8698181 1 Jan van Rijn 9954 Supervised Classification 7707 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1) 6675600 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "mean" 7660 strategy_nominal "most_frequent" 7660 verbose 0 7660 categorical_features [] 7661 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7661 handle_unknown "ignore" 7661 n_values "auto" 7661 sparse true 7661 threshold 0.0 7662 copy true 7663 with_mean false 7663 with_std true 7663 C 12281.596146982398 7708 cache_size 200 7708 class_weight null 7708 coef0 0.5772885804771086 7708 decision_function_shape null 7708 degree 5 7708 gamma 0.16032044625359804 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 5946 7708 shrinking false 7708 tol 0.0007995704775210946 7708 verbose false 7708 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18313299 description https://api.openml.org/data/download/18313299/description.xml -1 18313300 predictions https://api.openml.org/data/download/18313300/predictions.arff area_under_roc_curve 0.9954288983585857 [0.984257,0.99925,0.999961,0.999408,0.998382,0.992069,0.999684,0.997593,0.995857,0.999842,0.998343,0.999645,0.998816,0.944721,0.998343,0.99858,1,0.992385,0.982915,0.972735,0.997001,0.999211,0.993529,0.998974,0.998501,0.978812,0.988991,0.99858,0.997988,0.999961,0.999132,0.999921,1,0.990017,0.99637,0.998303,0.99345,0.984691,0.998935,0.999408,0.99858,0.996607,1,0.99854,0.99562,1,0.998816,0.99779,0.997199,0.996765,0.99491,0.998461,0.997869,0.993726,0.995107,0.9927,1,0.999014,0.992385,0.995975,0.996883,0.999487,0.996922,0.998895,0.99854,0.992464,0.985638,0.999684,0.99712,0.993056,0.996922,0.99854,0.994081,0.997396,0.98907,0.999211,0.999211,0.990609,0.99929,0.999921,0.996212,0.996133,0.997356,0.994752,0.999053,0.999803,0.999803,0.998816,0.998974,0.999487,0.999605,0.999605,0.997356,0.995344,0.993253,0.995818,0.996094,0.998422,0.965002,0.994437] average_cost 0 f_measure 0.7896507611516705 [0.648649,0.866667,0.967742,0.814815,0.857143,0.774194,0.9375,0.736842,0.774194,0.857143,0.848485,0.896552,0.857143,0.625,0.857143,0.823529,0.933333,0.606061,0.4,0.242424,0.774194,0.9375,0.6875,0.857143,0.857143,0.444444,0.545455,0.848485,0.903226,1,0.903226,0.967742,0.9375,0.5,0.83871,0.848485,0.516129,0.482759,0.875,0.896552,0.727273,0.875,0.933333,0.823529,0.857143,0.888889,0.857143,0.75,0.705882,0.75,0.848485,0.8,0.787879,0.6875,0.774194,0.580645,0.896552,0.9375,0.65,0.666667,0.785714,0.857143,0.689655,0.903226,0.909091,0.702703,0.387097,0.903226,0.8,0.6875,0.787879,0.785714,0.6875,0.896552,0.682927,0.9375,0.896552,0.648649,0.967742,1,0.848485,0.8125,0.83871,0.5625,0.909091,0.866667,0.967742,0.8,0.875,0.903226,0.8125,0.785714,0.866667,0.774194,0.882353,0.8,0.777778,0.8,0.555556,0.866667] kappa 0.7840909090909091 kb_relative_information_score 951.9155383487158 mean_absolute_error 0.016023197358708795 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 0.8029811953440436 [0.571429,0.928571,1,1,1,0.8,0.9375,0.636364,0.8,1,0.823529,1,0.789474,0.625,1,0.777778,1,0.588235,0.368421,0.235294,0.8,0.9375,0.6875,0.789474,0.789474,0.4,0.529412,0.823529,0.933333,1,0.933333,1,0.9375,0.5,0.866667,0.823529,0.533333,0.538462,0.875,1,0.705882,0.875,1,0.777778,1,0.8,1,0.75,0.666667,0.75,0.823529,0.857143,0.764706,0.6875,0.8,0.6,1,0.9375,0.541667,0.6,0.916667,0.789474,0.769231,0.933333,0.882353,0.619048,0.4,0.933333,0.857143,0.6875,0.764706,0.916667,0.6875,1,0.56,0.9375,1,0.571429,1,1,0.823529,0.8125,0.866667,0.5625,0.882353,0.928571,1,0.857143,0.875,0.933333,0.8125,0.916667,0.928571,0.8,0.833333,0.736842,0.7,0.857143,0.5,0.928571] predictive_accuracy 0.78625 prior_entropy 6.6438561897747395 recall 0.78625 [0.75,0.8125,0.9375,0.6875,0.75,0.75,0.9375,0.875,0.75,0.75,0.875,0.8125,0.9375,0.625,0.75,0.875,0.875,0.625,0.4375,0.25,0.75,0.9375,0.6875,0.9375,0.9375,0.5,0.5625,0.875,0.875,1,0.875,0.9375,0.9375,0.5,0.8125,0.875,0.5,0.4375,0.875,0.8125,0.75,0.875,0.875,0.875,0.75,1,0.75,0.75,0.75,0.75,0.875,0.75,0.8125,0.6875,0.75,0.5625,0.8125,0.9375,0.8125,0.75,0.6875,0.9375,0.625,0.875,0.9375,0.8125,0.375,0.875,0.75,0.6875,0.8125,0.6875,0.6875,0.8125,0.875,0.9375,0.8125,0.75,0.9375,1,0.875,0.8125,0.8125,0.5625,0.9375,0.8125,0.9375,0.75,0.875,0.875,0.8125,0.6875,0.8125,0.75,0.9375,0.875,0.875,0.75,0.625,0.8125] relative_absolute_error 0.809252391853978 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08301148040653694 root_relative_squared_error 0.834296769092187 total_cost 0 area_under_roc_curve 0.9962473131120134 