5973730 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) 4008463 bootstrap true 6902 class_weight null 6902 criterion "gini" 6902 max_depth null 6902 max_features 0.6073026756626932 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 4 6902 min_samples_split 10 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 100 6902 n_jobs 1 6902 oob_score false 6902 random_state 34827 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 "mean" 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 12870819 description https://api.openml.org/data/download/12870819/description.xml -1 12870820 predictions https://api.openml.org/data/download/12870820/predictions.arff area_under_roc_curve 0.9963687657828282 [0.994476,0.999684,1,1,0.999842,0.995896,0.99925,0.999605,0.997554,0.999921,0.998816,0.999961,1,0.99203,0.999961,0.999724,1,0.986506,0.991162,0.98402,0.999566,1,0.997001,1,0.998777,0.970762,0.985204,0.998935,0.998461,1,0.999803,0.999842,0.998935,0.991083,0.999329,0.997869,0.992937,0.985085,0.999171,0.999763,0.99925,0.999487,0.999092,0.998935,0.999132,0.999605,0.998658,0.99929,0.997199,0.996922,0.998658,0.994397,0.996133,0.989031,0.998027,0.983665,0.999961,0.999527,0.992188,0.993963,0.999921,0.999566,0.993529,0.998461,0.998895,0.996607,0.988636,0.999724,0.989741,0.994081,0.996607,0.997672,0.994358,1,0.986427,0.998461,0.999842,0.990294,0.999684,0.999645,0.996133,0.996922,0.998382,0.995936,0.996094,0.997317,1,0.995226,0.999053,1,0.999724,0.999014,0.998777,0.995226,0.997909,0.998382,0.99708,0.995226,0.97459,0.999684] average_cost 0 f_measure 0.8076742812767379 [0.714286,0.833333,1,1,0.941176,0.740741,0.9375,0.909091,0.823529,0.9375,0.875,0.9375,0.909091,0.758621,0.941176,0.933333,0.941176,0.625,0.588235,0.466667,0.9375,0.967742,0.774194,1,0.848485,0.434783,0.3125,0.857143,0.9375,0.967742,0.9375,0.875,0.83871,0.606061,0.9375,0.8,0.571429,0.606061,0.909091,0.9375,0.823529,0.933333,0.736842,0.742857,0.903226,0.933333,0.8125,0.896552,0.727273,0.8,0.903226,0.628571,0.764706,0.666667,0.8,0.533333,0.969697,0.866667,0.4375,0.538462,0.969697,0.820513,0.727273,0.888889,0.882353,0.789474,0.540541,0.888889,0.56,0.666667,0.740741,0.83871,0.606061,1,0.740741,0.967742,0.969697,0.769231,0.967742,0.933333,0.666667,0.75,0.787879,0.606061,0.705882,0.875,1,0.774194,0.833333,1,0.888889,0.882353,0.9375,0.857143,0.909091,0.8,0.83871,0.709677,0.48,0.909091] kappa 0.8087121212121212 kb_relative_information_score 1176.209591363868 mean_absolute_error 0.012905324698057698 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 0.8177045294933297 [0.833333,0.75,1,1,0.888889,0.909091,0.9375,0.882353,0.777778,0.9375,0.875,0.9375,0.882353,0.846154,0.888889,1,0.888889,0.625,0.555556,0.5,0.9375,1,0.8,1,0.823529,0.714286,0.3125,0.789474,0.9375,1,0.9375,0.875,0.866667,0.588235,0.9375,0.736842,0.526316,0.588235,0.882353,0.9375,0.777778,1,0.636364,0.684211,0.933333,1,0.8125,1,0.705882,0.857143,0.933333,0.578947,0.722222,0.714286,0.736842,0.571429,0.941176,0.928571,0.4375,0.7,0.941176,0.695652,0.705882,0.8,0.833333,0.681818,0.47619,0.8,0.777778,0.714286,0.909091,0.866667,0.588235,1,0.909091,1,0.941176,1,1,1,0.818182,0.75,0.764706,0.588235,0.666667,0.875,1,0.8,0.75,1,0.8,0.833333,0.9375,1,0.882353,0.736842,0.866667,0.733333,0.666667,0.882353] predictive_accuracy 0.810625 prior_entropy 6.6438561897747395 recall 0.810625 [0.625,0.9375,1,1,1,0.625,0.9375,0.9375,0.875,0.9375,0.875,0.9375,0.9375,0.6875,1,0.875,1,0.625,0.625,0.4375,0.9375,0.9375,0.75,1,0.875,0.3125,0.3125,0.9375,0.9375,0.9375,0.9375,0.875,0.8125,0.625,0.9375,0.875,0.625,0.625,0.9375,0.9375,0.875,0.875,0.875,0.8125,0.875,0.875,0.8125,0.8125,0.75,0.75,0.875,0.6875,0.8125,0.625,0.875,0.5,1,0.8125,0.4375,0.4375,1,1,0.75,1,0.9375,0.9375,0.625,1,0.4375,0.625,0.625,0.8125,0.625,1,0.625,0.9375,1,0.625,0.9375,0.875,0.5625,0.75,0.8125,0.625,0.75,0.875,1,0.75,0.9375,1,1,0.9375,0.9375,0.75,0.9375,0.875,0.8125,0.6875,0.375,0.9375] relative_absolute_error 0.6517840756594785 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07108699820155498 root_relative_squared_error 0.7144512136582629 total_cost 0 area_under_roc_curve 0.9956512220364617 [0.993711,1,1,1,1,1,1,1,0.996835,1,0.993671,1,1,0.996835,1,1,1,0.987342,0.984177,0.993711,1,1,0.993711,1,1,0.996835,0.971519,1,1,1,0.996835,1,0.996835,0.981013,1,0.981132,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.968354,1,1,1,0.993671,1,0.993711,1,1,0.993711,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,0.990506,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.984177,0.990506,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.987342,0.901899,1] area_under_roc_curve 0.9954161193376324 