6131087 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) 4162437 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 15 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 "median" 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__bootstrap": [true, false], "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 13192760 description https://api.openml.org/data/download/13192760/description.xml -1 13192761 predictions https://api.openml.org/data/download/13192761/predictions.arff -1 13192762 trace https://api.openml.org/data/download/13192762/trace.arff area_under_roc_curve 0.9899992108585864 [0.984612,0.998935,1,1,0.997317,0.989741,0.999211,0.998106,0.992898,0.995758,0.993253,0.999211,0.997159,0.949811,0.995896,0.987847,0.999645,0.974096,0.982816,0.931976,0.997988,0.999408,0.984474,1,0.994752,0.967073,0.978871,0.997317,0.996883,0.999961,0.999605,1,0.997317,0.990254,0.997554,0.996804,0.982816,0.974708,0.997396,0.999053,0.995936,0.999053,0.997869,0.994081,0.998619,0.996804,0.997396,0.996843,0.954309,0.995285,0.995305,0.992109,0.961569,0.936336,0.989978,0.979186,1,0.997869,0.99203,0.985026,0.999961,0.997869,0.995778,0.989366,0.998856,0.98761,0.972498,0.999408,0.979897,0.974826,0.987946,0.996528,0.983823,0.997021,0.982146,0.997613,0.998698,0.971019,0.999053,0.999408,0.985815,0.99633,0.985736,0.996804,0.99053,0.994871,0.999803,0.992898,0.996843,0.997554,0.998382,0.999053,0.993647,0.977746,0.995936,0.997593,0.991122,0.99272,0.933377,0.997711] average_cost 0 f_measure 0.6898202167813196 [0.580645,0.764706,1,0.967742,0.8125,0.545455,0.882353,0.8125,0.551724,0.933333,0.689655,0.857143,0.787879,0.516129,0.8125,0.714286,0.820513,0.384615,0.486486,0.230769,0.764706,0.896552,0.545455,0.941176,0.666667,0.24,0.344828,0.736842,0.827586,0.969697,0.882353,1,0.764706,0.333333,0.758621,0.702703,0.536585,0.421053,0.823529,0.864865,0.705882,0.875,0.8,0.709677,0.733333,0.774194,0.774194,0.75,0.645161,0.810811,0.774194,0.689655,0.702703,0.615385,0.571429,0.230769,0.969697,0.8125,0.6,0.516129,0.969697,0.764706,0.6875,0.516129,0.777778,0.4,0.181818,0.857143,0.384615,0.444444,0.625,0.764706,0.315789,0.777778,0.580645,0.848485,0.882353,0.347826,0.909091,0.827586,0.615385,0.75,0.827586,0.727273,0.588235,0.823529,0.941176,0.606061,0.625,0.8,0.705882,0.8,0.827586,0.615385,0.742857,0.722222,0.666667,0.6,0.173913,0.727273] kappa 0.6950757575757576 kb_relative_information_score 1130.189658306701 mean_absolute_error 0.013227631721321586 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 0.6956428021511846 [0.6,0.722222,1,1,0.8125,0.529412,0.833333,0.8125,0.615385,1,0.769231,0.789474,0.764706,0.533333,0.8125,0.833333,0.695652,0.5,0.428571,0.3,0.722222,1,0.529412,0.888889,0.647059,0.333333,0.384615,0.636364,0.923077,0.941176,0.833333,1,0.722222,0.357143,0.846154,0.619048,0.44,0.363636,0.777778,0.761905,0.666667,0.875,0.736842,0.733333,0.785714,0.8,0.8,0.75,0.666667,0.714286,0.8,0.769231,0.619048,0.8,0.666667,0.3,0.941176,0.8125,0.642857,0.533333,0.941176,0.722222,0.6875,0.533333,0.7,0.428571,0.176471,0.789474,0.5,0.545455,0.625,0.722222,0.272727,0.7,0.6,0.823529,0.833333,0.571429,0.882353,0.923077,0.8,0.75,0.923077,0.705882,0.555556,0.777778,0.888889,0.588235,0.625,0.736842,0.666667,0.736842,0.923077,0.8,0.684211,0.65,0.647059,0.642857,0.285714,0.705882] predictive_accuracy 