6020361 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) 4055046 bootstrap true 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features 0.7331455258377714 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 6 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 4164 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 12963845 description https://api.openml.org/data/download/12963845/description.xml -1 12963846 predictions https://api.openml.org/data/download/12963846/predictions.arff area_under_roc_curve 0.9947687815656565 [0.985361,0.999921,1,1,0.998935,0.994752,0.999684,0.998816,0.997554,0.999921,0.999014,0.999803,0.999487,0.986545,0.999684,0.998658,1,0.983033,0.982521,0.97017,0.999211,0.999605,0.993647,1,0.99854,0.967803,0.988518,0.993253,0.999132,0.999724,0.999961,0.999921,0.998698,0.990057,0.998185,0.999053,0.991872,0.993292,0.998895,0.999842,0.996725,0.999132,1,0.998658,0.99858,0.99929,0.999053,0.996015,0.992306,0.994437,0.996409,0.992503,0.998106,0.985953,0.993056,0.982915,1,0.999645,0.994989,0.980311,1,0.998777,0.996015,0.999132,0.998264,0.995265,0.978338,0.999961,0.989426,0.981731,0.995502,0.998382,0.994358,0.998146,0.963818,0.997869,0.999724,0.985085,0.999842,1,0.992148,0.996567,0.995581,0.995107,0.994318,0.996252,0.999921,0.994634,0.997554,1,0.999842,0.999527,0.997869,0.991359,0.999329,0.999527,0.996567,0.995541,0.965159,0.99929] average_cost 0 f_measure 0.7749112991389967 [0.642857,0.941176,1,0.969697,0.933333,0.625,0.882353,0.857143,0.714286,0.967742,0.689655,0.903226,0.842105,0.666667,0.941176,0.896552,0.888889,0.6,0.625,0.173913,0.857143,0.933333,0.709677,1,0.857143,0.32,0.352941,0.742857,0.875,0.969697,0.967742,0.941176,0.848485,0.709677,0.8,0.857143,0.545455,0.5,0.909091,0.969697,0.758621,0.8,1,0.888889,0.848485,0.866667,0.866667,0.774194,0.774194,0.875,0.774194,0.758621,0.789474,0.615385,0.6875,0.285714,1,0.969697,0.571429,0.689655,0.914286,0.717949,0.666667,0.810811,0.833333,0.777778,0.368421,0.914286,0.5,0.545455,0.642857,0.8,0.594595,0.8125,0.785714,0.896552,0.941176,0.454545,0.941176,0.967742,0.714286,0.666667,0.685714,0.666667,0.8,0.875,0.969697,0.777778,0.764706,1,0.864865,0.833333,0.75,0.692308,0.857143,0.857143,0.8,0.742857,0.48,0.785714] kappa 0.7803030303030303 kb_relative_information_score 1141.3261324942798 mean_absolute_error 0.013529750913027407 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 0.7876618754968131 [0.75,0.888889,1,0.941176,1,0.625,0.833333,0.789474,0.833333,1,0.769231,0.933333,0.727273,0.818182,0.888889,1,0.8,0.642857,0.625,0.285714,0.789474,1,0.733333,1,0.789474,0.444444,0.333333,0.684211,0.875,0.941176,1,0.888889,0.823529,0.733333,0.736842,0.789474,0.529412,0.45,0.882353,0.941176,0.846154,0.736842,1,0.8,0.823529,0.928571,0.928571,0.8,0.8,0.875,0.8,0.846154,0.681818,0.8,0.6875,0.333333,1,0.941176,0.526316,0.769231,0.842105,0.608696,0.714286,0.714286,0.75,0.7,0.318182,0.842105,0.75,1,0.75,0.736842,0.52381,0.8125,0.916667,1,0.888889,0.833333,0.888889,1,0.833333,0.714286,0.631579,0.714286,0.736842,0.875,0.941176,0.7,0.722222,1,0.761905,0.75,0.75,0.9,0.789474,0.789474,0.857143,0.684211,0.666667,0.916667] predictive_accuracy 0.7825 prior_entropy 6.6438561897747395 recall 0.7825 [0.5625,1,1,1,0.875,0.625,0.9375,0.9375,0.625,0.9375,0.625,0.875,1,0.5625,1,0.8125,1,0.5625,0.625,0.125,0.9375,0.875,0.6875,1,0.9375,0.25,0.375,0.8125,0.875,1,0.9375,1,0.875,0.6875,0.875,0.9375,0.5625,0.5625,0.9375,1,0.6875,0.875,1,1,0.875,0.8125,0.8125,0.75,0.75,0.875,0.75,0.6875,0.9375,0.5,0.6875,0.25,1,1,0.625,0.625,1,0.875,0.625,0.9375,0.9375,0.875,0.4375,1,0.375,0.375,0.5625,0.875,0.6875,0.8125,0.6875,0.8125,1,0.3125,1,0.9375,0.625,0.625,0.75,0.625,0.875,0.875,1,0.875,0.8125,1,1,0.9375,0.75,0.5625,0.9375,0.9375,0.75,0.8125,0.375,0.6875] relative_absolute_error 