8803186 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) 6778267 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 5.166929111578464 7708 cache_size 200 7708 class_weight null 7708 coef0 -0.7177546519023601 7708 decision_function_shape null 7708 degree 5 7708 gamma 0.14356724069100682 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 2501 7708 shrinking true 7708 tol 2.7013316243476966e-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 18522377 description https://api.openml.org/data/download/18522377/description.xml -1 18522378 predictions https://api.openml.org/data/download/18522378/predictions.arff area_under_roc_curve 0.9952304292929293 [0.982757,0.999211,0.999961,0.999329,0.998343,0.991635,0.999645,0.997356,0.995147,0.999763,0.998224,0.999527,0.998935,0.943734,0.998027,0.998422,1,0.992661,0.983744,0.972932,0.99708,0.999092,0.993095,0.998895,0.998737,0.977588,0.988202,0.998619,0.998146,1,0.999092,0.999921,1,0.989978,0.995739,0.998461,0.993332,0.98398,0.998698,0.999408,0.998027,0.996094,1,0.998737,0.99562,1,0.998777,0.997909,0.996922,0.996725,0.995028,0.998461,0.997909,0.993963,0.994515,0.992069,1,0.998974,0.99199,0.995581,0.996528,0.999487,0.996843,0.998777,0.99858,0.992069,0.984336,0.999645,0.996607,0.991359,0.997475,0.998303,0.99416,0.996488,0.989228,0.999171,0.998816,0.990767,0.99925,0.999961,0.99637,0.995857,0.997317,0.994397,0.999053,0.999724,0.999803,0.998501,0.998974,0.999329,0.999605,0.999605,0.997159,0.994279,0.992858,0.995265,0.995344,0.997554,0.965278,0.9942] average_cost 0 f_measure 0.7829861019649074 [0.631579,0.933333,0.967742,0.814815,0.857143,0.689655,0.9375,0.702703,0.75,0.857143,0.848485,0.896552,0.882353,0.606061,0.857143,0.777778,0.933333,0.580645,0.388889,0.242424,0.733333,0.9375,0.6875,0.857143,0.857143,0.444444,0.516129,0.848485,0.903226,0.967742,0.903226,0.967742,0.9375,0.484848,0.8,0.848485,0.413793,0.5,0.848485,0.896552,0.727273,0.875,0.967742,0.833333,0.857143,0.914286,0.814815,0.787879,0.705882,0.787879,0.875,0.8,0.787879,0.647059,0.774194,0.625,0.896552,0.9375,0.631579,0.647059,0.814815,0.833333,0.689655,0.903226,0.875,0.702703,0.4,0.9375,0.709677,0.709677,0.823529,0.785714,0.709677,0.896552,0.7,0.909091,0.896552,0.615385,0.967742,1,0.848485,0.8125,0.83871,0.529412,0.909091,0.866667,0.896552,0.8,0.903226,0.83871,0.774194,0.785714,0.866667,0.75,0.882353,0.777778,0.742857,0.8,0.555556,0.83871] kappa 0.7771464646464646 kb_relative_information_score 943.944003261933 mean_absolute_error 0.016073423235357286 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 0.797366438722358 [0.545455,1,1,1,1,0.769231,0.9375,0.619048,0.75,1,0.823529,1,0.833333,0.588235,1,0.7,1,0.6,0.35,0.235294,0.785714,0.9375,0.6875,0.789474,0.789474,0.4,0.533333,0.823529,0.933333,1,0.933333,1,0.9375,0.470588,0.857143,0.823529,0.461538,0.5,0.823529,1,0.705882,0.875,1,0.75,1,0.842105,1,0.764706,0.666667,0.764706,0.875,0.857143,0.764706,0.611111,0.8,0.625,1,0.9375,0.545455,0.611111,1,0.75,0.769231,0.933333,0.875,0.619048,0.428571,0.9375,0.733333,0.733333,0.777778,0.916667,0.733333,1,0.583333,0.882353,1,0.521739,1,1,0.823529,0.8125,0.866667,0.5,0.882353,0.928571,1,0.857143,0.933333,0.866667,0.8,0.916667,0.928571,0.75,0.833333,0.7,0.684211,0.857143,0.5,0.866667] predictive_accuracy 0.779375 prior_entropy 6.6438561897747395 recall 0.779375 [0.75,0.875,0.9375,0.6875,0.75,0.625,0.9375,0.8125,0.75,0.75,0.875,0.8125,0.9375,0.625,0.75,0.875,0.875,0.5625,0.4375,0.25,0.6875,0.9375,0.6875,0.9375,0.9375,0.5,0.5,0.875,0.875,0.9375,0.875,0.9375,0.9375,0.5,0.75,0.875,0.375,0.5,0.875,0.8125,0.75,0.875,0.9375,0.9375,0.75,1,0.6875,0.8125,0.75,0.8125,0.875,0.75,0.8125,0.6875,0.75,0.625,0.8125,0.9375,0.75,0.6875,0.6875,0.9375,0.625,0.875,0.875,0.8125,0.375,0.9375,0.6875,0.6875,0.875,0.6875,0.6875,0.8125,0.875,0.9375,0.8125,0.75,0.9375,1,0.875,0.8125,0.8125,0.5625,0.9375,0.8125,0.8125,0.75,0.875,0.8125,0.75,0.6875,0.8125,0.75,0.9375,0.875,0.8125,0.75,0.625,0.8125] relative_absolute_error 0.8117890522907705 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08328210463861384 root_relative_squared_error 0.8370166449618146 total_cost 0 area_under_roc_curve 0.9962871188599636 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.977848,1,0.981132,1,1,0.981013,0.993711,0.981132,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,0.993671,0.993671,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,0.943396,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.949367,1] area_under_roc_curve 0.9947431534113523 