5963754 1 Jan van Rijn 9954 Supervised Classification 6970 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1) 3998488 class_weight null 6941 criterion "gini" 6941 max_depth 9 6941 max_features null 6941 max_leaf_nodes null 6941 min_impurity_split 1e-07 6941 min_samples_leaf 1 6941 min_samples_split 2 6941 min_weight_fraction_leaf 0.0 6941 presort false 6941 random_state 49853 6941 splitter "best" 6941 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "mean" 6947 strategy_nominal "most_frequent" 6947 verbose 0 6947 categorical_features [] 6948 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 6948 handle_unknown "ignore" 6948 n_values "auto" 6948 sparse false 6948 threshold 0.0 6949 algorithm "SAMME" 6971 learning_rate 1.9813158925528012 6971 n_estimators 376 6971 random_state 2080 6971 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 12850909 description https://api.openml.org/data/download/12850909/description.xml -1 12850910 predictions https://api.openml.org/data/download/12850910/predictions.arff area_under_roc_curve 0.9925118371212127 [0.974905,0.999803,1,1,0.997277,0.996054,0.999014,0.998935,0.996804,1,0.997672,0.999566,0.99708,0.96658,1,0.997514,1,0.979916,0.973919,0.952612,0.993963,0.999053,0.995028,1,0.996291,0.966225,0.979048,0.996843,0.998501,0.999842,0.999882,1,0.999132,0.987887,0.998027,0.998658,0.991083,0.975931,0.999882,0.999921,0.994358,0.997633,1,0.993805,0.997238,0.99925,0.999448,0.999724,0.995699,0.99274,0.98473,0.995384,0.991241,0.969658,0.992266,0.973958,1,0.999487,0.992109,0.977825,0.998816,0.998935,0.992148,0.997593,0.999803,0.984651,0.970565,1,0.987058,0.9869,0.984414,0.998185,0.990412,0.999961,0.976484,0.998422,0.999566,0.985519,0.999921,0.999882,0.988952,0.996212,0.992464,0.995423,0.995265,0.999448,1,0.996252,0.995936,0.999803,0.998422,0.999645,0.996094,0.994476,0.983744,0.998224,0.985322,0.99203,0.946023,0.998816] average_cost 0 f_measure 0.7941249661499039 [0.540541,0.848485,1,1,0.827586,0.785714,0.933333,0.967742,0.848485,0.933333,0.8,0.903226,0.875,0.740741,1,0.9375,0.933333,0.428571,0.32,0.071429,0.827586,0.967742,0.787879,1,0.875,0.5625,0.3125,0.833333,0.903226,0.933333,0.967742,1,0.9375,0.5,0.714286,0.83871,0.533333,0.322581,0.914286,0.896552,0.875,0.764706,0.933333,0.774194,0.9375,0.714286,0.896552,0.9375,0.810811,0.827586,0.8125,0.733333,0.8,0.758621,0.727273,0.410256,1,0.969697,0.742857,0.666667,0.9375,0.875,0.645161,0.882353,0.9375,0.666667,0.434783,0.967742,0.75,0.631579,0.685714,0.785714,0.625,0.967742,0.685714,0.875,0.933333,0.564103,0.967742,0.941176,0.875,0.774194,0.666667,0.705882,0.909091,0.83871,0.933333,0.733333,0.769231,0.9375,0.896552,0.903226,0.714286,0.857143,0.967742,0.83871,0.848485,0.705882,0.421053,0.941176] kappa 0.7891414141414141 kb_relative_information_score 0.2806865708773306 mean_absolute_error 0.019799838344716863 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 0.8077474419024085 [0.47619,0.823529,1,1,0.923077,0.916667,1,1,0.823529,1,0.857143,0.933333,0.875,0.909091,1,0.9375,1,0.5,0.444444,0.083333,0.923077,1,0.764706,1,0.875,0.5625,0.3125,0.75,0.933333,1,1,1,0.9375,0.5,0.833333,0.866667,0.571429,0.333333,0.842105,1,0.875,0.722222,1,0.8,0.9375,0.576923,1,0.9375,0.714286,0.923077,0.8125,0.785714,0.857143,0.846154,0.705882,0.347826,1,0.941176,0.684211,0.647059,0.9375,0.875,0.666667,0.833333,0.9375,0.6,0.333333,1,0.75,0.545455,0.631579,0.916667,0.625,1,0.631579,0.875,1,0.478261,1,0.888889,0.875,0.8,0.647059,0.666667,0.882353,0.866667,1,0.785714,1,0.9375,1,0.933333,0.833333,0.789474,1,0.866667,0.823529,0.666667,0.363636,0.888889] predictive_accuracy 0.79125 prior_entropy 6.6438561897747395 recall 0.79125 [0.625,0.875,1,1,0.75,0.6875,0.875,0.9375,0.875,0.875,0.75,0.875,0.875,0.625,1,0.9375,0.875,0.375,0.25,0.0625,0.75,0.9375,0.8125,1,0.875,0.5625,0.3125,0.9375,0.875,0.875,0.9375,1,0.9375,0.5,0.625,0.8125,0.5,0.3125,1,0.8125,0.875,0.8125,0.875,0.75,0.9375,0.9375,0.8125,0.9375,0.9375,0.75,0.8125,0.6875,0.75,0.6875,0.75,0.5,1,1,0.8125,0.6875,0.9375,0.875,0.625,0.9375,0.9375,0.75,0.625,0.9375,0.75,0.75,0.75,0.6875,0.625,0.9375,0.75,0.875,0.875,0.6875,0.9375,1,0.875,0.75,0.6875,0.75,0.9375,0.8125,0.875,0.6875,0.625,0.9375,0.8125,0.875,0.625,0.9375,0.9375,0.8125,0.875,0.75,0.5,1] relative_absolute_error 0.9999918355917589 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.09949793136795444 root_relative_squared_error 0.9999918356485986 total_cost 0 area_under_roc_curve 0.9923396325929463 [0.949686,1,1,1,1,0.993711,1,1,1,1,0.996835,0.996835,0.993671,0.981013,1,0.962264,1,0.946203,0.987342,0.955975,0.949686,1,1,1,0.993711,1,0.968354,0.993671,1,1,1,1,0.996835,0.977848,1,0.993711,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.968354,1,1,1,0.993671,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.993671,0.96519,1,1,0.880503,0.977848,1,1,1,1,1,1,0.927215,1,1,1,1,0.987421,1,0.996835,0.990506,1,0.996835,0.984177,1,1,1,1,0.993671,0.996835,0.993671,1,0.990506,0.962025,1] area_under_roc_curve 0.9929645828357614 [1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.841772,1,1,1,0.96519,0.987342,0.955975,1,0.993711,1,1,1,0.962025,0.981013,1,1,1,1,1,1,1,0.993671,0.993711,0.993711,0.974843,1,1,1,1,1,0.993671,1,1,1,1,0.996835,0.996835,1,0.984177,1,1,1,0.968553,1,1,1,0.952532,1,1,1,0.996835,1,0.990506,0.993671,1,1,1,0.993671,1,1,1,1,1,0.996835,0.993671,1,1,0.990506,0.990506,1,1,0.993671,1,1,1,0.984177,1,0.996835,1,0.996835,0.987342,1,1,0.981132,0.990506,0.962025,0.993711] area_under_roc_curve 0.9948277406257462 [0.977848,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,0.981132,0.93038,1,1,1,1,1,0.955696,0.974684,0.981132,1,1,1,1,1,0.977848,1,1,0.993711,0.993711,1,1,1,0.981132,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,0.981132,0.987342,1,1,1,0.968553,1,0.993671,0.996835,1,0.937107,0.993671,0.996835,0.996835,1,1,0.987421,1,1,0.987342,1,1,1,1,1,0.996835,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,0.993671,0.949686,1] area_under_roc_curve 0.9917442878751691 [0.958861,1,1,1,0.996835,0.984177,1,1,0.993671,1,1,1,0.993711,1,1,1,1,0.996835,0.81761,0.939873,0.993711,1,0.990506,1,0.987421,0.968354,0.987342,1,1,1,1,1,1,0.990506,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.89557,1,0.990506,1,1,1,0.984177,1,1,0.996835,1,1,0.990506,0.968354,1,1,0.987342,0.943038,0.996835,0.968553,1,1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,1,1,1,1,0.981132,1,1,1,0.949367,0.984177,1,1] area_under_roc_curve 0.9927700322426558 [1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,0.981132,0.955975,1,1,1,0.993671,0.981132,0.958861,1,1,1,1,1,0.987342,0.955696,1,1,1,1,1,1,0.968354,0.993711,1,0.996835,0.93038,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,0.984177,1,0.996835,1,1,1,1,0.987421,1,1,0.996835,0.993711,1,0.987342,0.996835,0.996835,1,1,1,0.949367,1,1,0.977848,1,1,1,1,1,0.990506,0.987421,1,1,0.987421,1,1,1,0.996835,1,0.987421,0.89557,0.993711,1,1,0.943396,1] area_under_roc_curve 0.9934007045617385 [0.939873,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,0.993711,1,1,1,1,1,0.911392,1,1,0.996835,1,0.993671,0.958861,0.987342,1,1,1,1,1,1,0.993671,1,0.993671,0.984177,0.971519,1,1,0.974684,0.996835,1,1,0.993711,1,1,1,0.993671,0.981013,1,1,1,1,1,0.971519,1,1,0.993671,0.993711,1,1,0.987421,1,1,0.977848,1,1,0.984177,0.990506,0.993671,1,1,1,0.987342,1,1,0.990506,1,1,0.993711,1,1,0.993671,1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.886792,1] area_under_roc_curve 0.9910402237083034 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,0.943038,0.993671,1,0.981013,1,0.996835,0.874214,1,0.993671,1,1,0.996835,1,1,0.993711,1,1,0.984177,0.971519,1,1,1,0.993671,1,1,0.968553,0.996835,1,1,1,1,0.874214,1,1,1,0.990506,0.987342,1,1,1,1,1,1,1,1,1,0.943396,0.886792,1,1,1,1,1,1,1,0.993671,1,1,0.993711,1,1,0.949367,1,1,1,1,1,1,0.993711,1,1,1,1,0.987342,1,1,1,0.977848,0.974843,0.816456,1] area_under_roc_curve 