0.6833207531832011 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07352181036935428 root_relative_squared_error 0.7389219966751784 total_cost 0 area_under_roc_curve 0.9942291616909482 [1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,1,1,0.968354,0.974684,0.981132,1,1,0.993711,1,1,0.987342,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.968354,1,1,0.993671,1,1,1,1,0.987421,0.981013,1,1,1,1,1,1,0.993671,1,1,0.937107,0.993671,1,0.993671,1,1,1,1,0.977848,1,1,1,1,1,1,0.996835,0.974684,1,0.996835,0.990506,1,1,1,1,0.977848,1,1,1,1,0.848101,1] area_under_roc_curve 0.9941910974444712 [1,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.955696,1,1,1,0.968354,0.996835,0.987421,1,0.993711,1,1,1,0.955696,0.987342,0.962025,1,1,1,1,1,1,0.996835,1,0.981132,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.952532,1,1,1,0.924528,1,1,1,0.89557,1,1,1,0.996835,1,0.993671,1,1,0.987421,1,1,1,1,1,1,1,1,0.987342,1,1,0.996835,0.993671,0.981132,0.993711,1,1,1,1,0.993671,1,1,1,1,0.990506,1,1,1,0.996835,0.996835,1] area_under_roc_curve 0.9945075531406736 [1,1,1,1,0.996835,0.987342,1,1,0.993671,1,1,1,1,1,1,1,1,0.993671,0.981132,0.927215,0.993711,1,0.949367,1,0.993711,0.984177,1,1,1,1,1,1,1,0.968354,0.993711,1,0.993711,0.974843,1,1,1,1,1,1,0.996835,1,1,1,0.971519,1,0.996835,1,1,1,1,1,1,1,0.993711,0.981013,1,0.993711,1,1,1,0.990506,0.968354,1,0.974843,0.974684,1,1,0.993711,0.993671,1,1,1,0.981013,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,0.996835,0.996835,0.962264,1] area_under_roc_curve 0.9945120312873181 [0.993671,0.996835,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.993671,0.849057,0.990506,1,1,0.996835,1,0.987421,0.968354,0.974684,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,0.955696,1,1,0.958861,0.993671,0.993671,0.981132,1,1,0.990506,1,0.96519,1,1,0.955975,0.996835,1,0.990506,1,1,1,1,1,1,1,1,0.974843,1,1,1,0.993671,0.996835,0.993711,0.996835] area_under_roc_curve 0.9928904446302045 [0.996835,1,1,1,1,0.993671,1,1,1,1,1,1,1,0.886792,1,1,1,1,1,0.990506,0.993671,1,1,1,1,0.974684,0.984177,1,1,1,1,1,1,0.974684,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.974843,1,0.993711,0.958861,0.96519,1,0.993671,0.996835,1,1,1,1,1,0.996835,1,0.930818,1,0.971519,0.987342,1,1,1,1,0.81962,1,1,0.996835,1,1,1,1,1,0.996835,0.974843,1,1,1,1,1,1,0.996835,1,0.987421,0.990506,1,0.996835,1,0.987421,0.996835] area_under_roc_curve 0.9947822128015285 [0.933544,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.920886,1,0.996835,0.996835,1,1,0.993671,0.984177,0.993711,1,1,1,1,0.987342,1,0.987421,1,0.987342,0.993671,1,1,1,0.993671,1,1,1,1,1,1,0.996835,0.971519,1,1,1,1,0.990506,0.981013,1,1,0.993671,1,1,1,0.987421,1,1,0.996835,0.981132,1,0.996835,1,0.993671,0.990506,1,1,0.962025,1,1,1,1,1,1,0.996835,1,0.990506,0.993711,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.974843,1,0.993671] area_under_roc_curve 0.9960554991640789 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.962025,1,1,1,1,0.993671,0.830189,0.987421,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,0.981013,1,1,1,1,1,1,0.996835,0.993671,1,1,0.993671,0.993711,1,1,1,0.996835,1,0.993711,0.968553,1,0.993671,0.990506,0.993711,1,1,1,1,1,1,0.981132,1,1,0.987342,1,0.993671,0.993671,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,0.971519,1] area_under_roc_curve 0.9962891091473607 