8978800 1 Jan van Rijn 9954 Supervised Classification 8330 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)(2) 6914381 axis 0 7104 categorical_features [] 7104 copy true 7104 fill_empty 0 7104 missing_values "NaN" 7104 strategy "most_frequent" 7104 strategy_nominal "most_frequent" 7104 verbose 0 7104 categorical_features [] 7105 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7105 handle_unknown "ignore" 7105 n_values "auto" 7105 sparse true 7105 threshold 0.0 7106 C 752.5256692420229 7107 cache_size 200 7107 class_weight null 7107 coef0 -0.0950558313714962 7107 decision_function_shape null 7107 degree 2 7107 gamma 0.006833588566949055 7107 kernel "sigmoid" 7107 max_iter -1 7107 probability true 7107 random_state 32584 7107 shrinking false 7107 tol 0.06830658569213303 7107 verbose false 7107 copy true 7713 with_mean false 7713 with_std true 7713 openml-pimp openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18872667 description https://api.openml.org/data/download/18872667/description.xml -1 18872668 predictions https://api.openml.org/data/download/18872668/predictions.arff area_under_roc_curve 0.9964768781565654 [0.98907,0.999882,1,0.999961,0.997988,0.994871,0.999842,0.999566,0.997909,0.999961,0.998343,0.999605,0.999684,0.98544,0.998461,0.999763,1,0.990688,0.984178,0.97455,0.99854,0.999448,0.997356,1,0.998146,0.974392,0.992937,0.999329,0.998816,1,0.999921,1,1,0.98986,0.999605,0.999132,0.99345,0.981297,0.999921,1,0.999369,0.999645,1,0.998935,0.997238,0.999803,0.999211,0.995739,0.997396,0.996291,0.994318,0.999171,0.998303,0.990096,0.997041,0.99203,1,0.999645,0.992464,0.996252,0.999171,0.999369,0.997435,0.99925,0.999448,0.99562,0.987926,1,0.998422,0.993845,0.995975,0.999566,0.996567,0.998264,0.98761,0.999605,0.999605,0.990688,1,1,0.99416,0.998146,0.997711,0.99566,0.99929,0.999763,1,0.99929,0.999605,1,0.999092,0.999014,0.998974,0.997317,0.997475,0.997435,0.997396,0.998264,0.967251,0.998619] average_cost 0 f_measure 0.8264382033949981 [0.742857,0.9375,1,1,0.933333,0.827586,0.969697,0.967742,0.875,0.967742,0.774194,0.909091,0.909091,0.709677,0.914286,0.933333,1,0.533333,0.4,0.242424,0.8125,0.967742,0.888889,1,0.866667,0.424242,0.5625,0.848485,0.933333,0.896552,0.967742,1,1,0.5,0.903226,0.848485,0.529412,0.4375,0.909091,1,0.8,0.969697,1,0.875,0.785714,0.9375,0.83871,0.666667,0.764706,0.8125,0.75,0.827586,0.866667,0.705882,0.689655,0.625,1,0.9375,0.684211,0.764706,0.933333,0.848485,0.75,0.909091,0.909091,0.742857,0.580645,1,0.866667,0.685714,0.787879,0.896552,0.645161,0.967742,0.8,0.909091,0.9375,0.628571,1,1,0.774194,0.875,0.903226,0.482759,0.848485,0.967742,0.933333,0.875,0.967742,0.967742,0.823529,0.827586,0.909091,0.866667,0.9375,0.777778,0.8,0.742857,0.482759,0.9375] kappa 0.8238636363636365 kb_relative_information_score 976.4353945316408 mean_absolute_error 0.016274931745061737 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 0.8325243233718463 [0.684211,0.9375,1,1,1,0.923077,0.941176,1,0.875,1,0.8,0.882353,0.882353,0.733333,0.842105,1,1,0.571429,0.368421,0.235294,0.8125,1,0.8,1,0.928571,0.411765,0.5625,0.823529,1,1,1,1,1,0.583333,0.933333,0.823529,0.5,0.4375,0.882353,1,0.736842,0.941176,1,0.875,0.916667,0.9375,0.866667,0.714286,0.722222,0.8125,0.75,0.923077,0.928571,0.666667,0.769231,0.625,1,0.9375,0.590909,0.722222,1,0.823529,0.75,0.882353,0.882353,0.684211,0.6,1,0.928571,0.631579,0.764706,1,0.666667,1,0.736842,0.882353,0.9375,0.578947,1,1,0.8,0.875,0.933333,0.538462,0.823529,1,1,0.875,1,1,0.777778,0.923077,0.882353,0.928571,0.9375,0.7,0.736842,0.684211,0.538462,0.9375] predictive_accuracy 0.825625 prior_entropy 6.6438561897747395 recall 0.825625 [0.8125,0.9375,1,1,0.875,0.75,1,0.9375,0.875,0.9375,0.75,0.9375,0.9375,0.6875,1,0.875,1,0.5,0.4375,0.25,0.8125,0.9375,1,1,0.8125,0.4375,0.5625,0.875,0.875,0.8125,0.9375,1,1,0.4375,0.875,0.875,0.5625,0.4375,0.9375,1,0.875,1,1,0.875,0.6875,0.9375,0.8125,0.625,0.8125,0.8125,0.75,0.75,0.8125,0.75,0.625,0.625,1,0.9375,0.8125,0.8125,0.875,0.875,0.75,0.9375,0.9375,0.8125,0.5625,1,0.8125,0.75,0.8125,0.8125,0.625,0.9375,0.875,0.9375,0.9375,0.6875,1,1,0.75,0.875,0.875,0.4375,0.875,0.9375,0.875,0.875,0.9375,0.9375,0.875,0.75,0.9375,0.8125,0.9375,0.875,0.875,0.8125,0.4375,0.9375] relative_absolute_error 0.8219662497505914 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08331967259275316 root_relative_squared_error 0.8373942171073341 total_cost 0 area_under_roc_curve 0.9964040482445663 [0.993711,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,0.993711,1,0.990506,0.984177,0.987421,0.993711,1,1,1,1,0.996835,0.996835,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.968553,0.993671,1,1,1,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987342,0.987421,0.996835,0.924051,1] area_under_roc_curve 0.9966008379109944 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.917722,1,1,1,0.981013,0.993671,1,1,1,0.993711,1,1,0.974684,0.974684,1,1,1,1,1,1,0.996835,1,1,0.987421,0.993711,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,1,1,1,1,0.993671,1,1,0.974684,0.996835,1,0.987421,0.996835,1,1,0.996835,1,1,1,1,1,0.993671,1,1,1,0.996835,1,0.974843] area_under_roc_curve 0.9968769902873973 [1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993671,0.990506,1,1,0.993671,1,0.943038,1,1,0.996835,1,1,0.933544,1,1,1,1,1,1,1,0.984177,1,1,0.993711,1,1,1,0.996835,0.993711,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,0.996835,1,1,0.981132,0.984177,1,1,1,0.987421,1,0.996835,1,1,1,0.984177,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1] area_under_roc_curve 0.9967219966563171 [1,1,1,1,0.996835,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.987342,0.943396,0.968354,1,1,1,1,0.993711,0.984177,1,1,1,1,1,1,1,0.993671,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.981013,1,1,0.993711,1,1,1,1,1,1,0.996835,0.987342,1,1,0.981013,0.971519,1,0.981132,1,0.993711,1,1,0.996835,1,1,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,1,1,1] area_under_roc_curve 0.9964453467080647 [1,1,1,1,1,0.993671,1,1,1,1,0.993671,1,1,0.974843,0.993711,1,1,0.990506,0.987421,0.990506,1,1,1,1,0.996835,0.993671,0.993671,1,1,1,1,1,1,0.977848,1,1,0.990506,0.984177,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.987421,1,1,0.990506,0.996835,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.946203,1,0.993671,1,1,1,1,1,1,0.987342,0.993711,0.993711,1,1,1,1,1,0.996835,1,1,0.974684,0.993711,1,1,0.949686,1] area_under_roc_curve 0.9957300871745881 [0.920886,1,1,1,1,0.984177,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.955696,1,1,1,1,0.993671,0.987342,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.96519,1,1,0.996835,1,1,1,1,1,1,0.996835,0.993671,0.996835,1,1,1,1,1,0.990506,1,1,0.993671,1,0.990506,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.987342,1,1,1,1,1,0.987421,0.993671,1,0.993671,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.968553,1] area_under_roc_curve 0.995340737202452 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993711,0.993671,0.968354,1,1,0.990506,1,1,0.880503,0.974843,0.993671,1,1,0.996835,1,1,0.987421,1,1,0.977848,0.993671,1,1,1,1,1,1,0.974843,0.996835,1,0.990506,1,1,0.993711,1,1,1,1,1,1,1,0.987342,1,1,1,1,0.996835,1,0.993711,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.968354,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,0.89557,1] area_under_roc_curve 0.9976337970702968 [0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.987342,1,1,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.987342,0.993671,1,1,1,1,1,1,1,1,1,0.990506,1,0.987421,0.955975,1,0.996835,1,1,0.984177,1,1,0.993671,1,1,1,1,1,1,0.974843,0.955975,1,1,0.996835,1,1,1,1,1,1,1,0.981132,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1] area_under_roc_curve 0.9971178150624948 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.990506,0.955975,1,1,0.993711,1,1,0.987421,0.993711,1,1,1,1,1,1,0.993711,1,1,1,0.943038,1,1,1,1,1,0.993671,1,1,1,0.993671,0.993711,1,0.990506,1,1,0.958861,0.996835,0.981132,1,1,1,1,1,1,1,1,1,1,0.984177,1,0.996835,1,1,0.993711,0.990506,1,1,1,1,0.993711,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] area_under_roc_curve 0.9976300652814265 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.971519,0.987421,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,0.996835,1,1,1,0.987342,1,1,1,0.981013,1,0.993711,0.993711,0.993671,1,1,1,1,1,0.993671,0.990506,1,0.993671,1,1,1,1,1,0.968553,1,0.993711,1,1,1,0.993671,1,1,0.993671,1,1,0.987342,1,1,0.993711,0.990506,1,1] 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 0.7978681405448086 kappa 0.842040832444813 kappa 0.8862873613140126 kappa 0.8168252339031227 kappa 0.8041615667074664 kappa 0.797876120168963 kappa 0.8104789355233545 kappa 0.8293906243829233 kappa 0.8610233733417562 kappa 0.7916008841174613 kb_relative_information_score 97.82616061210243 kb_relative_information_score 96.94716490831644 kb_relative_information_score 98.00717710222014 kb_relative_information_score 96.88617222986475 kb_relative_information_score 96.77070664486537 kb_relative_information_score 95.89837464925382 kb_relative_information_score 99.50168866790281 