0.9937268629090037 [0.990506,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.987421,0.981013,0.993671,1,1,1,1,0.987342,0.993711,0.949686,1,0.981132,1,1,1,1,1,0.990506,1,0.977848,0.977848,1,1,1,1,1,1,1,0.996835,1,0.996835,0.981132,0.981132,0.968553,0.996835,0.984177,0.993711,0.981013,0.952532,1,1,0.996835,0.91195,1,1,1,1,1,0.962264,0.880503,1,0.993671,0.996835,1,1,0.996835,1,0.993671,1,1,1,1,1,0.984177,1,0.971519,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.971519,1] area_under_roc_curve 0.9952646087094974 [0.987421,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.968553,0.996835,0.924528,1,1,0.955975,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,0.993711,1,1,0.968354,1,1,1,1,0.993711,0.993711,0.987342,0.996835,1,0.962025,0.96519,0.924528,1,1,0.993671,0.984177,1,1,1,0.996835,1,1,0.987342,1,0.996835,0.993711,1,0.993711,0.968354,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,0.993671,1] area_under_roc_curve 0.9952596329910038 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,1,0.990506,0.968553,1,0.996835,1,1,1,0.987421,0.974843,1,0.993671,1,1,1,1,1,1,1,0.990506,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,0.993671,0.936709,1,1,1,1,0.987342,0.993671,1,0.996835,0.977848,1,1,0.993711,0.987342,1,1,0.993711,1,1,0.993671,1,0.936709,0.993671,1,1,1,1,1,0.974843,1,0.987421,0.996835,1,1,1,1,1,1,1,1,0.990506,1,0.996835,0.955975,0.996835,0.993671,1] 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 0.7409981048641819 kappa 0.7915762049500651 kappa 0.8230997038499506 kappa 0.78520097923083 kappa 0.8483472216737096 kappa 0.7663034896573504 kappa 0.7725387987205308 kappa 0.7725477807613331 kappa 0.7662758103359786 kappa 0.8230927183699257 kb_relative_information_score 0.028538747076459398 kb_relative_information_score 0.027197550712811814 kb_relative_information_score 0.028145160361501796 kb_relative_information_score 0.027693278600838447 kb_relative_information_score 0.028819607855404442 kb_relative_information_score 0.027800304310235658 kb_relative_information_score 0.028967861001155528 kb_relative_information_score 0.02675085118601815 kb_relative_information_score 0.028571488229792625 kb_relative_information_score 0.028201721543112738 mean_absolute_error 0.019799835633307925 mean_absolute_error 0.019799843363081104 mean_absolute_error 0.019799837904437696 mean_absolute_error 0.019799840507992748 mean_absolute_error 0.019799834018258716 mean_absolute_error 0.01979983989149993 mean_absolute_error 0.01979983316402431 mean_absolute_error 0.0197998459368265 mean_absolute_error 0.019799835447791514 mean_absolute_error 0.019799837579947955 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 0.74375 predictive_accuracy 0.79375 predictive_accuracy 0.825 predictive_accuracy 0.7875 predictive_accuracy 0.85 predictive_accuracy 0.76875 predictive_accuracy 0.775 predictive_accuracy 0.775 predictive_accuracy 0.76875 predictive_accuracy 0.825 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 0.74375 [0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,0,0.5,0,0,0,0,1,1,1,1,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,0,1,1,0,0.5,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,0.5,1,1,1,0.5,0.5,1,1,1,1,0,1,1,0.5,0.5,0.5,0,1,1,1,1,1,1,0.5,1,1,0.5,1] recall 0.79375 [1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,0,0.5,1,0.5,1,0.5,0,1] recall 0.825 [0.5,1,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,1,0.5,0.5,0,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,0,1] recall 0.7875 [0.5,1,1,1,0.5,0,1,1,0.5,0.5,1,1,0,0.5,1,1,1,0,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,0.5,1,1,1,0,0.5,0,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1] recall 0.85 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,0.5,1,1,1,1,1] recall 