0.698125 prior_entropy 6.6438561897747395 recall 0.698125 [0.5625,0.8125,1,0.9375,0.8125,0.5625,0.9375,0.8125,0.5,0.875,0.625,0.9375,0.8125,0.5,0.8125,0.625,1,0.3125,0.5625,0.1875,0.8125,0.8125,0.5625,1,0.6875,0.1875,0.3125,0.875,0.75,1,0.9375,1,0.8125,0.3125,0.6875,0.8125,0.6875,0.5,0.875,1,0.75,0.875,0.875,0.6875,0.6875,0.75,0.75,0.75,0.625,0.9375,0.75,0.625,0.8125,0.5,0.5,0.1875,1,0.8125,0.5625,0.5,1,0.8125,0.6875,0.5,0.875,0.375,0.1875,0.9375,0.3125,0.375,0.625,0.8125,0.375,0.875,0.5625,0.875,0.9375,0.25,0.9375,0.75,0.5,0.75,0.75,0.75,0.625,0.875,1,0.625,0.625,0.875,0.75,0.875,0.75,0.5,0.8125,0.8125,0.6875,0.5625,0.125,0.75] relative_absolute_error 0.6680622081475537 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07452690078652034 root_relative_squared_error 0.7490235354452439 total_cost 0 area_under_roc_curve 0.9916035994347588 [0.955975,1,1,1,0.993671,1,1,1,0.993671,1,0.981013,1,0.996835,0.993671,1,1,1,0.990506,1,0.987421,1,1,0.993711,1,0.993711,0.987342,0.955696,0.996835,0.977848,1,1,1,1,1,1,0.993711,0.993711,0.993711,0.993711,1,1,0.993711,0.996835,0.996835,1,0.974684,1,1,1,0.973101,0.990506,1,1,1,1,1,1,1,0.968553,1,1,1,0.996835,0.993671,1,1,0.96519,1,1,0.91195,0.993671,1,0.987342,1,1,1,1,0.974684,1,1,0.987342,1,0.987421,1,0.968354,0.981013,1,1,0.996835,1,0.990506,1,1,0.971519,1,0.996835,0.993711,0.996835,0.848101,1] area_under_roc_curve 0.9888120969668017 [0.987421,1,1,1,1,1,1,1,1,1,0.996835,0.993671,0.996835,0.693038,1,1,1,0.974684,0.990506,1,1,0.987421,1,1,1,0.958861,0.987342,0.990506,1,1,1,1,1,0.987342,0.993671,0.987421,0.924528,1,1,1,0.987421,1,1,0.971519,1,1,1,1,0.981013,0.996835,1,0.96519,1,1,1,0.974843,1,1,1,0.968354,1,0.981132,1,0.977848,1,0.96519,0.993671,1,1,1,0.981013,1,0.974684,1,1,1,0.993671,0.968354,1,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.946203,1,1,1,0.993671,0.962025,1] area_under_roc_curve 0.9892474723350052 [0.977848,1,1,1,0.996835,0.990506,1,1,0.990506,1,0.981013,1,1,1,0.996835,0.974684,1,0.96519,1,0.89557,1,1,0.976266,1,0.987421,0.993671,1,0.993711,1,1,1,1,1,0.981013,1,0.993711,0.993711,0.930818,1,1,1,1,1,1,1,1,1,1,0.704114,1,0.993671,1,1,0.974684,1,1,1,1,0.981132,0.971519,1,1,1,1,1,0.990506,0.990506,1,0.987421,0.974684,0.996835,0.993671,0.993711,0.996835,1,1,1,0.990506,1,1,1,1,1,0.993671,0.987421,1,1,0.996835,0.984177,1,1,1,1,0.990506,1,1,0.996835,0.987342,0.974843,0.981013] area_under_roc_curve 0.9909556364939102 [0.962025,1,1,1,1,0.987342,1,1,0.993671,1,0.993671,1,1,1,1,1,1,0.996835,0.874214,0.993671,1,1,0.990506,1,0.974843,0.971519,0.97943,1,1,1,1,1,1,0.984177,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.990506,1,1,0.974684,1,0.990506,1,1,1,0.987342,1,1,0.996835,0.987421,1,0.984177,0.952532,1,0.993711,0.958861,0.949367,0.990506,0.962264,1,0.987421,0.985759,1,0.914557,1,1,1,1,1,0.987342,0.993711,1,1,0.996835,1,1,0.996835,1,0.893082,0.96519,1,0.993711,0.996835,0.996835,0.987421,0.993671] area_under_roc_curve 0.9883479868242974 [0.987342,1,1,1,1,0.984177,1,0.996835,0.974843,1,1,1,1,0.858491,1,1,1,0.996835,0.943396,0.993671,0.987342,1,1,1,1,0.996835,0.987342,1,0.987421,1,1,1,0.990506,0.993671,1,1,0.990506,0.931962,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,0.427673,0.96519,0.971519,1,0.981013,1,0.987421,1,1,1,1,0.996835,1,0.968553,1,0.987342,0.993671,1,1,1,0.996855,0.93038,1,1,0.981013,1,1,1,1,0.914557,1,0.981132,1,1,1,1,1,1,1,1,0.987421,0.981013,1,0.996835,0.993711,0.974843,1] area_under_roc_curve 