kb_relative_information_score 97.78579877919182 kb_relative_information_score 98.46463459685205 kb_relative_information_score 98.34751634107117 mean_absolute_error 0.016171039605225465 mean_absolute_error 0.01636799770006529 mean_absolute_error 0.016273133400800548 mean_absolute_error 0.016371552803902602 mean_absolute_error 0.016397638289088648 mean_absolute_error 0.016451173378614858 mean_absolute_error 0.016051916206766628 mean_absolute_error 0.016240043099385926 mean_absolute_error 0.01620006835208831 mean_absolute_error 0.01622475461467917 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 0.8 predictive_accuracy 0.84375 predictive_accuracy 0.8875 predictive_accuracy 0.81875 predictive_accuracy 0.80625 predictive_accuracy 0.8 predictive_accuracy 0.8125 predictive_accuracy 0.83125 predictive_accuracy 0.8625 predictive_accuracy 0.79375 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 0.8 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0,1] recall 0.84375 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,0] recall 0.8875 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1] recall 0.81875 [0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,0,1,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1] recall 0.80625 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,0,0,1,1,1,1,1,0,0,1,1,0.5,1,1,0,0,0.5,1,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.8 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1] recall 0.8125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,0.5,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,0,0,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.83125 [0.5,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1] recall 0.8625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1] recall 0.79375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.8167191719810828 relative_absolute_error 0.8266665505083465 relative_absolute_error 0.8218754242828545 relative_absolute_error 0.8268461012072008 relative_absolute_error 0.8281635499539708 relative_absolute_error 0.8308673423542844 relative_absolute_error 0.8107028387255859 relative_absolute_error 0.8202041969386817 relative_absolute_error 0.8181852703074891 relative_absolute_error 0.8194320512464214 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 0.08290362636116666 root_mean_squared_error 0.08365044030823199 root_mean_squared_error 0.08325792569557705 root_mean_squared_error 0.08378227044139049 root_mean_squared_error 0.08393468762971665 root_mean_squared_error 0.08421048352320779 root_mean_squared_error 0.08227035694920848 root_mean_squared_error 0.08312805771417134 root_mean_squared_error 0.08289336617855998 root_mean_squared_error 0.0831473803531661 root_relative_squared_error 0.8332127951509289 root_relative_squared_error 0.8407185577285656 root_relative_squared_error 0.8367736374409652 root_relative_squared_error 0.8420435004187151 root_relative_squared_error 0.8435753507983478 root_relative_squared_error 0.8463472038208657 root_relative_squared_error 0.8268481980882797 root_relative_squared_error 0.8354684151179239 root_relative_squared_error 0.8331096764358173 root_relative_squared_error 0.8356626149467274 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 1609.8183789999894 usercpu_time_millis 1456.444936999958 usercpu_time_millis 1449.5724689999747 usercpu_time_millis 1453.1645819999994 usercpu_time_millis 1460.173568000016 usercpu_time_millis 1477.4666669999874 usercpu_time_millis 1513.2636220000109 usercpu_time_millis 1483.7443970000095 usercpu_time_millis 1466.2144079999848 usercpu_time_millis 1455.5525959999613 usercpu_time_millis_testing 172.95942100003003 usercpu_time_millis_testing 165.0268980000078 usercpu_time_millis_testing 166.3614349999989 usercpu_time_millis_testing 163.9652910000109 usercpu_time_millis_testing 170.1335910000239 usercpu_time_millis_testing 171.21004299997367 usercpu_time_millis_testing 170.72124700001723 usercpu_time_millis_testing 164.88537300000417 usercpu_time_millis_testing 170.8837909999943 usercpu_time_millis_testing 165.1152169999932 usercpu_time_millis_training 1436.8589579999593 usercpu_time_millis_training 1291.41803899995 usercpu_time_millis_training 1283.2110339999758 usercpu_time_millis_training 1289.1992909999885 usercpu_time_millis_training 1290.0399769999922 usercpu_time_millis_training 1306.2566240000137 usercpu_time_millis_training 1342.5423749999936 usercpu_time_millis_training 1318.8590240000053 usercpu_time_millis_training 1295.3306169999905 usercpu_time_millis_training 1290.4373789999681