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.867089,1,1,1,0.977848,0.993671,0.987421,1,0.993711,0.981132,1,1,0.984177,0.968354,1,1,1,1,1,1,0.996835,0.977848,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,0.993671,1,1,0.993671,1,1,1,0.993711,1,1,1,0.987342,1,1,1,1,1,0.996835,1,1,0.981132,1,1,1,1,1,1,1,0.996835,0.996835,1,1,0.968354,0.990506,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.955696,1,1,1,0.996835,1,0.981132] area_under_roc_curve 0.9960495283018869 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.990506,1,1,0.993671,1,0.936709,1,1,0.990506,1,1,0.946203,0.990506,0.993711,1,1,1,1,1,0.981013,1,1,0.987421,1,0.993711,0.996835,0.996835,0.962264,1,1,1,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,1,0.987421,0.996835,0.990506,1,1,0.993711,0.990506,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,0.996835,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1] area_under_roc_curve 0.9964854012419391 [0.996835,1,1,0.993711,1,0.987342,1,1,0.996835,1,1,1,1,1,0.993671,1,1,0.987342,0.937107,0.981013,1,1,0.993671,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.955975,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.984177,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.968354,0.987342,0.996835,0.993711,1,0.993711,1,1,0.993671,1,1,1,1,0.990506,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.984177,1,1,1] area_under_roc_curve 0.9949882075471699 [1,1,1,1,1,1,1,0.996835,0.993711,1,0.987342,1,1,0.874214,1,0.996835,1,0.990506,0.968553,0.984177,1,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.984177,1,1,0.996835,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.984177,1,0.990506,1,1,0.987342,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.949367,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.958861,0.993711,1,1,0.899371,1] area_under_roc_curve 0.9952942142345353 [0.901899,1,1,1,1,0.977848,1,0.990506,1,1,1,1,1,1,0.993711,1,1,1,1,0.96519,1,1,1,1,0.993671,0.981013,0.977848,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.987342,0.993671,1,0.990506,1,1,0.993711,1,1,1,1,0.993671,0.993671,1,1,0.993671,1,0.996835,0.987342,1,1,0.993671,0.993711,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.993671,1,1,0.993671,1,1,0.996835,1,1,1,1,1,0.993711,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.990506] area_under_roc_curve 0.9947476315579967 [0.996835,1,1,1,1,1,1,0.993671,0.993711,1,1,1,0.993671,1,1,1,1,1,1,0.971519,0.993671,1,0.990506,1,1,0.886792,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.974843,1,0.996835,0.996835,0.993711,1,1,1,1,1,0.996835,1,1,0.996835,0.990506,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,0.993711,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835,1,0.838608,1] area_under_roc_curve 0.9966488535944589 [0.996835,1,1,1,1,1,1,1,1,1,0.993711,1,0.993671,1,0.993711,1,1,0.993711,0.993671,0.984177,0.996835,1,0.993671,1,0.993671,0.993711,1,1,0.968553,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.981132,0.955975,1,1,1,1,0.987342,1,1,0.996835,1,1,1,1,1,1,0.974843,0.90566,1,1,0.996835,1,1,1,1,1,1,1,0.968553,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,0.993671,1,1,1,0.987342] area_under_roc_curve 0.9965247094180396 [1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,0.993711,0.996835,0.981132,0.996835,1,0.955975,0.990506,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.955696,1,1,1,1,1,1,0.996835,1,1,0.993671,0.993711,1,0.993671,1,1,0.987342,0.971519,0.993711,1,1,1,1,0.990506,1,1,1,1,1,0.987342,1,1,1,1,0.993711,0.971519,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,0.987421,0.984177,1,1,1,1] area_under_roc_curve 0.9971160735610225 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,0.96519,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.990506,0.996835,0.993711,0.987421,0.977848,1,1,1,1,1,0.987342,0.987342,1,0.987342,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.993671,1,1,1,1,1,1,1,0.996835,0.993711,0.996835,1,1] 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 0.753700414446418 kappa 0.7599494630448516 kappa 0.7789095503178175 kappa 0.7725747226280255 kappa 0.8104415133085854 kappa 0.772592680326898 kappa 0.7915103652517275 kappa 0.8041306322315681 kappa 0.785226420308737 kappa 0.7411003236245954 kb_relative_information_score 95.88631906346545 kb_relative_information_score 92.7275907397015 kb_relative_information_score 92.72149404233558 kb_relative_information_score 91.98997571036449 kb_relative_information_score 94.27886340024784 kb_relative_information_score 94.07506966876298 kb_relative_information_score 98.91762911978537 kb_relative_information_score 94.91580851044687 kb_relative_information_score 94.1245734528413 kb_relative_information_score 94.30667955398171 mean_absolute_error 0.01586453683795319 