6683190 1 Jan van Rijn 9954 Supervised Classification 7096 sklearn.model_selection._search.RandomizedSearchCV(estimator=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) 4715522 bootstrap true 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features "auto" 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 1 6902 min_samples_split 2 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 10 6902 n_jobs 1 6902 oob_score false 6902 random_state 1 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 true 6948 threshold 0.0 6949 cv null 7096 error_score "raise" 7096 fit_params {} 7096 iid true 7096 n_iter 50 7096 n_jobs -1 7096 param_distributions {"classifier__min_samples_leaf": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__max_features": [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], "classifier__min_samples_split": [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "classifier__criterion": ["gini", "entropy"], "imputation__strategy": ["mean", "median", "most_frequent"]} 7096 pre_dispatch "2*n_jobs" 7096 random_state 1 7096 refit true 7096 return_train_score true 7096 scoring null 7096 verbose 0 7096 openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 14297181 description https://api.openml.org/data/download/14297181/description.xml -1 14297182 predictions https://api.openml.org/data/download/14297182/predictions.arff -1 14297183 trace https://api.openml.org/data/download/14297183/trace.arff area_under_roc_curve 0.9805916587752529 [0.923414,0.999566,0.999882,1,0.99856,0.993213,0.99779,0.998856,0.995896,0.999645,0.996173,0.999211,0.998244,0.938763,0.964548,0.991083,0.999803,0.976562,0.982264,0.934324,0.997277,0.99781,0.982836,0.999467,0.993845,0.870758,0.981929,0.989564,0.982876,0.998757,0.999625,0.999744,0.961174,0.95709,0.994298,0.997948,0.979818,0.93894,0.995403,0.999527,0.987906,0.996113,0.998264,0.997435,0.996607,0.992681,0.995088,0.994456,0.984789,0.99343,0.985105,0.983329,0.928228,0.936257,0.98767,0.966856,1,0.996252,0.99345,0.933712,0.999921,0.963443,0.986506,0.994851,0.996982,0.995956,0.933101,0.998777,0.935784,0.878216,0.98039,0.997001,0.982225,0.956242,0.953105,0.996271,0.997711,0.909308,0.999665,0.999645,0.990116,0.995541,0.98404,0.964035,0.993963,0.961746,0.999605,0.984513,0.994575,0.999132,0.994456,0.999448,0.995009,0.986308,0.994338,0.995186,0.949455,0.95859,0.917732,0.992168] average_cost 0 f_measure 0.6622919142549043 [0.357143,0.909091,0.882353,1,0.823529,0.444444,0.848485,0.742857,0.722222,0.967742,0.645161,0.888889,0.882353,0.615385,0.733333,0.709677,0.842105,0.5,0.4375,0.24,0.685714,0.933333,0.62069,0.9375,0.648649,0.142857,0.322581,0.606061,0.645161,0.75,0.857143,0.866667,0.823529,0.484848,0.774194,0.810811,0.344828,0.342857,0.823529,0.848485,0.733333,0.722222,0.764706,0.709677,0.8,0.6875,0.75,0.714286,0.625,0.6875,0.514286,0.606061,0.625,0.466667,0.6,0.214286,0.969697,0.764706,0.666667,0.466667,0.909091,0.774194,0.529412,0.702703,0.628571,0.666667,0.30303,0.756757,0.166667,0.363636,0.62069,0.774194,0.411765,0.62069,0.461538,0.787879,0.756757,0.307692,0.888889,0.896552,0.625,0.689655,0.625,0.666667,0.606061,0.857143,0.896552,0.631579,0.8,0.8,0.727273,0.756757,0.705882,0.4,0.75,0.722222,0.533333,0.611111,0.214286,0.733333] kappa 0.6679292929292929 kb_relative_information_score 1115.9976040515592 mean_absolute_error 0.013271712422429534 