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,0.958861,0.993671,0.987421,1,1,1,1,1,0.933544,0.993671,1,1,1,1,1,1,1,1,0.993711,0.974843,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.955696,1,1,1,0.968553,1,1,1,0.971519,1,1,1,0.993671,1,1,1,1,0.968553,1,0.996835,1,1,1,1,1,1,0.981013,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,1,0.987342,0.990506,1] area_under_roc_curve 0.9973947137966723 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.990506,1,0.949367,1,1,0.993671,1,1,0.993671,1,1,1,1,1,1,1,0.981013,1,1,0.993711,0.962264,1,1,0.996835,1,1,1,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.981132,0.993671,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,0.996835,0.943396,1] area_under_roc_curve 0.9974713398614761 [0.996835,1,1,1,1,0.996835,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.962264,0.990506,1,1,1,1,1,0.993671,0.971519,1,1,1,1,1,1,0.990506,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,0.996835,1,0.96519,1,1,1,1,0.996835,0.987421,1,0.993711,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.987342,0.996835,1,1] area_under_roc_curve 0.9951379766738315 [1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.987421,1,1,1,0.996835,0.993711,0.987342,1,1,1,1,1,0.981013,0.987342,1,1,1,1,1,1,0.987342,1,1,1,0.936709,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.987342,1,0.993671,0.974684,1,0.996835,1,1,1,1,1,1,0.996835,1,0.968553,1,0.993671,0.971519,0.996835,1,1,1,0.949367,1,1,0.962025,1,1,1,1,1,0.993671,0.987421,1,1,1,1,1,1,0.993671,1,1,0.977848,1,0.996835,1,0.993711,1] area_under_roc_curve 0.9960077322665397 [0.984177,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,0.949367,1,1,1,1,1,1,0.974684,0.993711,1,1,1,1,0.993671,0.993671,0.987421,0.993671,0.977848,0.987342,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.993671,0.993711,1,0.996835,0.987421,1,1,0.996835,0.993711,1,0.971519,1,0.990506,0.993671,1,1,0.971519,1,1,1,1,1,0.993711,0.996835,1,0.996835,1,1,1,1,0.996835,1,1,0.996835,0.993711,1,1,1,1,0.981132,1,1] area_under_roc_curve 0.9980676797229519 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.993671,0.996835,1,0.996835,1,0.993671,0.918239,0.987421,0.996835,1,1,1,1,1,0.987421,1,1,0.993671,1,1,1,1,1,1,1,0.987421,0.993671,1,1,0.993711,1,1,1,1,1,0.993671,1,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,0.974843,1,1,1,1,0.993671,1,1,1,1,1,0.996835,1] area_under_roc_curve 0.9966841811957649 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987421,0.977848,0.996835,1,1,1,1,1,1,0.987421,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.990506,0.987342,1,0.996835,0.968354,1,1,0.977848,0.981132,1,1,1,1,1,0.949686,0.987421,1,0.996835,0.990506,1,1,1,1,1,1,1,1,1,1,0.993671,0.993711,0.984177,1,0.996835,0.987342,1,0.993711,1,1,1,1,1,0.993711,1,0.996835,0.993671,1,0.990506,1] area_under_roc_curve 0.996090826765385 [0.987421,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,1,1,0.949686,0.987421,1,1,1,1,1,1,1,1,1,0.996835,0.96519,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993711,1,0.996835,0.993671,1,0.971519,0.996835,0.981132,1,1,1,0.987342,1,1,0.996835,1,1,1,0.990506,1,0.993671,0.981132,1,0.981132,0.974684,1,1,0.987342,1,1,1,1,1,1,1,0.993711,1,1,1,0.990506,1,1,1,1,1,0.987342,1,0.996835,0.993711,1,0.958861,1] area_under_roc_curve 0.9968827123636652 [0.993711,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.971519,1,1,1,0.993711,0.990506,1,1,1,1,1,1,0.924528,0.987421,1,0.996835,1,1,1,1,1,1,0.993671,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.993711,1,1,0.993671,0.996835,1,0.996835,0.971519,1,0.993711,1,0.996835,1,1,0.993711,1,1,0.984177,1,0.996835,0.996835,1,1,1,0.996835,1,0.974843,1,0.968553,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1] 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 0.7914938988271532 kappa 0.8294377763739735 kappa 0.8420720151610865 kappa 0.7915350600126342 kappa 0.8168035375868604 kappa 0.7788658979624072 kappa 0.854666087437305 kappa 0.7915185974887466 kappa 0.7851840151634812 kappa 0.8041461006910168 kb_relative_information_score 116.66402235394675 kb_relative_information_score 117.01847361590495 kb_relative_information_score 118.26598835550845 kb_relative_information_score 117.85042272201756 kb_relative_information_score 117.40423385744069 kb_relative_information_score 116.64269930345253 kb_relative_information_score 120.2896320064663 kb_relative_information_score 117.70402155923962 kb_relative_information_score 116.8172022306425 kb_relative_information_score 117.55289535924828 mean_absolute_error 0.01295466931678175 mean_absolute_error 0.012888218240248512 mean_absolute_error 0.012915306556057896 