8803059 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) 6778140 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 14839.356701618754 7708 cache_size 200 7708 class_weight null 7708 coef0 -0.31772049739763175 7708 decision_function_shape null 7708 degree 4 7708 gamma 1.23794777956786 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 50059 7708 shrinking true 7708 tol 0.0068565365371139496 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 18522123 description https://api.openml.org/data/download/18522123/description.xml -1 18522124 predictions https://api.openml.org/data/download/18522124/predictions.arff area_under_roc_curve 0.9959071180555553 [0.985756,0.999448,0.999961,0.999487,0.998303,0.993213,0.999724,0.998777,0.996528,0.999882,0.998658,0.999605,0.999211,0.960227,0.998698,0.999171,1,0.991951,0.983941,0.974471,0.997396,0.999605,0.994555,0.999092,0.998777,0.978614,0.98986,0.998737,0.998185,1,0.99925,0.999921,1,0.990412,0.997475,0.998422,0.993213,0.983389,0.99925,0.999645,0.998658,0.997317,1,0.998698,0.995896,1,0.999014,0.998737,0.997356,0.996804,0.995028,0.998737,0.997988,0.993174,0.995818,0.992819,1,0.999171,0.993134,0.996528,0.997396,0.999566,0.996962,0.999053,0.999014,0.993056,0.985874,0.999724,0.997672,0.992858,0.996962,0.99858,0.995107,0.997711,0.989583,0.999369,0.99929,0.990688,0.999566,0.999961,0.996686,0.996567,0.997751,0.995423,0.99925,0.999961,0.999842,0.999053,0.999092,0.999803,0.999487,0.999605,0.99779,0.996252,0.994673,0.996567,0.996922,0.998106,0.966422,0.995778] average_cost 0 f_measure 0.805684472591728 [0.648649,0.903226,0.967742,0.857143,0.857143,0.8,0.9375,0.756757,0.83871,0.896552,0.848485,0.866667,0.882353,0.625,0.896552,0.875,0.933333,0.580645,0.411765,0.242424,0.774194,0.9375,0.764706,0.882353,0.882353,0.411765,0.588235,0.848485,0.903226,1,0.9375,0.967742,0.9375,0.580645,0.83871,0.875,0.5625,0.533333,0.9375,0.896552,0.705882,0.875,0.933333,0.848485,0.827586,0.888889,0.896552,0.8125,0.685714,0.8125,0.848485,0.758621,0.787879,0.727273,0.774194,0.606061,0.933333,0.969697,0.631579,0.722222,0.827586,0.882353,0.689655,0.896552,0.909091,0.722222,0.451613,0.9375,0.83871,0.6875,0.787879,0.827586,0.727273,0.933333,0.7,0.9375,0.896552,0.631579,1,1,0.848485,0.8125,0.83871,0.545455,0.875,0.866667,0.967742,0.8,0.903226,0.933333,0.8125,0.827586,0.866667,0.8,0.909091,0.823529,0.810811,0.83871,0.606061,0.866667] kappa 0.8005050505050505 kb_relative_information_score 970.4495532565624 mean_absolute_error 0.01591189338788163 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 0.8164975317510144 [0.571429,0.933333,1,1,1,0.857143,0.9375,0.666667,0.866667,1,0.823529,0.928571,0.833333,0.625,1,0.875,1,0.6,0.388889,0.235294,0.8,0.9375,0.722222,0.833333,0.833333,0.388889,0.555556,0.823529,0.933333,1,0.9375,1,0.9375,0.6,0.866667,0.875,0.5625,0.571429,0.9375,1,0.666667,0.875,1,0.823529,0.923077,0.8,1,0.8125,0.631579,0.8125,0.823529,0.846154,0.764706,0.705882,0.8,0.588235,1,0.941176,0.545455,0.65,0.923077,0.833333,0.769231,1,0.882353,0.65,0.466667,0.9375,0.866667,0.6875,0.764706,0.923077,0.705882,1,0.583333,0.9375,1,0.545455,1,1,0.823529,0.8125,0.866667,0.529412,0.875,0.928571,1,0.857143,0.933333,1,0.8125,0.923077,0.928571,0.857143,0.882353,0.777778,0.714286,0.866667,0.588235,0.928571] predictive_accuracy 0.8025 prior_entropy 6.6438561897747395 recall 0.8025 [0.75,0.875,0.9375,0.75,0.75,0.75,0.9375,0.875,0.8125,0.8125,0.875,0.8125,0.9375,0.625,0.8125,0.875,0.875,0.5625,0.4375,0.25,0.75,0.9375,0.8125,0.9375,0.9375,0.4375,0.625,0.875,0.875,1,0.9375,0.9375,0.9375,0.5625,0.8125,0.875,0.5625,0.5,0.9375,0.8125,0.75,0.875,0.875,0.875,0.75,1,0.8125,0.8125,0.75,0.8125,0.875,0.6875,0.8125,0.75,0.75,0.625,0.875,1,0.75,0.8125,0.75,0.9375,0.625,0.8125,0.9375,0.8125,0.4375,0.9375,0.8125,0.6875,0.8125,0.75,0.75,0.875,0.875,0.9375,0.8125,0.75,1,1,0.875,0.8125,0.8125,0.5625,0.875,0.8125,0.9375,0.75,0.875,0.875,0.8125,0.75,0.8125,0.75,0.9375,0.875,0.9375,0.8125,0.625,0.8125] relative_absolute_error 0.8036309791859395 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08237054817894275 root_relative_squared_error 0.8278551578346822 total_cost 0 area_under_roc_curve 0.9964453467080647 