0.9898079870233264 [0.996835,1,1,1,1,0.987342,1,1,1,0.987342,0.993671,0.996835,1,1,1,0.996835,1,0.939873,1,0.686709,1,1,0.993671,1,0.996835,0.996835,0.96519,0.987421,1,1,1,1,0.987342,0.987342,1,0.987342,0.990506,0.993671,1,1,0.996835,1,1,1,0.987421,1,1,1,0.990506,1,1,1,1,1,1,0.977848,1,1,0.984177,0.993711,1,1,0.987421,1,1,0.990506,0.981132,1,1,0.987342,0.987342,0.993671,1,1,0.993671,1,1,0.987342,1,1,0.949686,0.993671,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.773585,1] area_under_roc_curve 0.9909231699307377 [0.990506,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993711,0.949686,0.958861,1,0.91195,0.974684,0.949367,0.996835,1,0.990506,1,0.993671,0.86478,0.968553,0.987342,0.993711,1,0.990506,1,1,0.993711,1,1,0.996835,1,1,1,0.993671,1,1,1,1,0.993671,0.996835,0.993671,1,1,1,1,1,0.974843,0.981013,0.971519,1,0.996835,0.996835,0.987421,1,1,1,0.962025,1,0.993711,0.937107,1,0.984177,0.962025,1,1,1,1,0.993671,1,1,0.981132,1,1,0.963608,1,0.987342,1,1,1,1,0.962264,1,1,1,1,0.996835,1,0.987421,1,1,0.968553,0.939873,1] area_under_roc_curve 0.9894282153092904 [1,1,1,1,1,0.993671,1,1,0.993711,1,1,1,0.993671,0.993711,0.993711,0.981013,1,1,0.987342,0.987342,0.993671,1,1,1,1,0.962264,0.974843,1,1,1,1,1,1,1,1,1,0.984177,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835,0.713608,1,0.990506,0.985759,1,1,0.987342,1,1,0.990506,0.993711,0.996835,1,0.974843,0.949686,1,0.927215,0.990506,1,1,1,0.974843,0.996835,1,1,0.993711,1,1,0.993671,1,0.990506,1,0.977848,0.987342,1,1,0.993711,1,1,1,1,0.962264,1,1,0.971519,1,0.878165,1] area_under_roc_curve 0.9909983032799936 [0.993711,0.996835,1,1,0.996835,0.993711,0.996835,0.993711,0.981013,1,1,1,0.981013,0.996835,1,0.974843,1,1,1,1,1,1,0.937107,1,1,0.883648,0.962264,1,1,1,1,1,1,1,1,1,0.974684,0.971519,1,1,1,1,0.984177,0.996835,1,1,1,0.993671,0.930818,0.987421,1,0.990506,1,0.931962,0.974684,0.930818,1,1,1,0.981013,1,1,0.990506,1,1,1,0.990506,1,0.946203,1,1,1,0.955696,1,0.968354,1,1,1,1,1,0.996835,0.993711,1,1,0.996835,1,1,0.987342,1,0.987342,1,0.990506,1,0.987342,1,0.993671,0.981132,1,0.958861,1] area_under_roc_curve 0.9947516121327921 [0.974843,1,1,1,1,1,1,0.974843,1,1,1,1,1,0.996835,0.996835,1,1,0.981132,0.993671,0.937107,0.996835,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,0.993711,1,1,0.996835,0.971519,0.993671,1,0.987421,1,1,1,1,1,1,0.993671,1,1,0.993671,0.996835,1,0.946203,1,0.987421,1,1,0.990506,0.987342,1,0.996835,0.993671,0.993671,1,0.987421,0.981013,1,1,1,0.955975,1,0.981013,0.996835,0.993671,0.990506,1,1,1,1,1,0.974843,1,0.993711,0.993671,1,0.996835,0.990506,1,0.993671,1,0.996835,1,0.996835,1,0.993671,0.943396,0.996835,1,1] 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 0.7347124077217639 kappa 0.6906077348066298 kappa 0.7157857340228161 kappa 0.6400236826524571 kappa 0.7030719418779121 kappa 0.6842562260725421 kappa 0.7094583925469762 kappa 0.6714060031595577 kappa 0.6778141903897027 kappa 0.7220247966516623 kb_relative_information_score 107.4790369606146 kb_relative_information_score 113.71235027144725 kb_relative_information_score 115.88244381412935 kb_relative_information_score 