mean_absolute_error 0.016336552716826803 mean_absolute_error 0.016337319149881936 mean_absolute_error 0.01643959846641077 mean_absolute_error 0.016203126029287574 mean_absolute_error 0.016058516756325247 mean_absolute_error 0.015444083264252072 mean_absolute_error 0.016079452868128695 mean_absolute_error 0.016040150982200248 mean_absolute_error 0.01593089528230616 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 0.75625 predictive_accuracy 0.7625 predictive_accuracy 0.78125 predictive_accuracy 0.775 predictive_accuracy 0.8125 predictive_accuracy 0.775 predictive_accuracy 0.79375 predictive_accuracy 0.80625 predictive_accuracy 0.7875 predictive_accuracy 0.74375 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 0.75625 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,1,0,1,0,0.5,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,0,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.7625 [1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,0,0,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,0.5,0] recall 0.78125 [1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,0,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1] recall 0.775 [0,1,1,0,0.5,0,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1] recall 0.8125 [1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.775 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,0.5,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,0.5] recall 0.79375 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0.5,0.5,0.5,0.5,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.80625 [0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5] recall 0.7875 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,0,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,0,1,1,1,0.5,0,1,0.5,1] recall 0.74375 [1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0.5,1,0,1,1,1,0,1,0,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.8012392342400588 relative_absolute_error 0.8250784200417565 relative_absolute_error 0.8251171287819146 relative_absolute_error 0.8302827508288254 relative_absolute_error 0.818339698448866 relative_absolute_error 0.811036199814405 relative_absolute_error 0.7800042052652549 relative_absolute_error 0.8120935791984175 relative_absolute_error 0.8101086354646576 relative_absolute_error 0.8045906708235421 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 0.08245078465995909 root_mean_squared_error 0.0841433866424341 root_mean_squared_error 0.08421470820285183 root_mean_squared_error 0.08475135288323209 root_mean_squared_error 0.083580900951143 root_mean_squared_error 0.08331945062812446 root_mean_squared_error 0.08079017054044406 root_mean_squared_error 0.08301240745163047 root_mean_squared_error 0.08332596636193804 root_mean_squared_error 0.08316677976597323 root_relative_squared_error 0.8286615648105298 root_relative_squared_error 0.845672854796231 root_relative_squared_error 0.846389663448862 root_relative_squared_error 0.8517831454202605 root_relative_squared_error 0.8400196608933331 root_relative_squared_error 0.8373919862788795 root_relative_squared_error 0.8119717649438701 root_relative_squared_error 0.8343060862459416 root_relative_squared_error 0.8374574718676474 root_relative_squared_error 0.8358575863813772 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 1899.6612090000085 usercpu_time_millis 1696.4800400000045 usercpu_time_millis 1682.8739190000022 usercpu_time_millis 1703.8600240000007 usercpu_time_millis 1686.9637989999974 usercpu_time_millis 2051.7652119999993 usercpu_time_millis 1740.8602060000007 usercpu_time_millis 1734.6667699999969 usercpu_time_millis 1670.588591000005 usercpu_time_millis 1681.9858069999966 usercpu_time_millis_testing 143.95241600000475 usercpu_time_millis_testing 140.8744770000041 usercpu_time_millis_testing 144.4663480000017 usercpu_time_millis_testing 138.64645600000358 usercpu_time_millis_testing 138.30147099999834 usercpu_time_millis_testing 188.22338700000074 usercpu_time_millis_testing 140.0721309999966 usercpu_time_millis_testing 139.29527299999478 usercpu_time_millis_testing 133.22775800000386 usercpu_time_millis_testing 139.12996499999508 usercpu_time_millis_training 1755.7087930000037 usercpu_time_millis_training 1555.6055630000003 usercpu_time_millis_training 1538.4075710000004 usercpu_time_millis_training 1565.2135679999972 usercpu_time_millis_training 1548.662327999999 usercpu_time_millis_training 1863.5418249999987 usercpu_time_millis_training 1600.788075000004 usercpu_time_millis_training 1595.3714970000021 usercpu_time_millis_training 1537.360833000001 usercpu_time_millis_training 1542.8558420000015