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.984177,1,0.981132,1,0.996835,0.981013,0.987421,0.987421,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.990506,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,0.949686,0.996835,1,1,1,1,1,1,0.952532,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.946203,1] area_under_roc_curve 0.99557310325611 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.879747,1,1,1,0.984177,0.993671,0.987421,1,0.993711,0.993711,1,1,0.984177,0.968354,1,1,1,1,1,1,0.996835,0.990506,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,1,1,1,1,1,1,0.996835,0.993671,1,1,0.974684,0.990506,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.962025,1,1,1,0.996835,1,0.981132] area_under_roc_curve 0.9964831621686171 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.996835,1,1,0.993671,1,0.939873,1,1,0.993671,1,1,0.943038,0.990506,0.993711,1,1,1,1,1,0.984177,1,1,0.993711,1,0.993711,0.996835,0.996835,0.974843,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.984177,1,1,1,0.987421,1,0.990506,1,1,0.993711,0.987342,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1] area_under_roc_curve 0.9969991441764188 [1,1,1,0.993711,1,0.987342,1,1,0.996835,1,1,1,1,1,1,1,1,0.987342,0.943396,0.981013,1,1,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.977848,0.987342,0.996835,0.987421,1,0.993711,1,1,0.993671,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.990506,1,1,1] area_under_roc_curve 0.9958947834567311 [1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.899371,1,1,1,0.990506,0.987421,0.987342,1,1,1,1,1,0.990506,0.987342,1,1,1,1,1,1,0.984177,1,1,0.996835,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.987342,1,0.993671,1,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.952532,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.96519,0.993711,1,1,0.924528,1] area_under_roc_curve 0.9960057419791417 [0.911392,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.968354,1,1,1,1,0.993671,0.984177,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,1,0.987342,0.996835,1,0.990506,1,1,0.993711,1,1,1,1,0.993671,0.993671,1,1,0.993671,1,0.996835,0.987342,1,1,0.996835,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671] area_under_roc_curve 0.9950237839343998 [0.996835,1,1,1,1,1,1,0.996835,0.993711,1,1,1,0.996835,1,1,1,1,1,1,0.971519,0.996835,1,0.990506,1,1,0.899371,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.968553,1,0.996835,0.996835,1,1,1,1,1,1,0.996835,1,1,0.996835,0.993671,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,0.838608,1] area_under_roc_curve 0.9970421841413899 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.993711,0.993671,0.981013,0.996835,1,0.996835,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.987421,0.962264,1,1,1,1,0.987342,1,1,0.993671,1,1,1,1,1,1,0.981132,0.918239,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.993671] area_under_roc_curve 0.9969979002467955 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.993671,0.981132,0.996835,1,0.974843,0.990506,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.955696,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.981013,0.974684,1,1,1,1,1,0.993671,1,1,1,1,1,0.984177,1,1,1,1,0.993711,0.981013,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,1,0.990506,1,1,1,1] area_under_roc_curve 0.9973534153331742 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,0.962025,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.987342,0.996835,0.993711,0.987421,0.984177,1,1,1,1,1,0.990506,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.993671,1,1,1,1,1,1,1,1,0.993711,1,1,1] 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 0.7726285872182529 kappa 0.7725657427149965 kappa 0.83575489576753 kappa 0.7852179406190777 kappa 0.829397361977727 kappa 0.8041615667074664 kappa 0.8104639684106614 kappa 0.8104415133085854 kappa 0.8041847611527833 kappa 0.7789793582507795 kb_relative_information_score 98.40216605959982 kb_relative_information_score 95.38487012153213 kb_relative_information_score 96.06365905425113 kb_relative_information_score 95.10522974337584 kb_relative_information_score 96.56111089716015 kb_relative_information_score 96.63757157491347 kb_relative_information_score 101.08104852698465 kb_relative_information_score 97.12729678455221 kb_relative_information_score 96.97908214638923 kb_relative_information_score 97.10751834780227 