8810768 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) 6785818 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "median" 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 22.26982743759295 7708 cache_size 200 7708 class_weight null 7708 coef0 0.8397611701700962 7708 decision_function_shape null 7708 degree 5 7708 gamma 0.00971485253059389 7708 kernel "sigmoid" 7708 max_iter -1 7708 probability true 7708 random_state 11241 7708 shrinking true 7708 tol 0.013089055713910303 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 18537475 description https://api.openml.org/data/download/18537475/description.xml -1 18537476 predictions https://api.openml.org/data/download/18537476/predictions.arff area_under_roc_curve 0.9960400883838385 [0.988163,0.999882,1,0.999171,0.996173,0.994476,0.999961,0.998658,0.996015,1,0.999014,1,0.999724,0.978299,0.999092,0.99929,1,0.98761,0.987255,0.970644,0.997751,0.999171,0.994989,1,0.998027,0.979837,0.985598,0.999448,0.999132,0.999961,0.999961,1,1,0.991438,0.999329,0.999132,0.993016,0.985085,0.999842,0.999763,0.998816,0.999527,1,0.999171,0.997988,0.999842,0.998224,0.991872,0.998185,0.995423,0.994555,0.998224,0.997948,0.99128,0.997435,0.984138,1,0.999842,0.9942,0.99491,0.999092,0.99925,0.996686,0.999211,0.999408,0.993056,0.989544,1,0.998974,0.994476,0.994437,0.998856,0.99491,0.998224,0.985085,0.999724,0.999645,0.9884,1,0.999763,0.992977,0.997554,0.998067,0.997396,0.999448,0.999487,1,0.999527,0.999605,0.999921,0.996962,0.998224,0.997633,0.99783,0.998067,0.997672,0.997277,0.99779,0.964765,0.99858] average_cost 0 f_measure 0.7999698423230379 [0.702703,0.875,1,0.903226,0.903226,0.740741,1,0.967742,0.823529,0.967742,0.8,0.9375,0.9375,0.647059,0.842105,0.903226,0.967742,0.3125,0.387097,0.137931,0.8,0.967742,0.722222,1,0.903226,0.424242,0.378378,0.848485,0.903226,0.933333,0.967742,1,0.967742,0.611111,0.83871,0.8,0.588235,0.333333,0.848485,0.896552,0.787879,0.941176,1,0.875,0.692308,0.9375,0.75,0.709677,0.848485,0.866667,0.8125,0.8,0.866667,0.8,0.709677,0.533333,1,0.903226,0.611111,0.756757,0.75,0.875,0.6875,0.903226,0.875,0.625,0.588235,1,0.875,0.764706,0.758621,0.8,0.625,0.933333,0.787879,0.909091,0.903226,0.5,0.967742,0.969697,0.8,0.848485,0.777778,0.62069,0.857143,0.866667,0.933333,0.909091,0.9375,0.9375,0.733333,0.692308,0.857143,0.875,0.9375,0.787879,0.8,0.736842,0.424242,0.875] kappa 0.7967171717171717 kb_relative_information_score 965.8080896024339 mean_absolute_error 0.01642563384512133 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 0.8084113022770143 [0.619048,0.875,1,0.933333,0.933333,0.909091,1,1,0.777778,1,0.857143,0.9375,0.9375,0.611111,0.727273,0.933333,1,0.3125,0.4,0.153846,0.857143,1,0.65,1,0.933333,0.411765,0.333333,0.823529,0.933333,1,1,1,1,0.55,0.866667,0.857143,0.555556,0.357143,0.823529,1,0.764706,0.888889,1,0.875,0.9,0.9375,0.75,0.733333,0.823529,0.928571,0.8125,0.857143,0.928571,0.857143,0.733333,0.571429,1,0.933333,0.55,0.666667,0.75,0.875,0.6875,0.933333,0.875,0.625,0.555556,1,0.875,0.722222,0.846154,0.857143,0.625,1,0.764706,0.882353,0.933333,0.45,1,0.941176,0.857143,0.823529,0.7,0.692308,0.789474,0.928571,1,0.882353,0.9375,0.9375,0.785714,0.9,0.789474,0.875,0.9375,0.764706,0.736842,0.636364,0.411765,0.875] predictive_accuracy 0.79875 prior_entropy 6.6438561897747395 recall 0.79875 [0.8125,0.875,1,0.875,0.875,0.625,1,0.9375,0.875,0.9375,0.75,0.9375,0.9375,0.6875,1,0.875,0.9375,0.3125,0.375,0.125,0.75,0.9375,0.8125,1,0.875,0.4375,0.4375,0.875,0.875,0.875,0.9375,1,0.9375,0.6875,0.8125,0.75,0.625,0.3125,0.875,0.8125,0.8125,1,1,0.875,0.5625,0.9375,0.75,0.6875,0.875,0.8125,0.8125,0.75,0.8125,0.75,0.6875,0.5,1,0.875,0.6875,0.875,0.75,0.875,0.6875,0.875,0.875,0.625,0.625,1,0.875,0.8125,0.6875,0.75,0.625,0.875,0.8125,0.9375,0.875,0.5625,0.9375,1,0.75,0.875,0.875,0.5625,0.9375,0.8125,0.875,0.9375,0.9375,0.9375,0.6875,0.5625,0.9375,0.875,0.9375,0.8125,0.875,0.875,0.4375,0.875] relative_absolute_error 0.8295774669253181 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08412628279231872 root_relative_squared_error 0.8455009546347059 total_cost 0 area_under_roc_curve 0.9951004099992036 [0.974843,1,1,1,0.993671,1,1,1,0.993671,1,1,1,1,0.955696,1,0.981132,1,1,0.981013,0.974843,0.993711,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,1,1,1,0.993711,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.981013,1,1,0.962264,0.993671,1,1,1,1,1,1,0.96519,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.993671,1,0.984177,1,0.993671,0.908228,1] area_under_roc_curve 0.9963639937106918 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.924051,1,1,1,0.971519,0.993671,0.987421,1,1,1,1,1,0.981013,0.974684,1,1,1,1,1,1,1,1,1,0.993711,0.987421,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.981132,1,1,1,0.987342,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,1,1,0.981013,1,0.993711,1,0.996835,1,1,1,1,1,0.993671,1,1,0.993671,1,1,1,0.996835,0.984177,0.974843] area_under_roc_curve 0.9963649888543904 [1,1,1,1,1,0.993671,1,1,0.993671,1,1,1,1,0.993671,0.996835,1,1,0.984177,1,0.936709,1,1,0.987342,1,1,0.974684,0.993671,1,1,1,1,1,1,0.984177,1,1,1,0.993711,1,1,0.993671,0.993711,1,1,0.996835,1,1,0.993711,0.996835,1,1,1,1,1,1,0.993671,1,1,0.981132,0.984177,1,1,1,0.987421,1,0.993671,1,1,1,0.984177,1,0.996835,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,0.993711,0.993671,1,1,1,1,1,1,1,0.949686,1] area_under_roc_curve 0.9960495283018866 [0.996835,1,1,1,0.987342,0.993671,1,1,0.993671,1,1,1,1,0.984177,1,1,1,0.996835,0.955975,0.955696,1,1,0.996835,1,0.993711,0.974684,1,1,1,1,1,1,1,0.996835,1,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,0.981013,1,1,0.993711,1,1,1,1,1,1,0.996835,0.984177,1,1,0.984177,0.974684,1,0.981132,1,0.993711,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.993671,0.974843,0.996835] area_under_roc_curve 0.996484157312316 [1,1,1,1,1,1,1,0.993671,1,1,0.993671,1,1,0.968553,0.993711,1,1,0.996835,1,0.990506,1,1,1,1,0.993671,0.996835,0.984177,1,1,1,1,1,1,0.977848,1,1,1,0.977848,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.981132,1,1,0.993671,0.993671,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.933544,1,0.993671,1,1,1,1,1,1,0.993671,0.993711,0.993711,1,1,1,1,1,0.996835,1,0.993711,0.974684,0.993711,1,1,0.974843,1] area_under_roc_curve 0.995138474245681 [0.93038,1,1,0.993671,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.993711,0.962025,0.996835,1,0.990506,1,0.993671,0.981013,0.974684,1,1,1,1,1,1,0.993671,1,0.996835,0.996835,0.977848,1,1,0.996835,1,1,1,1,1,1,0.993671,0.996835,0.996835,1,1,1,1,0.993671,0.984177,1,1,0.996835,1,0.990506,1,0.993711,1,1,0.971519,1,1,1,1,1,1,1,1,0.993671,1,1,0.993671,1,1,0.993711,0.996835,1,0.993671,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,0.924528,1] area_under_roc_curve 0.9957756149988057 [0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.987421,0.990506,0.977848,1,1,0.990506,1,1,0.91195,0.949686,0.996835,1,1,0.996835,1,1,0.993711,1,1,0.971519,0.993671,1,1,1,0.996835,1,1,0.974843,1,0.996835,0.987342,1,1,0.987421,1,1,1,0.993671,0.993671,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.96519,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,0.943038,1] area_under_roc_curve 