 mean_absolute_error 0.013637368917540787 mean_absolute_error 0.013961786145941476 mean_absolute_error 0.013357147636064912 mean_absolute_error 0.013607307531010656 mean_absolute_error 0.013740891211219335 mean_absolute_error 0.013772937869330953 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 0.78125 predictive_accuracy 0.8375 predictive_accuracy 0.84375 predictive_accuracy 0.7625 predictive_accuracy 0.75 predictive_accuracy 0.78125 predictive_accuracy 0.83125 predictive_accuracy 0.7875 predictive_accuracy 0.825 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 0.78125 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,0,0,0.5,1,0.5,1,1,1,0.5,0.5,1,1,0,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,1] recall 0.8375 [0,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,0.5,1,1,0.5,0.5,1,0,0,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] recall 0.84375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1] recall 0.7625 [0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,0.5,0,0.5] recall 0.75 [0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,0,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,0.5,0,1,1,0.5,1,1,1,0,1,0.5,1,0,1,0,0.5,0.5,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0,0,1] recall 0.78125 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,0.5,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1] recall 0.83125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1,1,1,1,1,0,1] recall 0.7875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,0,1,1,1,1,1,0,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0,1,0,0.5,1,0,1,1,1,1,1,0,1,1,0,0,0,1] recall 0.825 [0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,0.5,1,0.5,1,1,1,0,1,1,0.5,1,0.5,1,0,1,0,0.5,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] recall 0.78125 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,0,0.5,1,1,0.5,1,0.5,0,0,1,0.5,1,0,0,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1] relative_absolute_error 0.6940165771924065 relative_absolute_error 0.6827602136219546 relative_absolute_error 0.6886564471933783 relative_absolute_error 0.6959138941794065 relative_absolute_error 0.6887560059364022 relative_absolute_error 0.7051407144414875 relative_absolute_error 0.6746034159628732 relative_absolute_error 0.6872377540914463 relative_absolute_error 0.6939844046070359 relative_absolute_error 0.6956029226934813 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 0.07441517896694312 root_mean_squared_error 0.07314497648379777 root_mean_squared_error 0.07370837284801889 root_mean_squared_error 0.07454835741823866 root_mean_squared_error 0.07406972500183784 root_mean_squared_error 0.07547700306003911 root_mean_squared_error 0.07236871963403912 root_mean_squared_error 0.07377008988991061 root_mean_squared_error 0.07398797974227621 root_mean_squared_error 0.07464370058692484 root_relative_squared_error 0.7479006889105974 root_relative_squared_error 0.735134673624625 root_relative_squared_error 0.7407970201348428 root_relative_squared_error 0.7492391827078941 root_relative_squared_error 0.7444287459269769 root_relative_squared_error 0.7585724225777453 root_relative_squared_error 0.7273329987410108 root_relative_squared_error 0.7414172997443141 root_relative_squared_error 0.7436071751562007 root_relative_squared_error 0.7501974176074566 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 882.3004760001822 usercpu_time_millis 853.5912979996283 usercpu_time_millis 856.1967240002559 usercpu_time_millis 847.3307510002996 usercpu_time_millis 856.5602880003098 usercpu_time_millis 855.7320129998516 usercpu_time_millis 853.4849259999646 usercpu_time_millis 855.8612710003217 usercpu_time_millis 846.5444919993388 usercpu_time_millis 853.1488579997131 usercpu_time_millis_testing 41.64375400023346 usercpu_time_millis_testing 42.08481599971492 usercpu_time_millis_testing 41.91300599995884 usercpu_time_millis_testing 41.62584200003039 usercpu_time_millis_testing 41.82303400011733 usercpu_time_millis_testing 41.95153099999516 usercpu_time_millis_testing 44.604791999972804 usercpu_time_millis_testing 42.243417000008776 usercpu_time_millis_testing 41.61633699959566 usercpu_time_millis_testing 42.25414399979854 usercpu_time_millis_training 840.6567219999488 usercpu_time_millis_training 811.5064819999134 usercpu_time_millis_training 814.2837180002971 usercpu_time_millis_training 805.7049090002693 usercpu_time_millis_training 814.7372540001925 usercpu_time_millis_training 813.7804819998564 usercpu_time_millis_training 808.8801339999918 usercpu_time_millis_training 813.617854000313 usercpu_time_millis_training 804.9281549997431 usercpu_time_millis_training 810.8947139999145