mean_absolute_error 0.015693219880814895 mean_absolute_error 0.016161814339707405 mean_absolute_error 0.016110083281104193 mean_absolute_error 0.01622350229517602 mean_absolute_error 0.016087336374100664 mean_absolute_error 0.015911019095940268 mean_absolute_error 0.015321748055983136 mean_absolute_error 0.015964366349476573 mean_absolute_error 0.01586795053186965 mean_absolute_error 0.015777893674643485 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 0.775 predictive_accuracy 0.775 predictive_accuracy 0.8375 predictive_accuracy 0.7875 predictive_accuracy 0.83125 predictive_accuracy 0.80625 predictive_accuracy 0.8125 predictive_accuracy 0.8125 predictive_accuracy 0.80625 predictive_accuracy 0.78125 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 0.775 [1,0.5,1,0,0.5,1,1,1,1,1,0.5,0.5,1,0,1,0,0.5,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.775 [1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,0.5,0] recall 0.8375 [1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1] recall 0.7875 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0,0,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0,1,1,1,1,0.5,1,1] recall 0.83125 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.80625 [0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5] recall 0.8125 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.8125 [0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5] recall 0.80625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1] recall 0.78125 [1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.7925868626674176 relative_absolute_error 0.8162532494801706 relative_absolute_error 0.8136405697527357 relative_absolute_error 0.8193688027866664 relative_absolute_error 0.8124917360656887 relative_absolute_error 0.803586823027285 relative_absolute_error 0.7738256593930863 relative_absolute_error 0.8062811287614418 relative_absolute_error 0.8014116430237184 relative_absolute_error 0.7968633169011848 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 0.08150715520806978 root_mean_squared_error 0.08325607986441917 root_mean_squared_error 0.08300714501079098 root_mean_squared_error 0.08364848742757307 root_mean_squared_error 0.0828705122547894 root_mean_squared_error 0.08247887194624014 root_mean_squared_error 0.07999231716859494 root_mean_squared_error 0.08229651776833329 root_mean_squared_error 0.08235043669840798 root_mean_squared_error 0.0822402403365912 root_relative_squared_error 0.819177731983119 root_relative_squared_error 0.8367550861398226 root_relative_squared_error 0.8342531967254994 root_relative_squared_error 0.8406989305394564 root_relative_squared_error 0.8328799858596525 root_relative_squared_error 0.8289438526589344 root_relative_squared_error 0.8039530368464657 root_relative_squared_error 0.8271111242132656 root_relative_squared_error 0.8276530298500995 root_relative_squared_error 0.826545514742801 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 1580.9650230000045 usercpu_time_millis 1369.9782690000006 usercpu_time_millis 1366.9189810000048 usercpu_time_millis 1367.860892000003 usercpu_time_millis 1388.1270689999994 usercpu_time_millis 1376.0433670000011 usercpu_time_millis 1381.0159070000054 usercpu_time_millis 1454.0984300000018 usercpu_time_millis 1381.9803880000022 usercpu_time_millis 1381.7794340000019 usercpu_time_millis_testing 134.03645700000055 usercpu_time_millis_testing 132.78192399999966 usercpu_time_millis_testing 132.64321500000165 usercpu_time_millis_testing 132.33547699999804 usercpu_time_millis_testing 140.79004399999917 usercpu_time_millis_testing 133.5063360000035 usercpu_time_millis_testing 134.12684300000421 usercpu_time_millis_testing 132.94725600000135 usercpu_time_millis_testing 132.80822000000114 usercpu_time_millis_testing 134.47893900000452 usercpu_time_millis_training 1446.928566000004 usercpu_time_millis_training 1237.196345000001 usercpu_time_millis_training 1234.2757660000032 usercpu_time_millis_training 1235.525415000005 usercpu_time_millis_training 1247.3370250000003 usercpu_time_millis_training 1242.5370309999976 usercpu_time_millis_training 1246.8890640000013 usercpu_time_millis_training 1321.1511740000005 usercpu_time_millis_training 1249.1721680000012 usercpu_time_millis_training 1247.3004949999975