0.9964485809250856 [0.984177,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.981132,0.984177,0.984177,0.993671,1,0.996835,1,0.993671,0.987421,0.993711,1,0.987421,1,1,1,1,1,1,1,0.993671,0.981013,1,1,0.996835,1,1,1,1,1,1,0.990506,1,0.981132,0.955975,0.996835,0.996835,1,1,0.981013,1,1,0.993671,1,1,1,1,1,1,0.968553,0.955975,1,1,0.993671,1,1,1,1,0.996835,1,1,0.974843,1,1,0.987342,1,0.990506,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1] area_under_roc_curve 0.9970391987102938 [1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,0.996835,1,1,0.987421,1,0.968553,1,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,1,1,1,0.993671,1,1,0.993711,0.990506,0.993711,1,0.990506,1,1,0.962025,0.996835,0.955975,1,1,0.996835,1,1,1,1,1,1,1,0.984177,1,0.996835,1,1,0.987421,0.981013,1,1,1,1,0.993711,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,1,0.990506,1] area_under_roc_curve 0.9972750477668975 [0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,0.987421,0.974684,0.981132,1,1,1,1,1,0.993711,0.993711,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,0.993671,1,1,1,1,0.981013,1,1,1,0.987342,1,0.993711,0.993711,0.987342,1,1,1,1,1,0.981013,0.987342,1,0.993671,1,1,1,1,1,0.968553,1,0.993711,1,1,1,1,1,1,0.993671,1,1,0.993671,1,1,0.993711,0.996835,1,1] 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 0.7789182787208844 kappa 0.8231206569804169 kappa 0.8420345944238212 kappa 0.7978840991631139 kappa 0.8041383667667036 kappa 0.7536420703541395 kappa 0.8041615667074664 kappa 0.7914938988271532 kappa 0.8168035375868604 kappa 0.7537198563365829 kb_relative_information_score 96.18918792421987 kb_relative_information_score 96.49580057971546 kb_relative_information_score 96.92557907857082 kb_relative_information_score 96.4045237908179 kb_relative_information_score 96.58762189616873 kb_relative_information_score 94.78784523092521 kb_relative_information_score 97.79698532062439 kb_relative_information_score 96.42698087432194 kb_relative_information_score 97.57307778076014 kb_relative_information_score 96.62048712631058 mean_absolute_error 0.016375581281013737 mean_absolute_error 0.016439013340488612 mean_absolute_error 0.01640273704963632 mean_absolute_error 0.016436219640053422 mean_absolute_error 0.016463149514720124 mean_absolute_error 0.016607310937820006 mean_absolute_error 0.01630128028064684 mean_absolute_error 0.016427268344185363 mean_absolute_error 0.016346762102093952 mean_absolute_error 0.016457015960554943 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 0.78125 predictive_accuracy 0.825 predictive_accuracy 0.84375 predictive_accuracy 0.8 predictive_accuracy 0.80625 predictive_accuracy 0.75625 predictive_accuracy 0.80625 predictive_accuracy 0.79375 predictive_accuracy 0.81875 predictive_accuracy 0.75625 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 0.78125 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,0.5,0,0.5,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1] recall 0.825 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,0,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,0,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,0] recall 0.84375 [1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1] recall 0.8 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1] recall 0.80625 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,1,0.5,0,1,0.5,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,0,0.5,0,1,1,1,1] recall 