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 0.666780655017497 [0.416667,0.882353,0.833333,1,0.777778,0.545455,0.823529,0.684211,0.65,1,0.666667,0.8,0.833333,0.8,0.785714,0.733333,0.727273,0.45,0.4375,0.333333,0.631579,1,0.692308,0.9375,0.571429,0.166667,0.333333,0.588235,0.666667,0.75,0.789474,0.928571,0.777778,0.470588,0.8,0.714286,0.384615,0.315789,0.777778,0.823529,0.785714,0.65,0.722222,0.733333,0.857143,0.6875,0.75,0.833333,0.625,0.6875,0.473684,0.588235,0.625,0.5,0.642857,0.25,0.941176,0.722222,0.6,0.5,0.882353,0.8,0.5,0.619048,0.578947,0.714286,0.294118,0.666667,0.25,0.666667,0.692308,0.8,0.388889,0.692308,0.6,0.764706,0.666667,0.4,0.8,1,0.625,0.769231,0.625,0.647059,0.588235,1,1,0.545455,0.736842,0.736842,0.705882,0.666667,0.666667,0.555556,0.75,0.65,0.571429,0.55,0.25,0.785714] predictive_accuracy 0.67125 prior_entropy 6.6438561897747395 recall 0.67125 [0.3125,0.9375,0.9375,1,0.875,0.375,0.875,0.8125,0.8125,0.9375,0.625,1,0.9375,0.5,0.6875,0.6875,1,0.5625,0.4375,0.1875,0.75,0.875,0.5625,0.9375,0.75,0.125,0.3125,0.625,0.625,0.75,0.9375,0.8125,0.875,0.5,0.75,0.9375,0.3125,0.375,0.875,0.875,0.6875,0.8125,0.8125,0.6875,0.75,0.6875,0.75,0.625,0.625,0.6875,0.5625,0.625,0.625,0.4375,0.5625,0.1875,1,0.8125,0.75,0.4375,0.9375,0.75,0.5625,0.8125,0.6875,0.625,0.3125,0.875,0.125,0.25,0.5625,0.75,0.4375,0.5625,0.375,0.8125,0.875,0.25,1,0.8125,0.625,0.625,0.625,0.6875,0.625,0.75,0.8125,0.75,0.875,0.875,0.75,0.875,0.75,0.3125,0.75,0.8125,0.5,0.6875,0.1875,0.6875] relative_absolute_error 0.6702885061833085 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07538311238816858 root_relative_squared_error 0.7576287858204452 total_cost 0 area_under_roc_curve 0.9819143828118783 [0.981132,1,1,1,1,1,1,1,1,1,1,1,1,0.906646,1,1,1,0.990506,0.984177,0.962264,0.993711,1,0.981132,1,1,0.974684,0.990506,1,0.946203,0.993711,1,1,1,0.987342,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.982595,0.987342,1,1,0.971519,1,0.943396,1,1,1,0.924051,1,1,0.984177,1,1,1,0.976266,1,1,0.374214,0.990506,0.993711,0.990506,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.988924,0.718354,1,1,0.992089,1,1,0.993711,1,0.984177,0.996835,0.993671,1,1,0.696203,1] area_under_roc_curve 0.9846818774381021 [0.418239,1,1,1,1,1,0.990506,1,1,1,1,1,0.996835,0.713608,1,1,1,0.981013,0.981013,0.981132,1,1,1,1,1,0.721519,0.993671,0.990506,0.990506,1,1,1,1,1,1,0.981132,0.993711,1,0.993711,1,1,1,1,0.996835,1,0.990506,1,1,0.976266,1,1,0.933544,0.993711,1,1,0.981132,1,1,1,0.995253,1,1,0.993671,1,1,0.993671,0.996835,1,0.943396,0.993711,1,0.993711,1,0.996835,0.993711,1,1,0.97943,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.996835,0.976266,0.981013,1,0.981132,0.987342,1,1] area_under_roc_curve 0.9729976464851523 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[1,1,1,1,1,0.993671,1,1,1,1,0.987421,0.993711,0.996835,1,0.993711,0.943038,1,1,0.968354,0.984177,0.990506,1,1,1,0.996835,0.943396,1,0.976266,0.937107,0.996835,1,1,1,1,0.996835,1,0.962025,0.726266,1,1,0.993671,1,1,1,0.981132,0.996835,1,1,1,1,0.937107,0.993671,0.727848,1,0.993671,0.936709,1,1,0.996835,0.987421,1,0.977848,0.993711,1,1,0.968553,0.41195,1,0.976266,0.949367,1,1,0.996835,0.430818,0.996835,1,1,0.955975,1,1,0.949367,1,1,0.996835,0.971519,1,1,1,0.993711,1,0.962264,1,1,0.993711,0.987421,1,0.623418,0.95283,0.984177,0.977848] area_under_roc_curve 0.9897712910994348 [0.937107,1,1,1,1,1,1,1,0.990506,1,0.974843,1,1,1,1,1,1,1,1,1,1,0.981013,0.927673,0.998418,1,0.993711,0.981132,1,0.971519,1,1,1,1,1,1,1,0.981013,0.96519,1,1,0.949686,0.996835,1,0.984177,1,0.996835,1,1,0.987421,1,0.952532,0.968354,1,0.939873,0.977848,0.943396,1,1,1,0.914557,1,1,0.96519,0.981013,1,1,0.981013,0.987421,0.990506,1,1,0.981132,0.962025,0.996835,0.974684,0.993671,1,0.962264,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.993671,1,0.974684,0.962264,0.996835,0.968354,1] area_under_roc_curve 0.9899925861794442 [0.823899,1,1,1,1,1,1,0.987421,0.996835,1,0.993711,1,1,0.974684,1,1,1,0.987421,0.996835,0.968553,0.996835,1,1,1,1,0.924528,0.987421,0.962025,1,1,1,1,0.981132,0.993711,1,0.993671,0.962025,0.987342,0.973101,1,1,0.987342,0.996835,1,1,0.995253,1,0.982595,1,1,0.990506,1,1,0.957278,0.984177,0.962264,1,1,0.984177,1,1,1,0.968354,0.996835,0.987421,1,0.996835,1,0.993671,1,0.993711,1,0.971519,0.952532,0.966772,0.977848,1,0.981132,1,1,0.993671,0.971698,0.971698,0.984277,1,1,1,1,1,1,1,1,0.982595,0.993671,1,1,0.987421,1,0.939873,1] 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 0.7220247966516623 kappa 0.7409674234945706 kappa 0.6526818486797964 kappa 0.6210626036156943 kappa 0.6336793905182962 kappa 0.6463390566410104 kappa 0.6967423494570582 kappa 0.646269245953415 kappa 0.67156166114006 kappa 0.6463390566410104 kb_relative_information_score 115.22713425523743 kb_relative_information_score 115.92980553271641 kb_relative_information_score 110.17188905388647 kb_relative_information_score 111.11437045132834 kb_relative_information_score 110.01986985016873 kb_relative_information_score 110.90828410603302 kb_relative_information_score 114.09418803071866 kb_relative_information_score 110.13700814887183 kb_relative_information_score 110.04205304705911 kb_relative_information_score 108.353001575541 mean_absolute_error 0.012727139041514079 mean_absolute_error 0.012874963237333479 mean_absolute_error 0.01333646218875007 mean_absolute_error 0.013550121129891003 mean_absolute_error 0.0130189825268688 mean_absolute_error 0.01342552230245259 mean_absolute_error 0.012742161090542733 mean_absolute_error 0.013085628324901783 mean_absolute_error 0.013813569803841385 mean_absolute_error 0.01414257457819961 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 0.725 predictive_accuracy 0.74375 predictive_accuracy 0.65625 predictive_accuracy 0.625 predictive_accuracy 0.6375 predictive_accuracy 0.65 predictive_accuracy 0.7 predictive_accuracy 0.65 predictive_accuracy 0.675 predictive_accuracy 0.65 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 0.725 [0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,1,1,0,0.5,0,1,1,0,1,1,0,0.5,1,0,0,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,0,1,1,1,0,1,1,0,1,1,1,0.5,1,1,0,0,1,1,1,0,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1] recall 0.74375 [0,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,0,0,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,0,0,1,0,1,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,0,1,0,0.5,0.5,1] recall 0.65625 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0,0,1,0,1,0,1,1,0,1,1,0.5,0.5,1,0.5,1,1,1,1,0.5,0,1,1,0,1,0,0,1,1,1,1,1,0.5,1,0,1,0.5,1,0,0.5,1,0,1,1,0,0.5,1,1,0.5,1,1,0.5,0.5,1,0,0,1,1,0,1,1,1,1,0,1,1,1,0.5,0,0,1,1,1,0.5,1,1,0.5,1,0,0,1,1,1,1,0,0.5] recall 0.625 [0.5,0.5,1,1,0.5,0,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0.5,1,1,0.5,1,0,0,0,0,1,1,1,0.5,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0,1,0,0,0.5,1,0.5,1,0,1,0,0,0,1,1,1,1,0,0.5,0,1,0,0.5,0,0.5,0,0.5,1,0,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,0,0.5,0.5,0,0.5] recall 0.6375 [0,1,0.5,1,1,0,1,0.5,1,1,1,1,1,0,1,0.5,1,1,0,0,0.5,1,1,1,1,0,0.5,1,1,0.5,1,0,0.5,0,0,1,0.5,0,0.5,1,0.5,0.5,1,1,1,1,1,0,1,1,0,0,0.5,0,0.5,0.5,1,1,1,0,1,1,1,0,0.5,0,0,1,0,0,0.5,1,1,0,0,1,0,0,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0,0.5,1,0.5,1,1,1] recall 0.65 [0,1,1,1,1,0,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0,0.5,1,0.5,1,0.5,0,0,0,1,1,1,1,0.5,0.5,0,1,0.5,0,1,1,1,0.5,1,0,0,1,1,0,0.5,0.5,1,1,0.5,1,0.5,0,1,1,0.5,1,1,0.5,0,1,0.5,1,1,1,0,0,0.5,1,0,1,0,1,1,0.5,1,1,1,0,1,1,0,1,1,0,1,0.5,1,1,1,1,1,1,1,0,0,0.5] recall 0.7 [0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0.5,1,0.5,0,0,1,1,0.5,0.5,0,1,0,1,1,0.5,0.5,1,1,0.5,1,1,0.5,0,0.5,0,0.5,1,1,0,1,1,0,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0,0,0,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,0.5,1,1,0.5,0,0,1,0.5,1,0,1] recall 0.65 [1,1,1,1,1,0,1,1,1,1,0,1,1,0,0,0.5,1,1,0.5,0,0.5,1,1,1,0.5,0,0,0.5,0,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,1,1,0,0,0.5,0.5,1,0.5,0,1,1,1,1,1,0.5,1,1,1,0,0,1,0,0,1,1,0.5,0,0.5,1,1,0,1,1,0,0,0,0.5,0,0.5,1,1,1,1,0,1,1,0,1,1,0,0,0,0.5] recall 0.675 [0,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,1,1,0,0,0.5,1,0,0,0.5,0.5,1,1,1,1,1,1,1,0.5,0,1,1,0,1,0,0.5,0.5,0.5,1,1,0,1,0,0.5,1,0.5,0.5,0,1,1,1,0,1,1,0.5,0.5,1,1,0.5,0,0,1,1,0,0.5,0.5,0,1,1,0,1,0.5,1,1,0,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,0,1,0.5,0] recall 0.65 [0,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1,1,1,0,0,0.5,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0.5,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,0.5,1,1,0,0,0,1,1,0.5,1,1,0.5,0,1,0,1,0,1,0,1,0,0,0,0.5,0.5,0,1,0,1,0.5,0.5,0,0,0,1,1,0,1,1,1,1,1,0,0.5,1,1,0,0.5,0,1] relative_absolute_error 0.6427848000764677 relative_absolute_error 0.6502506685521948 relative_absolute_error 0.6735586964015176 relative_absolute_error 0.6843495520146959 relative_absolute_error 0.6575243700438778 relative_absolute_error 0.6780566819420488 relative_absolute_error 0.6435434894213492 relative_absolute_error 0.6608903194394828 relative_absolute_error 0.6976550405980487 relative_absolute_error 0.7142714433434135 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 0.07255997004331412 root_mean_squared_error 0.0731440918022139 root_mean_squared_error 0.07602052648095298 root_mean_squared_error 0.07669325725491934 root_mean_squared_error 0.07487400888565814 root_mean_squared_error 0.07625015605951628 root_mean_squared_error 0.07302417465949407 root_mean_squared_error 0.07521549246766851 root_mean_squared_error 0.07692977292570571 root_mean_squared_error 0.07887876076764681 root_relative_squared_error 0.7292551376760624 root_relative_squared_error 0.7351257822401626 root_relative_squared_error 0.7640350384927201 root_relative_squared_error 0.770796237165968 root_relative_squared_error 0.7525121031014064 root_relative_squared_error 0.7663429025923016 root_relative_squared_error 0.73392056960885 root_relative_squared_error 0.7559441422335124 root_relative_squared_error 0.773173309096385 root_relative_squared_error 0.7927613739226974 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