5983952 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) 4018685 bootstrap false 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features 0.28363242048443604 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 6 6902 min_samples_split 16 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 100 6902 n_jobs 1 6902 oob_score false 6902 random_state 56872 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 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 12891217 description https://api.openml.org/data/download/12891217/description.xml -1 12891218 predictions https://api.openml.org/data/download/12891218/predictions.arff area_under_roc_curve 0.995707267992424 [0.990175,0.999724,1,1,0.999408,0.994081,0.999527,0.999566,0.997475,0.999842,0.998185,1,0.998816,0.988439,0.999842,0.999092,0.999842,0.985874,0.988518,0.97021,0.998816,1,0.993687,1,0.99854,0.972143,0.990372,0.998382,0.998185,0.999882,0.999921,1,0.999408,0.989978,0.999487,0.999014,0.991162,0.992266,0.998974,0.999882,0.999408,0.998974,0.999961,0.999132,0.999211,0.999566,0.999487,0.998501,0.99779,0.997396,0.99783,0.993174,0.998382,0.983112,0.996765,0.983152,1,0.999882,0.996173,0.987334,1,0.99929,0.99345,0.999171,0.99858,0.995699,0.979759,1,0.996686,0.989504,0.994871,0.998106,0.993884,0.999527,0.975024,0.998974,0.999605,0.989623,1,0.999882,0.994279,0.996922,0.996409,0.995857,0.997869,0.994831,0.999961,0.993371,0.998224,1,0.999803,0.999605,0.998935,0.993371,0.999763,0.999014,0.997948,0.996133,0.96368,0.999171] average_cost 0 f_measure 0.7853234899325297 [0.666667,0.888889,1,1,0.969697,0.689655,0.9375,0.857143,0.83871,0.967742,0.714286,0.903226,0.823529,0.538462,0.969697,0.896552,0.941176,0.533333,0.588235,0.222222,0.810811,0.967742,0.774194,1,0.864865,0.380952,0.352941,0.833333,0.875,0.914286,0.909091,1,0.848485,0.571429,0.909091,0.833333,0.5,0.536585,0.909091,0.969697,0.8,0.903226,0.967742,0.864865,0.823529,0.882353,0.83871,0.827586,0.764706,0.903226,0.8,0.666667,0.823529,0.48,0.689655,0.181818,1,0.969697,0.611111,0.642857,0.941176,0.756757,0.516129,0.823529,0.857143,0.774194,0.571429,0.914286,0.642857,0.592593,0.689655,0.709677,0.666667,0.941176,0.758621,0.967742,0.914286,0.666667,0.969697,0.903226,0.733333,0.666667,0.733333,0.709677,0.727273,0.875,0.969697,0.666667,0.875,0.969697,0.888889,0.789474,0.8,0.72,0.914286,0.823529,0.848485,0.777778,0.434783,0.882353] kappa 0.7916666666666666 kb_relative_information_score 1150.9115099584794 mean_absolute_error 0.01339259087475 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 0.7966338137420075 [0.818182,0.8,1,1,0.941176,0.769231,0.9375,0.789474,0.866667,1,0.833333,0.933333,0.777778,0.7,0.941176,1,0.888889,0.571429,0.555556,0.272727,0.714286,1,0.8,1,0.761905,0.8,0.333333,0.75,0.875,0.842105,0.882353,1,0.823529,0.526316,0.882353,0.75,0.5,0.44,0.882353,0.941176,0.736842,0.933333,1,0.761905,0.777778,0.833333,0.866667,0.923077,0.722222,0.933333,0.857143,0.714286,0.777778,0.666667,0.769231,0.333333,1,0.941176,0.55,0.75,0.888889,0.666667,0.533333,0.777778,0.789474,0.8,0.666667,0.842105,0.75,0.727273,0.769231,0.733333,0.565217,0.888889,0.846154,1,0.842105,1,0.941176,0.933333,0.785714,0.714286,0.785714,0.733333,0.705882,0.875,0.941176,0.565217,0.875,0.941176,0.8,0.681818,0.857143,1,0.842105,0.777778,0.823529,0.7,0.714286,0.833333] predictive_accuracy 0.79375 prior_entropy 6.6438561897747395 recall 0.79375 [0.5625,1,1,1,1,0.625,0.9375,0.9375,0.8125,0.9375,0.625,0.875,0.875,0.4375,1,0.8125,1,0.5,0.625,0.1875,0.9375,0.9375,0.75,1,1,0.25,0.375,0.9375,0.875,1,0.9375,1,0.875,0.625,0.9375,0.9375,0.5,0.6875,0.9375,1,0.875,0.875,0.9375,1,0.875,0.9375,0.8125,0.75,0.8125,0.875,0.75,0.625,0.875,0.375,0.625,0.125,1,1,0.6875,0.5625,1,0.875,0.5,0.875,0.9375,0.75,0.5,1,0.5625,0.5,0.625,0.6875,0.8125,1,0.6875,0.9375,1,0.5,1,0.875,0.6875,0.625,0.6875,0.6875,0.75,0.875,1,0.8125,0.875,1,1,0.9375,0.75,0.5625,1,0.875,0.875,0.875,0.3125,0.9375] relative_absolute_error 0.6763934785227259 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.0728943996391737 root_relative_squared_error 0.7326162815798696 total_cost 0 area_under_roc_curve 0.9945038213518032 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.974684,0.974684,0.993711,1,1,0.993711,1,1,0.993671,0.993671,1,0.993671,1,0.996835,1,0.996835,0.993671,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,0.993671,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.974843,0.990506,1,0.993671,1,1,1,1,0.987342,1,1,1,1,0.993711,1,0.993671,0.971519,1,0.996835,0.993671,1,1,1,1,0.984177,1,0.990506,1,1,0.810127,1] area_under_roc_curve 0.9956129090040603 [1,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,0.952532,1,1,1,0.984177,0.996835,0.987421,1,1,1,1,1,0.955696,0.990506,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,0.952532,1,1,1,0.981132,1,1,1,0.936709,1,1,1,0.993671,1,1,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.987342,1,1,0.990506,0.996835,0.968553,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,0.990506,0.996835,1] area_under_roc_curve 0.9954559250855823 [0.996835,1,1,1,1,0.990506,1,1,0.987342,1,0.996835,1,1,1,0.996835,1,1,0.993671,0.968553,0.93038,1,1,0.958861,1,1,0.981013,1,1,1,1,1,1,1,0.974684,1,1,0.993711,0.962264,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.993711,0.984177,1,1,1,1,1,0.984177,0.993671,1,0.993711,0.987342,1,1,0.993711,1,1,1,1,0.974684,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,1,0.987342,0.968553,1] area_under_roc_curve 0.9955736008279598 [1,0.996835,1,1,0.996835,0.981013,1,1,0.993671,1,1,1,1,1,1,1,1,0.996835,0.943396,0.990506,1,1,1,1,0.987421,0.96519,0.987342,1,1,1,1,1,1,0.981013,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.946203,1,0.990506,1,1,1,0.993671,1,1,0.993671,1,0.996835,1,0.96519,1,1,0.968354,0.990506,0.996835,0.981132,1,1,1,1,0.993671,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.993671,1,1,1,0.996835,1,0.990506] area_under_roc_curve 0.9941557698431656 [0.993671,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.949686,1,1,1,0.993671,1,0.987342,0.987342,1,1,1,1,0.968354,0.987342,1,1,1,1,1,1,0.984177,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.996835,0.993711,0.977848,0.971519,1,0.996835,1,0.993711,1,1,0.993711,1,1,1,0.861635,1,0.990506,0.987342,1,1,0.993711,1,0.889241,1,1,0.990506,1,1,1,1,0.996835,0.993671,0.993711,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,1,0.987421,0.996835] area_under_roc_curve 0.9956104211448131 [0.958861,1,1,1,1,0.993671,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.920886,1,1,0.990506,1,0.990506,0.993671,0.981013,1,1,1,1,1,0.987342,0.993671,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.993671,0.958861,1,1,0.993671,1,1,1,0.987421,1,1,1,1,1,1,1,0.981013,0.996835,1,1,0.977848,1,1,0.990506,1,1,1,0.996835,1,0.996835,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987421,1,1] area_under_roc_curve 0.9968040960114639 [1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.987421,0.993671,0.971519,0.996835,1,1,1,0.993671,0.924528,0.993711,0.987342,1,1,1,1,1,0.993711,1,1,0.993671,1,1,1,1,0.996835,1,1,1,0.996835,1,0.993671,1,1,1,1,1,1,0.993671,0.996835,1,1,1,0.993711,1,1,1,0.987342,1,1,0.968553,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.987342,1,0.993671,1,1,1,1,0.937107,1,1,1,1,1,0.993711,1,1,1,1,0.974684,1] area_under_roc_curve 