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.977848,1,0.981132,1,1,0.981013,0.993711,0.981132,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.968354,1,1,0.949686,0.996835,1,1,1,1,1,1,0.949367,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,1,0.993671,0.949367,1] area_under_roc_curve 0.9949409382214791 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.867089,1,1,1,0.977848,0.993671,0.981132,1,0.993711,0.981132,1,1,0.987342,0.968354,1,1,1,1,1,1,0.996835,0.981013,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,0.993671,1,1,0.996835,1,1,1,0.993711,1,1,1,0.990506,1,1,1,1,1,0.996835,1,1,0.987421,1,1,1,1,1,1,1,0.996835,0.993671,1,1,0.971519,0.990506,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.958861,1,1,1,0.996835,1,0.981132] area_under_roc_curve 0.9962470643260886 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.993671,1,1,0.993671,1,0.939873,1,1,0.990506,1,1,0.949367,0.990506,0.993711,1,1,1,1,1,0.981013,1,1,0.987421,1,0.993711,0.996835,0.996835,0.968553,1,1,1,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.984177,1,1,1,0.987421,0.996835,0.990506,1,1,0.993711,0.990506,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,0.996835,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1] area_under_roc_curve 0.996682937266141 [1,1,1,0.993711,1,0.987342,1,1,0.996835,1,1,1,1,1,0.996835,1,1,0.987342,0.943396,0.981013,1,1,0.993671,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.974684,0.987342,0.996835,0.987421,1,0.993711,1,1,0.993671,1,1,1,1,0.987342,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.984177,1,1,1] area_under_roc_curve 0.9951461866093464 [1,1,1,1,1,1,1,0.996835,0.993711,1,0.987342,1,1,0.880503,1,0.996835,1,0.993671,0.968553,0.984177,1,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.984177,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.996835,0.987342,1,0.990506,0.996835,1,0.993671,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.943038,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.962025,0.993711,1,1,0.899371,1] area_under_roc_curve 0.9956895350688638 [0.901899,1,1,1,1,0.981013,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,1,0.968354,1,1,1,1,0.993671,0.981013,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.987342,0.996835,1,0.990506,1,1,0.993711,1,1,1,1,0.993671,0.993671,1,1,0.993671,1,0.996835,0.987342,1,1,0.996835,0.993711,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671] area_under_roc_curve 0.9949048642623992 [0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,0.993671,1,1,1,1,1,1,0.974684,0.993671,1,0.990506,1,1,0.90566,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.968553,1,0.996835,0.996835,1,1,1,1,1,1,0.996835,1,1,0.996835,0.990506,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,0.993711,1,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.835443,1] area_under_roc_curve 0.9970426817132394 [1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,0.993711,1,1,0.993711,0.993671,0.984177,0.996835,1,0.996835,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.981132,0.962264,1,1,1,1,0.990506,1,1,0.996835,1,1,1,1,1,1,0.981132,0.918239,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.996835,1,0.993671,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.990506] area_under_roc_curve 0.9966438778759651 [1,0.996835,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,0.993711,0.996835,0.968553,0.996835,1,0.955975,0.990506,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.987342,0.971519,0.993711,1,1,1,1,0.990506,1,1,1,1,1,0.990506,1,1,1,1,0.993711,0.974684,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,0.987421,0.987342,1,1,1,1] area_under_roc_curve 0.9971553817371233 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,0.962025,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.987342,0.996835,0.993711,0.987421,0.981013,1,1,1,1,1,0.987342,0.990506,1,0.987342,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.993671,1,1,0.996835,1,1,1,1,0.996835,0.993711,1,1,1] 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 0.7599873677561977 kappa 0.759939984996249 kappa 0.7978681405448086 kappa 0.7725747226280255 kappa 0.8041228970855383 kappa 0.7915432902996564 kappa 0.7978202495656295 kappa 0.7914856646394439 kappa 0.8041615667074664 kappa 0.7600442023837714 kb_relative_information_score 96.93476728737855 kb_relative_information_score 93.74969022309492 kb_relative_information_score 94.0208697310089 kb_relative_information_score 92.95591018708241 kb_relative_information_score 94.67850892356806 kb_relative_information_score 94.76248426854232 kb_relative_information_score 99.35472723419102 kb_relative_information_score 95.4762630743754 kb_relative_information_score 94.92405969689175 