113.75898278998383 kb_relative_information_score 115.4927925220164 kb_relative_information_score 112.9466202159691 kb_relative_information_score 115.0101437152909 kb_relative_information_score 116.83932193997288 kb_relative_information_score 112.56194909274517 kb_relative_information_score 106.50601698453328 mean_absolute_error 0.014414874142401593 mean_absolute_error 0.012859900377399675 mean_absolute_error 0.012742475441225264 mean_absolute_error 0.012982173382172404 mean_absolute_error 0.012658815386002845 mean_absolute_error 0.0133101703851698 mean_absolute_error 0.012757152534964724 mean_absolute_error 0.01262077880452854 mean_absolute_error 0.013096821789321592 mean_absolute_error 0.014833154970029582 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 0.7375 predictive_accuracy 0.69375 predictive_accuracy 0.71875 predictive_accuracy 0.64375 predictive_accuracy 0.70625 predictive_accuracy 0.6875 predictive_accuracy 0.7125 predictive_accuracy 0.675 predictive_accuracy 0.68125 predictive_accuracy 0.725 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 0.7375 [0,1,1,1,0.5,1,1,1,0,1,0.5,1,1,0.5,1,1,1,0,1,0,1,1,0,1,0,0.5,0,1,0.5,1,1,1,1,0,0,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0.5,0.5,1,0.5,0.5,1,0,0,1,1,0.5,1,1,1,1,0,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,1] recall 0.69375 [1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,1,0,0,1,1,0,1,1,1,0,0.5,1,1,1,1,1,1,0,0.5,0,0,1,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0,1,1,1,0,1,1,1,0.5,1,0,1,0.5,1,0,0,1,1,1,0.5,1,0,1,1,1,0.5,0.5,1,0,0.5,1,1,1,1,1,1,1,0,1,0,1,1,0.5,0.5,0.5,1,0,0,1] recall 0.71875 [0.5,0.5,1,1,0.5,0.5,1,1,0,1,0.5,1,1,1,0.5,0.5,1,0.5,1,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,0.5,1,0,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0,1,1,0.5,1,1,0.5,0,1,0,0.5,0.5,1,0,1,1,1,1,0,1,1,0,1,1,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0,0.5] recall 0.64375 [0.5,1,1,1,0.5,0,1,1,0.5,0.5,0,1,1,0.5,1,0.5,1,0.5,0,0,1,1,0.5,1,0,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,0.5,0,1,0,1,0,1,1,1,0.5,1,1,0.5,0,0.5,0,0,1,1,0.5,0.5,0.5,0,1,0,0.5,1,0,1,0.5,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,0,0.5,1,0,0.5,0.5,0,0] recall 0.70625 [0,1,1,1,1,0.5,1,1,0,1,1,1,1,0,1,1,1,0.5,0,0,0.5,1,1,1,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0.5,1,1,1,0,0.5,0,0,0,1,0.5,0.5,1,1,1,1,1,0.5,0,0,1,0,0.5,1,1,0,1,0.5,1,1,0,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,0,0.5] recall 0.6875 [1,1,1,1,1,0.5,1,0.5,1,0.5,0,1,0,1,1,1,1,0.5,1,0,0.5,0.5,0.5,1,1,0,0,0,1,1,1,1,0.5,0,0,0.5,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,1,1,0.5,0,0.5,0.5,1,1,0,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,0.5,1,1,0.5,0,1,1,1,0.5,1,0,1] recall 0.7125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,0,0,0.5,1,1,0,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,0,0.5,0.5,0.5,1,1,0,1,1,0,0.5,0,1,0.5,0.5,0,1,0.5,1,0,1,1,0,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,1,1,1,0,0,1,1,1,0,1,1,1,1,0,0,1] recall 0.675 [1,1,1,0.5,1,0.5,1,1,0,1,1,1,1,0,0,0.5,1,1,1,0.5,0.5,0.5,1,1,0.5,0,0,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0,1,0.5,0,1,1,0.5,1,1,0.5,1,0,1,0,0,1,0,0.5,0,1,1,0,0.5,1,1,0,1,1,0,0,0.5,1,0.5,0.5,1,0,0,1,0,1,1,0,0,1,0.5,0,0,1] recall 0.68125 [1,0.5,1,1,1,1,1,0,0.5,1,1,1,0.5,0.5,1,0,1,0,1,0,1,1,0,1,0.5,0,0,1,0.5,1,1,1,1,1,1,1,0.5,0,1,1,1,1,0,1,0,1,1,0.5,0,1,1,0.5,1,0.5,0,0,1,0,1,0,1,1,0.5,1,1,1,0.5,0,0,0,1,1,0,0.5,0.5,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0.5,1,0.5,1,0.5,1,0.5,0,0.5,0.5,1] recall 0.725 [0,0.5,1,1,1,0,1,0,1,1,1,1,0.5,0,1,1,1,0,0.5,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,0,0.5,1,1,0.5,0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,0.5,1,0,0,0,1,1,1,0.5,1,1,0.5,0.5,1,1,0,1,0.5,1,0,0,0,1,0.5,0.5,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0,1,0.5,1] relative_absolute_error 0.7280239465859378 relative_absolute_error 0.6494899180504876 relative_absolute_error 0.6435593657184466 relative_absolute_error 0.6556653223319385 relative_absolute_error 0.639334110404183 relative_absolute_error 0.6722308275338271 relative_absolute_error 0.6443006330790254 relative_absolute_error 0.6374130709357838 relative_absolute_error 0.6614556459253319 relative_absolute_error 0.7491492409105838 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 0.07805923487772606 root_mean_squared_error 0.07341600937453242 root_mean_squared_error 0.07283696562041105 root_mean_squared_error 0.0742190936521689 root_mean_squared_error 0.07233204171319466 root_mean_squared_error 0.07530360235529684 root_mean_squared_error 0.07267552419713524 root_mean_squared_error 0.07189795706625754 root_mean_squared_error 0.07450667321318293 root_mean_squared_error 0.07962969431005533 root_relative_squared_error 0.7845248288231543 root_relative_squared_error 0.7378586566682986 root_relative_squared_error 0.7320390479724821 root_relative_squared_error 0.7459299573469464 root_relative_squared_error 0.7269643717666732 root_relative_squared_error 0.7568296799231595 root_relative_squared_error 0.7304165006190676 root_relative_squared_error 0.7226016569147206 root_relative_squared_error 0.7488202406840938 root_relative_squared_error 0.8003085399913686 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 47414.83813599916 usercpu_time_millis 47506.53817700004 usercpu_time_millis 48299.85212900101 usercpu_time_millis 47776.04468999925 usercpu_time_millis 47602.817289000086 usercpu_time_millis 47101.339050001116 usercpu_time_millis 47470.40525599914 usercpu_time_millis 47760.939852001684 usercpu_time_millis 47074.12381500217 usercpu_time_millis 47255.020711001634 usercpu_time_millis_testing 4.212124000332551 usercpu_time_millis_testing 4.081281998878694 usercpu_time_millis_testing 4.046100000778097 usercpu_time_millis_testing 4.083960999196279 usercpu_time_millis_testing 4.527652999968268 usercpu_time_millis_testing 4.151000001002103 usercpu_time_millis_testing 4.046474999995553 usercpu_time_millis_testing 4.1407260014239 usercpu_time_millis_testing 4.134251001232769 usercpu_time_millis_testing 4.058858001371846 usercpu_time_millis_training 47410.62601199883 usercpu_time_millis_training 47502.45689500116 usercpu_time_millis_training 48295.80602900023 usercpu_time_millis_training 47771.96072900006 usercpu_time_millis_training 47598.28963600012 usercpu_time_millis_training 47097.18805000011 usercpu_time_millis_training 47466.358780999144 usercpu_time_millis_training 47756.79912600026 usercpu_time_millis_training 47069.989564000934 usercpu_time_millis_training 47250.96185300026