8706940 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) 6684359 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "most_frequent" 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 20388.3286904169 7708 cache_size 200 7708 class_weight null 7708 coef0 0.6836226731804709 7708 decision_function_shape null 7708 degree 2 7708 gamma 3.391119240602175 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 53781 7708 shrinking false 7708 tol 2.7829431521059083e-05 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 18330748 description https://api.openml.org/data/download/18330748/description.xml -1 18330749 predictions https://api.openml.org/data/download/18330749/predictions.arff area_under_roc_curve 0.9964745107323231 [0.98836,1,1,0.999842,0.99858,0.994594,0.999803,0.999566,0.997593,0.999961,0.998974,0.999566,0.999684,0.973524,0.999329,0.999566,1,0.991872,0.984178,0.974826,0.998224,0.999566,0.996607,0.999329,0.998698,0.976918,0.992188,0.99929,0.998737,1,0.999724,1,1,0.99053,0.999369,0.998895,0.99345,0.983586,0.999645,0.999842,0.999132,0.998777,1,0.998895,0.996962,0.999921,0.999408,0.99925,0.997514,0.996804,0.994515,0.999053,0.998185,0.992385,0.996962,0.993529,1,0.999408,0.992898,0.996725,0.998698,0.999448,0.997751,0.99929,0.999369,0.993924,0.986742,0.999921,0.997988,0.993766,0.99637,0.999329,0.99637,0.998303,0.989031,0.999448,0.999448,0.991517,0.999961,1,0.995778,0.997475,0.99779,0.995896,0.999211,0.999921,0.999961,0.999092,0.99929,0.999961,0.999487,0.999605,0.998777,0.997869,0.996804,0.997751,0.997869,0.998422,0.967251,0.99783] average_cost 0 f_measure 0.8248194148090846 [0.648649,0.967742,1,0.933333,0.933333,0.8,1,0.848485,0.83871,0.933333,0.882353,0.875,0.875,0.714286,0.933333,0.933333,1,0.533333,0.411765,0.258065,0.8,0.967742,0.75,1,0.875,0.424242,0.5625,0.848485,0.933333,0.933333,0.9375,0.967742,0.967742,0.580645,0.875,0.875,0.5625,0.545455,0.909091,0.933333,0.742857,0.909091,1,0.848485,0.9375,0.941176,0.827586,0.903226,0.764706,0.83871,0.787879,0.827586,0.866667,0.705882,0.774194,0.529412,1,0.9375,0.702703,0.742857,0.857143,0.848485,0.733333,0.9375,0.909091,0.7,0.5,0.969697,0.83871,0.727273,0.823529,0.933333,0.6875,0.967742,0.756757,0.909091,0.903226,0.685714,1,1,0.75,0.848485,0.83871,0.533333,0.848485,0.967742,0.933333,0.866667,0.933333,0.967742,0.774194,0.903226,0.903226,0.875,0.9375,0.777778,0.777778,0.8125,0.484848,0.903226] kappa 0.8207070707070707 kb_relative_information_score 986.6037371039492 mean_absolute_error 0.01599826905570106 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 0.8325435879479995 [0.571429,1,1,1,1,0.857143,1,0.823529,0.866667,1,0.833333,0.875,0.875,0.833333,1,1,1,0.571429,0.388889,0.266667,0.857143,1,0.75,1,0.875,0.411765,0.5625,0.823529,1,1,0.9375,1,1,0.6,0.875,0.875,0.5625,0.529412,0.882353,1,0.684211,0.882353,1,0.823529,0.9375,0.888889,0.923077,0.933333,0.722222,0.866667,0.764706,0.923077,0.928571,0.666667,0.8,0.5,1,0.9375,0.619048,0.684211,1,0.823529,0.785714,0.9375,0.882353,0.583333,0.5,0.941176,0.866667,0.705882,0.777778,1,0.6875,1,0.666667,0.882353,0.933333,0.631579,1,1,0.75,0.823529,0.866667,0.571429,0.823529,1,1,0.928571,1,1,0.8,0.933333,0.933333,0.875,0.9375,0.7,0.7,0.8125,0.470588,0.933333] predictive_accuracy 0.8225 prior_entropy 6.6438561897747395 recall 0.8225 [0.75,0.9375,1,0.875,0.875,0.75,1,0.875,0.8125,0.875,0.9375,0.875,0.875,0.625,0.875,0.875,1,0.5,0.4375,0.25,0.75,0.9375,0.75,1,0.875,0.4375,0.5625,0.875,0.875,0.875,0.9375,0.9375,0.9375,0.5625,0.875,0.875,0.5625,0.5625,0.9375,0.875,0.8125,0.9375,1,0.875,0.9375,1,0.75,0.875,0.8125,0.8125,0.8125,0.75,0.8125,0.75,0.75,0.5625,1,0.9375,0.8125,0.8125,0.75,0.875,0.6875,0.9375,0.9375,0.875,0.5,1,0.8125,0.75,0.875,0.875,0.6875,0.9375,0.875,0.9375,0.875,0.75,1,1,0.75,0.875,0.8125,0.5,0.875,0.9375,0.875,0.8125,0.875,0.9375,0.75,0.875,0.875,0.875,0.9375,0.875,0.875,0.8125,0.5,0.875] relative_absolute_error 0.8079933866515673 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08228870515552103 root_relative_squared_error 0.8270326045001424 total_cost 0 area_under_roc_curve 0.996522967916567 