mean_absolute_error 0.013040346453141232 mean_absolute_error 0.01281572692674666 mean_absolute_error 0.013086015201268278 mean_absolute_error 0.012468577790819203 mean_absolute_error 0.012818785053754833 mean_absolute_error 0.013082233422630032 mean_absolute_error 0.012983368019128444 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 0.79375 predictive_accuracy 0.83125 predictive_accuracy 0.84375 predictive_accuracy 0.79375 predictive_accuracy 0.81875 predictive_accuracy 0.78125 predictive_accuracy 0.85625 predictive_accuracy 0.79375 predictive_accuracy 0.7875 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 0.79375 [1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0,0.5,1,1,1,0,1,0,1,0,1,1,0,1,1,0.5,0,1,0,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1] recall 0.83125 [0,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] recall 0.84375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.79375 [0.5,0.5,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5] recall 0.81875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1] recall 0.78125 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,0.5,0,1,1,1,1,0.5,0.5,0,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1] recall 0.85625 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,0,0,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.79375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0.5,0.5,1,1,0.5,1,1,0,0,1,1,1,1,1,0,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0.5,0.5,1,0,1,1,1,1,1,0,1,0.5,0.5,0,0.5,1] recall 0.7875 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,0.5,0.5,0.5,1,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,0.5,0,1,1,0.5,1,1,1,0.5,1,0.5,0,1,0,0,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,1] recall 0.80625 [1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,0,0,1,0,1,0,1,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1] relative_absolute_error 0.6542762281202893 relative_absolute_error 0.6509201131438631 relative_absolute_error 0.6522882099019128 relative_absolute_error 0.658603356219253 relative_absolute_error 0.6472589356942747 relative_absolute_error 0.6609098586499119 relative_absolute_error 0.6297261510514739 relative_absolute_error 0.6474133865532733 relative_absolute_error 0.6607188597287884 relative_absolute_error 0.6557256575317386 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 0.07167587067153869 root_mean_squared_error 0.07067949168091493 root_mean_squared_error 0.07063092271740745 root_mean_squared_error 0.07159401281589041 root_mean_squared_error 0.07082843053303409 root_mean_squared_error 0.0722092786029503 root_mean_squared_error 0.06915883371368026 root_mean_squared_error 0.0706641906313268 root_mean_squared_error 0.0717112602886482 root_mean_squared_error 0.07166895268353146 root_relative_squared_error 0.7203696046652504 root_relative_squared_error 0.7103556190261835 root_relative_squared_error 0.7098674825764539 root_relative_squared_error 0.7195469022612248 root_relative_squared_error 0.7118525108115941 root_relative_squared_error 0.7257305560855289 root_relative_squared_error 0.695072431414723 root_relative_squared_error 0.7102018376916912 root_relative_squared_error 0.7207252836998763 root_relative_squared_error 0.7203000762697226 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 3057.0096639999065 usercpu_time_millis 2987.428213999806 usercpu_time_millis 3018.8944730000458 usercpu_time_millis 2957.2664080000095 usercpu_time_millis 3006.361340000012 usercpu_time_millis 2956.931109999914 usercpu_time_millis 2920.760859999973 usercpu_time_millis 2929.6662990000186 usercpu_time_millis 2962.562399000035 usercpu_time_millis 2994.419131999962 usercpu_time_millis_testing 45.94644499991318 usercpu_time_millis_testing 40.95630499989511 usercpu_time_millis_testing 40.15091799999482 usercpu_time_millis_testing 40.42795600003046 usercpu_time_millis_testing 40.04633500005639 usercpu_time_millis_testing 40.03143199997794 usercpu_time_millis_testing 39.98797999997805 usercpu_time_millis_testing 39.944699999978184 usercpu_time_millis_testing 40.88087299999188 usercpu_time_millis_testing 40.58392299998559 usercpu_time_millis_training 3011.0632189999933 usercpu_time_millis_training 2946.4719089999107 usercpu_time_millis_training 2978.743555000051 usercpu_time_millis_training 2916.838451999979 usercpu_time_millis_training 2966.315004999956 usercpu_time_millis_training 2916.899677999936 usercpu_time_millis_training 2880.772879999995 usercpu_time_millis_training 2889.7215990000404 usercpu_time_millis_training 2921.681526000043 usercpu_time_millis_training 2953.835208999976