0.9966841811957646 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.990506,1,0.987421,0.987342,0.987342,1,1,1,1,1,0.974843,0.987421,0.996835,0.987421,0.996835,1,1,1,1,1,1,0.987342,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.990506,1,1,0.984177,1,1,0.996835,1,1,0.996835,1,1,1,0.949686,0.968553,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,1,1,1,0.977848,0.993711,0.993671,0.996835,1,0.987342,1,1,0.993711,1,1,1,1,0.987421,1,1,0.996835,1,0.974684,1] area_under_roc_curve 0.9973947137966721 [0.993711,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.993671,0.981013,1,1,0.981132,0.996835,1,0.993671,1,1,1,1,0.993711,1,0.996835,0.996835,1,0.958861,1,0.955975,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,1,0.996835,0.993711,1,0.987421,0.987342,1,1,0.996835,1,1,1,1,0.990506,1,1,0.993711,0.996835,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,1,0.996835,1] area_under_roc_curve 0.9978308355226494 [0.949686,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,0.987421,0.993671,0.993711,1,1,1,1,1,0.962264,1,1,0.996835,1,1,1,1,0.987421,1,1,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.984177,1,1,1,1,0.993671,1,1,1,0.974684,1,0.993711,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,0.974843,1,0.993711,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.7662850375049349 kappa 0.8357678641926569 kappa 0.7789444597955236 kappa 0.7789095503178175 kappa 0.7852094602598018 kappa 0.7662296635602591 kappa 0.7914938988271532 kappa 0.8041461006910168 kappa 0.8041538340045803 kappa 0.8041461006910168 kb_relative_information_score 114.15746882067033 kb_relative_information_score 114.48351453934546 kb_relative_information_score 114.77822989578479 kb_relative_information_score 114.34684177716802 kb_relative_information_score 114.45713954605972 kb_relative_information_score 114.07285790416985 kb_relative_information_score 116.83032428126143 kb_relative_information_score 115.31719118841639 kb_relative_information_score 115.87242635547292 kb_relative_information_score 116.5955156501313 mean_absolute_error 0.013411171262765156 mean_absolute_error 0.013489432224025748 mean_absolute_error 0.013391811865218022 mean_absolute_error 0.013514980158729265 mean_absolute_error 0.01337958657314938 mean_absolute_error 0.013579318358724427 mean_absolute_error 0.013058036023004472 mean_absolute_error 0.013308893093018356 mean_absolute_error 0.013444416468947062 mean_absolute_error 0.01334826271991838 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 0.76875 predictive_accuracy 0.8375 predictive_accuracy 0.78125 predictive_accuracy 0.78125 predictive_accuracy 0.7875 predictive_accuracy 0.76875 predictive_accuracy 0.79375 predictive_accuracy 0.80625 predictive_accuracy 0.80625 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 0.76875 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,0,1,0,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0,1] recall 0.8375 [1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0,0,1,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1] recall 0.78125 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,0,0.5,0.5,1,0,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1] recall 0.78125 [0.5,1,1,1,1,0,1,1,0.5,1,0,0.5,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,1,1,1,1,0.5,1,0,1,0,0.5,0,0,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0.5] recall 0.7875 [0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,0,0,0.5,0,0,0,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1] recall 0.76875 [0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0.5,0.5,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0,1,1,0,1,1,0.5,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,1,1,1,0,0,1] recall 0.79375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,0,1,0.5,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0.5,1,0,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,1,0,0,1,1,1,1,0.5,1] recall 0.80625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1,1,0.5,0.5,1,1,0.5,1,1,0,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,0,0,0,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0,1] recall 0.80625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,0.5,0.5,0,1,1,0.5,0,1,1,0.5,1,1,1,0.5,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1] recall 0.80625 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,0,0.5,0,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,0,1,0,1,1,0,0.5,1,0,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1] relative_absolute_error 0.677331881957835 relative_absolute_error 0.681284455758875 relative_absolute_error 0.6763541346069697 relative_absolute_error 0.6825747554913759 relative_absolute_error 0.6757366956136039 relative_absolute_error 0.6858241595315355 relative_absolute_error 0.6594967688386086 relative_absolute_error 0.6721663178292087 relative_absolute_error 0.6790109327751029 relative_absolute_error 0.6741546828241595 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 0.07291080377097754 root_mean_squared_error 0.07315523019501032 root_mean_squared_error 0.07302780533517689 root_mean_squared_error 0.07342918370645563 root_mean_squared_error 0.07293112388943769 root_mean_squared_error 0.0739027743274998 root_mean_squared_error 0.07159623216388503 root_mean_squared_error 0.07268605188032762 root_mean_squared_error 0.07277909321172958 root_mean_squared_error 0.07250316346517408 root_relative_squared_error 0.7327811493077634 root_relative_squared_error 0.7352377272997787 root_relative_squared_error 0.7339570592724116 root_relative_squared_error 0.7379910636860345 root_relative_squared_error 0.7329853741823935 root_relative_squared_error 0.7427508285170493 root_relative_squared_error 0.7195692075478228 root_relative_squared_error 0.7305223078162217 root_relative_squared_error 0.731457408380632 root_relative_squared_error 0.7286842100842003 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 4881.406814999991 usercpu_time_millis 4874.357651000011 usercpu_time_millis 4860.535367000011 usercpu_time_millis 4860.090260000007 usercpu_time_millis 4856.854944999995 usercpu_time_millis 4857.556205999998 usercpu_time_millis 4868.506232000002 usercpu_time_millis 4871.349344999984 usercpu_time_millis 4868.706585000012 usercpu_time_millis 4857.607068999997 usercpu_time_millis_testing 52.670762999994736 usercpu_time_millis_testing 39.29989899999953 usercpu_time_millis_testing 39.31789800000729 usercpu_time_millis_testing 39.15951300000131 usercpu_time_millis_testing 39.28375599998901 usercpu_time_millis_testing 39.01183199999991 usercpu_time_millis_testing 38.89288099999533 usercpu_time_millis_testing 39.15458099999114 usercpu_time_millis_testing 39.09825300000591 usercpu_time_millis_testing 39.06382899999983 usercpu_time_millis_training 4828.736051999997 usercpu_time_millis_training 4835.0577520000115 usercpu_time_millis_training 4821.217469000004 usercpu_time_millis_training 4820.930747000006 usercpu_time_millis_training 4817.571189000006 usercpu_time_millis_training 4818.544373999998 usercpu_time_millis_training 4829.613351000006 usercpu_time_millis_training 4832.1947639999935 usercpu_time_millis_training 4829.608332000006 usercpu_time_millis_training 4818.543239999997