0.76875 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0.5,0.5,1,0.5,0,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1] recall 0.775 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,1,0.5,1,1,0,0,1,1,1,0.5,1,1,1,0.5,0.5,0.5,0,1,0,1,0,1,1,0,0.5,0.5,1,1,1,0,1,1,1,0.5,0.5,1,1,0.5,0,1,1,1,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0,1,1,1,0,1,1,1,1,0,0,1] recall 0.775 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0,1,1,1,0.5,1,0,1,0,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,0.5,1,1,1,1,1,0,0,0.5,0,1,0.5,0.5,1,1,1,0,1,0.5,1,1,1,0,0,1,1,1,1,0.5,0.5,1,0.5,1,1,0,1,1,1,1,0,1,1,0.5,1,0,0,1,1,1,1,1,1,1,0.5,1,1,1] recall 0.76875 [1,0.5,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0,1,0,0,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0.5,0.5,0,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0,1,1,1,0,0,1,1,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1] recall 0.825 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0,0,1,0.5,0,1,1,1,0,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,0,1,0,0.5,0.5,0.5,1,1,1,1,1,1,0,1,0,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1] relative_absolute_error 0.9999916986519137 relative_absolute_error 0.9999920890444985 relative_absolute_error 0.9999918133554375 relative_absolute_error 0.9999919448481169 relative_absolute_error 0.9999916170837718 relative_absolute_error 0.9999919137121159 relative_absolute_error 0.9999915739406201 relative_absolute_error 0.9999922190316397 relative_absolute_error 0.999991689282398 relative_absolute_error 0.9999917969670667 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 0.09949791774273303 root_mean_squared_error 0.09949795658602979 root_mean_squared_error 0.09949792915527221 root_mean_squared_error 0.09949794223891681 root_mean_squared_error 0.09949790962667487 root_mean_squared_error 0.09949793914092671 root_mean_squared_error 0.09949790533419231 root_mean_squared_error 0.09949796951951513 root_mean_squared_error 0.09949791681045446 root_mean_squared_error 0.09949792752480974 root_relative_squared_error 0.9999916987099714 root_relative_squared_error 0.9999920890997926 root_relative_squared_error 0.9999918134103057 root_relative_squared_error 0.9999919449058816 root_relative_squared_error 0.9999916171405178 root_relative_squared_error 0.9999919137699096 root_relative_squared_error 0.9999915739994448 root_relative_squared_error 0.9999922190862109 root_relative_squared_error 0.9999916893402191 root_relative_squared_error 0.9999917970235415 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 12449.118618 usercpu_time_millis 12493.914987 usercpu_time_millis 12483.805630000003 usercpu_time_millis 12457.054198000002 usercpu_time_millis 12508.201932999988 usercpu_time_millis 12455.473888 usercpu_time_millis 12440.479230999985 usercpu_time_millis 12352.855793999992 usercpu_time_millis 12396.689464000005 usercpu_time_millis 12358.664188999996 usercpu_time_millis_testing 106.5747169999991 usercpu_time_millis_testing 106.32696100000061 usercpu_time_millis_testing 106.45267400000336 usercpu_time_millis_testing 103.85877000000221 usercpu_time_millis_testing 104.82001699999444 usercpu_time_millis_testing 105.83651800000382 usercpu_time_millis_testing 103.15017399999249 usercpu_time_millis_testing 102.60749799999758 usercpu_time_millis_testing 102.37033399999973 usercpu_time_millis_testing 104.40292399999862 usercpu_time_millis_training 12342.543901000001 usercpu_time_millis_training 12387.588026 usercpu_time_millis_training 12377.352955999999 usercpu_time_millis_training 12353.195427999999 usercpu_time_millis_training 12403.381915999993 usercpu_time_millis_training 12349.637369999997 usercpu_time_millis_training 12337.329056999992 usercpu_time_millis_training 12250.248295999994 usercpu_time_millis_training 12294.319130000005 usercpu_time_millis_training 12254.261264999997