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.974843,0.981013,0.993671,0.996835,1,1,1,1,0.981132,0.987421,0.993671,1,0.996835,1,1,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,1,0.981013,1,1,1,1,1,0.996835,0.993711,0.996835,1,0.968553,0.974843,1,0.981013,0.993671,1,1,1,1,0.993671,1,1,0.962264,1,1,0.990506,0.993711,0.977848,1,0.987342,0.993671,1,1,1,1,1,1,1,0.981132,1,1,0.981013,1,0.990506,1] area_under_roc_curve 0.9973153510866968 [0.981132,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993711,1,0.996835,1,1,1,1,1,1,1,1,0.993671,1,1,0.974843,1,1,0.990506,0.996835,0.996835,1,1,0.987421,1,1,0.996835,1,0.974684,0.993671,0.974843,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.984177,0.993711,1,0.993711,0.984177,1,1,0.987342,1,1,1,1,1,1,1,0.987421,0.996835,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.993711,1,0.971519,1] area_under_roc_curve 0.9972800234853911 [0.918239,1,1,1,1,1,1,0.987421,0.996835,1,1,1,1,0.990506,1,1,1,0.993711,0.996835,1,1,1,1,1,1,0.937107,1,1,0.996835,1,1,1,1,0.993711,1,1,0.990506,1,0.996835,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,1,0.981013,1,1,1,1,0.993671,1,1,1,0.984177,1,0.987421,1,0.996835,1,1,0.993711,0.981132,1,0.987342,1,0.996835,1,1,1,0.993711,1,1,0.981132,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.8041692987997473 kappa 0.8041770302814956 kappa 0.7410185550730359 kappa 0.7346914603813811 kappa 0.7852094602598018 kappa 0.7346705097326964 kappa 0.7851924975320829 kappa 0.7725387987205308 kappa 0.8041306322315681 kappa 0.8357289527720739 kb_relative_information_score 113.73733900554225 kb_relative_information_score 113.70219738907709 kb_relative_information_score 112.98960522988008 kb_relative_information_score 113.2131614010187 kb_relative_information_score 113.08798092873927 kb_relative_information_score 112.91568048933946 kb_relative_information_score 116.12307085719073 kb_relative_information_score 114.58984516023118 kb_relative_information_score 115.19741410692504 kb_relative_information_score 115.7698379263363 mean_absolute_error 0.013532395173061203 mean_absolute_error 0.013555581374026482 mean_absolute_error 0.013584336811298513 mean_absolute_error 0.013648764860361286 mean_absolute_error 0.013602013969135176 mean_absolute_error 0.013685334161724887 mean_absolute_error 0.01320355127481083 mean_absolute_error 0.013511769489634295 mean_absolute_error 0.013535052602542497 mean_absolute_error 0.013438709413678761 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 0.80625 predictive_accuracy 0.80625 predictive_accuracy 0.74375 predictive_accuracy 0.7375 predictive_accuracy 0.7875 predictive_accuracy 0.7375 predictive_accuracy 0.7875 predictive_accuracy 0.775 predictive_accuracy 0.80625 predictive_accuracy 0.8375 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 0.80625 [1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0,0.5,0,1,1,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0,1] recall 0.80625 [1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,0,0.5,1,1,0,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0,1,0,0,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1] recall 0.74375 [0.5,1,1,1,0.5,0.5,1,1,0,1,0.5,1,1,1,1,0.5,1,1,0,0,1,0,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,0.5,1,0.5,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0,0,0.5,1,0,0.5,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,0.5] recall 0.7375 [0.5,1,1,1,0.5,0,1,1,0.5,1,0,1,1,0.5,1,1,1,0.5,0,0,1,1,0.5,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,0.5,0,0.5,0,1,0,0.5,1,0,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0.5,0.5,1,0.5] recall 0.7875 [0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0,0,0.5,0,0,0,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0.5,1,1,1,0,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,0.5,1,0,0.5] recall 