kb_relative_information_score 95.05825772258355 mean_absolute_error 0.01576337979758927 mean_absolute_error 0.016262245891023923 mean_absolute_error 0.016223016754358114 mean_absolute_error 0.016364603789236064 mean_absolute_error 0.016203778114534335 mean_absolute_error 0.01602415887694483 mean_absolute_error 0.015432387522349971 mean_absolute_error 0.016057485411584294 mean_absolute_error 0.015994362313346156 mean_absolute_error 0.015906555116120755 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 0.7625 predictive_accuracy 0.7625 predictive_accuracy 0.8 predictive_accuracy 0.775 predictive_accuracy 0.80625 predictive_accuracy 0.79375 predictive_accuracy 0.8 predictive_accuracy 0.79375 predictive_accuracy 0.80625 predictive_accuracy 0.7625 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 0.7625 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,1,0,1,0,0.5,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.7625 [1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,0.5,1,0,1,1,1,0,0,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,0.5,0] recall 0.8 [1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,0,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1] recall 0.775 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,0.5,1,0.5,0,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1] recall 0.80625 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.79375 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5] recall 0.8 [1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,0,0.5,0.5,0.5,0.5,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.79375 [0.5,0,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5] recall 0.80625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,0.5,1] recall 0.7625 [1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0.5,1,0,1,1,1,0,1,0,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.7961302928075376 relative_absolute_error 0.821325550051712 relative_absolute_error 0.8193442805231358 relative_absolute_error 0.8264951408705069 relative_absolute_error 0.8183726320471872 relative_absolute_error 0.8093009533810506 relative_absolute_error 0.7794135112297953 relative_absolute_error 0.810984111696175 relative_absolute_error 0.8077960764316227 relative_absolute_error 0.8033613695010469 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 0.08199088444593543 root_mean_squared_error 0.08378248196993877 root_mean_squared_error 0.08371056254340895 root_mean_squared_error 0.08442390278406772 root_mean_squared_error 0.08352681264815848 root_mean_squared_error 0.08310760139243158 root_mean_squared_error 0.08060930319458637 root_mean_squared_error 0.0828449454656835 root_mean_squared_error 0.0830767572823802 root_mean_squared_error 0.08298012738784424 root_relative_squared_error 0.824039393747134 root_relative_squared_error 0.8420456263606155 root_relative_squared_error 0.841322808927473 root_relative_squared_error 0.8484921480975549 root_relative_squared_error 0.8394760529947066 root_relative_squared_error 0.8352628213488283 root_relative_squared_error 0.8101539797225444 root_relative_squared_error 0.8326230299609907 root_relative_squared_error 0.8349528263790319 root_relative_squared_error 0.8339816593981003 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 1669.3313829998715 usercpu_time_millis 1531.533174999936 usercpu_time_millis 1485.0214419999475 usercpu_time_millis 1486.5533860000824 usercpu_time_millis 1533.0970140000773 usercpu_time_millis 1480.81256699993 usercpu_time_millis 1544.8098659999232 usercpu_time_millis 1514.8433549999254 usercpu_time_millis 1478.1672369999797 usercpu_time_millis 1513.2924299999786 usercpu_time_millis_testing 141.59021899990876 usercpu_time_millis_testing 152.3081599999614 usercpu_time_millis_testing 138.6547019999398 usercpu_time_millis_testing 145.8457130000852 usercpu_time_millis_testing 139.2929419999973 usercpu_time_millis_testing 146.31318700003249 usercpu_time_millis_testing 179.06734999996843 usercpu_time_millis_testing 139.8970539999027 usercpu_time_millis_testing 139.2351150000195 usercpu_time_millis_testing 152.7864969999655 usercpu_time_millis_training 1527.7411639999627 usercpu_time_millis_training 1379.2250149999745 usercpu_time_millis_training 1346.3667400000077 usercpu_time_millis_training 1340.7076729999972 usercpu_time_millis_training 1393.80407200008 usercpu_time_millis_training 1334.4993799998974 usercpu_time_millis_training 1365.7425159999548 usercpu_time_millis_training 1374.9463010000227 usercpu_time_millis_training 1338.9321219999601 usercpu_time_millis_training 1360.505933000013