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.990506,1,0.981132,1,0.993671,0.981013,0.987421,1,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.962264,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.993711,0.993671,0.933544,1] area_under_roc_curve 0.9963249343205159 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.892405,1,1,1,0.984177,0.993671,0.987421,1,0.993711,1,1,1,0.977848,0.971519,1,1,1,1,1,1,0.996835,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.990506,1,1,0.984177,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.974843] area_under_roc_curve 0.996956104211448 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.936709,1,1,0.993671,1,1,0.939873,0.993671,1,1,1,1,1,1,0.984177,1,1,0.993711,1,1,1,0.996835,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,1,0.993711,1,0.993671,1,1,1,0.984177,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1] area_under_roc_curve 0.997276042910596 [1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.990506,0.943396,0.974684,1,1,0.996835,1,0.993711,0.990506,1,1,1,1,1,1,1,0.990506,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,0.984177,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,0.977848,0.981013,1,0.981132,1,0.993711,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1] area_under_roc_curve 0.9961308812992599 [1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,0.918239,1,1,1,0.993671,0.987421,0.990506,1,1,1,1,1,0.993671,0.993671,1,1,1,1,1,1,0.977848,1,1,0.993671,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.990506,0.990506,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.939873,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.968354,0.993711,1,1,0.937107,1] area_under_roc_curve 0.9962430837512937 [0.920886,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.955696,1,1,1,1,0.993671,0.990506,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.981013,0.996835,1,0.996835,1,1,0.993711,1,1,1,1,0.990506,0.996835,1,1,1,1,0.996835,0.987342,1,1,0.993671,1,0.996835,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1] area_under_roc_curve 0.9956960035029055 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968354,0.996835,1,0.990506,1,1,0.893082,0.968553,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.990506,1,1,1,1,0.996835,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.886076,1] area_under_roc_curve 0.997515872541995 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671,0.996835,0.996835,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.993671,1,1,1,1,1,1,1,1,1,0.993671,1,0.987421,0.962264,1,0.996835,1,1,0.987342,1,1,0.993671,1,1,1,1,1,1,0.974843,0.937107,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835] area_under_roc_curve 0.9973539129050234 [1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.993711,0.990506,0.974843,0.996835,1,0.981132,0.993671,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.949367,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.993671,1,1,0.968354,0.990506,1,1,1,1,1,0.996835,1,1,1,1,1,0.987342,1,1,1,1,0.993711,0.987342,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1] area_under_roc_curve 0.9977091792054775 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.968354,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993671,1,0.993711,0.993711,0.987342,1,1,1,1,1,0.990506,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,1,1,0.996835,1,1,0.996835,1,1,0.996835,1,1,0.993711,1,1,1] 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 0.8041924914136829 kappa 0.7978042808624911 kappa 0.8736526236822364 kappa 0.8041924914136829 kappa 0.8167673656359831 kappa 0.8168035375868604 kappa 0.8167963043392427 kappa 0.8167528928557324 kappa 0.86101788605046 kappa 0.7979080323662917 kb_relative_information_score 99.34141196746538 kb_relative_information_score 97.14221705069181 kb_relative_information_score 98.54794532469978 kb_relative_information_score 97.52802897433355 kb_relative_information_score 98.17927869331014 kb_relative_information_score 97.87840986435256 kb_relative_information_score 101.38114562620511 kb_relative_information_score 98.36913884296887 kb_relative_information_score 99.35789622344286 kb_relative_information_score 98.87826453647824 mean_absolute_error 