0.75625 [0.5,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0,0.5,0.5,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0,1,0,1,1,1,1,0,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5] recall 0.80625 [1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.79375 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0.5,1,1,1,0.5,0,1,1,0,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,1,0,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1] recall 0.81875 [1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,0.5,0,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,0.5,1] recall 0.75625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,0,0.5,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,0.5,0.5,1,1,1,0.5,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1] relative_absolute_error 0.827049559647157 relative_absolute_error 0.830253199014575 relative_absolute_error 0.8284210631129442 relative_absolute_error 0.8301121030329998 relative_absolute_error 0.8314721977131362 relative_absolute_error 0.8387530776676756 relative_absolute_error 0.8232969838710511 relative_absolute_error 0.8296600173830977 relative_absolute_error 0.8255940455602994 relative_absolute_error 0.8311624222502484 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 0.0839931581607379 root_mean_squared_error 0.08410258075773484 root_mean_squared_error 0.08401162020191365 root_mean_squared_error 0.08418687325284897 root_mean_squared_error 0.0842242745365714 root_mean_squared_error 0.08505666253485028 root_mean_squared_error 0.08355631370920083 root_mean_squared_error 0.08410720914646015 root_mean_squared_error 0.08369484201039043 root_mean_squared_error 0.08432061946264333 root_relative_squared_error 0.8441630017458942 root_relative_squared_error 0.8452627402241523 root_relative_squared_error 0.8443485522411792 root_relative_squared_error 0.8461099116754748 root_relative_squared_error 0.8464858087202775 root_relative_squared_error 0.8548516228726595 root_relative_squared_error 0.8397725498140851 root_relative_squared_error 0.8453092572810788 root_relative_squared_error 0.8411648116258766 root_relative_squared_error 0.8474541116603838 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 1575.7990929996595 usercpu_time_millis 1371.701784999459 usercpu_time_millis 1383.819469003356 usercpu_time_millis 1395.908149999741 usercpu_time_millis 1398.4147880037199 usercpu_time_millis 1390.709755003627 usercpu_time_millis 1424.0084730045055 usercpu_time_millis 1416.7559659981634 usercpu_time_millis 1401.7631980022998 usercpu_time_millis 1389.058600005228 usercpu_time_millis_testing 152.4009839995415 usercpu_time_millis_testing 151.97471099963877 usercpu_time_millis_testing 151.26828500069678 usercpu_time_millis_testing 160.6750610008021 usercpu_time_millis_testing 156.78773800027557 usercpu_time_millis_testing 152.42011900409125 usercpu_time_millis_testing 160.57937900040997 usercpu_time_millis_testing 161.00564599764766 usercpu_time_millis_testing 157.08829700452043 usercpu_time_millis_testing 151.94120600062888 usercpu_time_millis_training 1423.398109000118 usercpu_time_millis_training 1219.7270739998203 usercpu_time_millis_training 1232.5511840026593 usercpu_time_millis_training 1235.233088998939 usercpu_time_millis_training 1241.6270500034443 usercpu_time_millis_training 1238.2896359995357 usercpu_time_millis_training 1263.4290940040955 usercpu_time_millis_training 1255.7503200005158 usercpu_time_millis_training 1244.6749009977793 usercpu_time_millis_training 1237.1173940045992