0.7375 [0.5,1,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,0,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,0,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,0,1,1,1,1,0,1,1,1,1,0,0,0.5] recall 0.7875 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,0.5,1,1,0,0,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0.5,0.5,1,1,0,1,1,1,0.5,0,1,1,0,0,1,0.5,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0.5,1] recall 0.775 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,0,0.5,0,1,1,1,1,1,0,0,0.5,0,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,1,1,0,1,1,1,0,1,1,0,0,0,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5] recall 0.80625 [0,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,1,0,1,1,0.5,0.5,0.5,1,1,1,1,0.5,1] recall 0.8375 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1,1,1,0.5,1,0,0,0,1,0.5,1,0,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1] relative_absolute_error 0.6834543016697566 relative_absolute_error 0.6846253219205282 relative_absolute_error 0.686077616732247 relative_absolute_error 0.6893315586041042 relative_absolute_error 0.6869704024815734 relative_absolute_error 0.6911784930164073 relative_absolute_error 0.6668460239803439 relative_absolute_error 0.6824126004865795 relative_absolute_error 0.683588515279923 relative_absolute_error 0.6787226976605425 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 0.07325430887505628 root_mean_squared_error 0.07343193077979485 root_mean_squared_error 0.07416373114665756 root_mean_squared_error 0.07405219005143844 root_mean_squared_error 0.07388901792677054 root_mean_squared_error 0.07445739245128456 root_mean_squared_error 0.07212830516261513 root_mean_squared_error 0.07356139691850594 root_mean_squared_error 0.07319556398824811 root_mean_squared_error 0.07305672818731972 root_relative_squared_error 0.7362335055011003 root_relative_squared_error 0.7380186728119065 root_relative_squared_error 0.7453735432310825 root_relative_squared_error 0.7442525130445761 root_relative_squared_error 0.7426125714877017 root_relative_squared_error 0.7483249503913673 root_relative_squared_error 0.7249167423898442 root_relative_squared_error 0.739319856463909 root_relative_squared_error 0.735643097174147 root_relative_squared_error 0.7342477448736986 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 7332.708146 usercpu_time_millis 7210.108218999999 usercpu_time_millis 7159.528004999998 usercpu_time_millis 7188.993224000001 usercpu_time_millis 7170.2356969999955 usercpu_time_millis 7171.353439999997 usercpu_time_millis 7178.759101000011 usercpu_time_millis 7190.040081999996 usercpu_time_millis 7193.316967000002 usercpu_time_millis 7172.994336999992 usercpu_time_millis_testing 44.63400499999892 usercpu_time_millis_testing 36.62378499999974 usercpu_time_millis_testing 33.89157400000187 usercpu_time_millis_testing 37.97637899999984 usercpu_time_millis_testing 34.377240999994285 usercpu_time_millis_testing 33.88968199999454 usercpu_time_millis_testing 34.78541200000507 usercpu_time_millis_testing 37.128812999995375 usercpu_time_millis_testing 34.25144899999566 usercpu_time_millis_testing 34.987044999994055 usercpu_time_millis_training 7288.074141000001 usercpu_time_millis_training 7173.484433999999 usercpu_time_millis_training 7125.636430999996 usercpu_time_millis_training 7151.016845000001 usercpu_time_millis_training 7135.858456000001 usercpu_time_millis_training 7137.463758000003 usercpu_time_millis_training 7143.973689000006 usercpu_time_millis_training 7152.911269 usercpu_time_millis_training 7159.065518000006 usercpu_time_millis_training 7138.0072919999975