0.015841794678643172 mean_absolute_error 0.016199753365984212 mean_absolute_error 0.01609076536135995 mean_absolute_error 0.016193567998006548 mean_absolute_error 0.01614286573251022 mean_absolute_error 0.016041757760254446 mean_absolute_error 0.015622410590358172 mean_absolute_error 0.016066529331538137 mean_absolute_error 0.015876015711090354 mean_absolute_error 0.0159072300272654 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 0.8 predictive_accuracy 0.875 predictive_accuracy 0.80625 predictive_accuracy 0.81875 predictive_accuracy 0.81875 predictive_accuracy 0.81875 predictive_accuracy 0.81875 predictive_accuracy 0.8625 predictive_accuracy 0.8 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 [1,0.5,1,0,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1] recall 0.8 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,0,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0] recall 0.875 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1] recall 0.80625 [0,1,1,1,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1] recall 0.81875 [1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.81875 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1] recall 0.81875 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.81875 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5] recall 0.8625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1] recall 0.8 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.8000906403355124 relative_absolute_error 0.8181693619183932 relative_absolute_error 0.8126649172404001 relative_absolute_error 0.817856969596289 relative_absolute_error 0.8152962491166764 relative_absolute_error 0.8101897858714353 relative_absolute_error 0.789010635876674 relative_absolute_error 0.8114408753302077 relative_absolute_error 0.8018189753075923 relative_absolute_error 0.8033954559224936 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 0.08169874107845279 root_mean_squared_error 0.08308599133185685 root_mean_squared_error 0.08247599694463648 root_mean_squared_error 0.08310331812990068 root_mean_squared_error 0.08277260131681821 root_mean_squared_error 0.0826395310175451 root_mean_squared_error 0.08059803158756512 root_mean_squared_error 0.0823975528611417 root_mean_squared_error 0.0818717323329692 root_mean_squared_error 0.082212630246582 root_relative_squared_error 0.8211032424291619 root_relative_squared_error 0.8350456320681517 root_relative_squared_error 0.8289149578056285 root_relative_squared_error 0.8352197729406661 root_relative_squared_error 0.831895943907767 root_relative_squared_error 0.8305585370791938 root_relative_squared_error 0.8100406958095937 root_relative_squared_error 0.8281265651026724 root_relative_squared_error 0.822841869954143 root_relative_squared_error 0.8262680228973813 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 1646.5013150009327 usercpu_time_millis 1475.728570992942 usercpu_time_millis 1481.5599110006588 usercpu_time_millis 1516.3852510013385 usercpu_time_millis 1490.6870720005827 usercpu_time_millis 1508.3172520025983 usercpu_time_millis 1487.3479610032518 usercpu_time_millis 1500.4941459992551 usercpu_time_millis 1556.4973130021826 usercpu_time_millis 1482.7168690026156 usercpu_time_millis_testing 141.80146800208604 usercpu_time_millis_testing 143.2879749991116 usercpu_time_millis_testing 137.56412300426746 usercpu_time_millis_testing 141.3222670016694 usercpu_time_millis_testing 139.05088100000285 usercpu_time_millis_testing 137.9753660003189 usercpu_time_millis_testing 138.80567400337895 usercpu_time_millis_testing 146.26715699705528 usercpu_time_millis_testing 146.21803499903763 usercpu_time_millis_testing 138.86329700471833 usercpu_time_millis_training 1504.6998469988466 usercpu_time_millis_training 1332.4405959938304 usercpu_time_millis_training 1343.9957879963913 usercpu_time_millis_training 1375.062983999669 usercpu_time_millis_training 1351.6361910005799 usercpu_time_millis_training 1370.3418860022794 usercpu_time_millis_training 1348.5422869998729 usercpu_time_millis_training 1354.2269890021998 usercpu_time_millis_training 1410.279278003145 usercpu_time_millis_training 1343.8535719978972