4862713 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) 2898122 class_weight null 6941 criterion "gini" 6941 max_depth 8 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 41375 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.R" 6971 learning_rate 0.7501051034777056 6971 n_estimators 57 6971 random_state 44791 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 10659631 description https://api.openml.org/data/download/10659631/description.xml -1 10659632 predictions https://api.openml.org/data/download/10659632/predictions.arff area_under_roc_curve 0.9774206912878789 [0.953086,0.989662,0.995502,1,0.975063,0.988242,0.996686,0.997475,0.993529,0.999842,0.986703,0.996567,0.984099,0.950797,0.999763,0.967487,0.99858,0.955611,0.947759,0.954072,0.978654,0.983507,0.978575,0.999566,0.988952,0.963463,0.951586,0.977983,0.95423,0.999487,0.996922,1,0.986979,0.986111,0.971867,0.99925,0.969066,0.950284,0.998935,0.998224,0.988952,0.991556,0.99925,0.957347,0.997633,0.98327,0.996054,0.992543,0.978022,0.940183,0.959261,0.966619,0.982323,0.926669,0.961253,0.947522,1,0.980745,0.990372,0.962516,0.991359,0.951468,0.992464,0.988005,0.994831,0.974747,0.955926,0.998501,0.925229,0.94693,0.95715,0.998501,0.969263,0.99708,0.95202,0.977786,0.988597,0.926531,0.997869,0.999132,0.971473,0.993529,0.983625,0.993095,0.939493,0.986348,0.999961,0.960878,0.984059,0.988005,0.98331,0.999487,0.977904,0.966185,0.980548,0.986348,0.945825,0.984848,0.887784,0.979719] average_cost 0 f_measure 0.5163994979978525 [0.25,0.5,0.666667,0.896552,0.47619,0.518519,0.692308,0.787879,0.6,0.857143,0.56,0.769231,0.625,0.482759,0.857143,0.408163,0.608696,0.060606,0.137931,0.146341,0.380952,0.645161,0.4375,0.857143,0.592593,0.294118,0.171429,0.325581,0.785714,0.857143,0.769231,0.857143,0.486486,0.173913,0.529412,0.583333,0.347826,0.285714,0.814815,0.666667,0.439024,0.588235,0.814815,0.344828,0.8,0.382979,0.740741,0.583333,0.5625,0.375,0.255319,0.551724,0.555556,0.125,0.571429,0.206897,1,0.62069,0.470588,0.166667,0.5,0.571429,0.551724,0.48,0.666667,0.357143,0.054054,0.774194,0.35,0.179104,0.27907,0.774194,0.413793,0.866667,0.482759,0.466667,0.608696,0.202899,0.785714,0.769231,0.457143,0.642857,0.411765,0.551724,0.324324,0.705882,0.933333,0.173913,0.611111,0.518519,0.333333,0.866667,0.482759,0.324324,0.545455,0.6,0.315789,0.451613,0.040816,0.296296] kappa 0.48674242424242425 kb_relative_information_score 952.51732853629 mean_absolute_error 0.014172378339104825 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 0.5870008504535883 [0.175,0.583333,1,1,1,0.636364,0.9,0.764706,0.642857,1,0.777778,1,0.625,0.538462,1,0.30303,1,0.058824,0.153846,0.12,0.8,0.666667,0.4375,1,0.727273,0.277778,0.157895,0.259259,0.916667,1,1,1,0.428571,0.285714,0.5,0.875,0.571429,0.263158,1,1,0.36,0.555556,1,0.384615,0.857143,0.290323,0.909091,0.875,0.5625,0.375,0.193548,0.615385,0.5,0.125,0.526316,0.230769,1,0.692308,0.444444,0.15,0.75,0.526316,0.615385,0.666667,0.818182,0.416667,0.047619,0.8,0.291667,0.117647,0.222222,0.8,0.461538,0.928571,0.538462,0.5,1,0.132075,0.916667,1,0.421053,0.75,0.388889,0.615385,0.285714,0.666667,1,0.285714,0.55,0.636364,0.5,0.928571,0.538462,0.285714,0.428571,0.642857,0.272727,0.466667,0.030303,0.363636] predictive_accuracy 0.491875 prior_entropy 6.6438561897747395 recall 0.491875 [0.4375,0.4375,0.5,0.8125,0.3125,0.4375,0.5625,0.8125,0.5625,0.75,0.4375,0.625,0.625,0.4375,0.75,0.625,0.4375,0.0625,0.125,0.1875,0.25,0.625,0.4375,0.75,0.5,0.3125,0.1875,0.4375,0.6875,0.75,0.625,0.75,0.5625,0.125,0.5625,0.4375,0.25,0.3125,0.6875,0.5,0.5625,0.625,0.6875,0.3125,0.75,0.5625,0.625,0.4375,0.5625,0.375,0.375,0.5,0.625,0.125,0.625,0.1875,1,0.5625,0.5,0.1875,0.375,0.625,0.5,0.375,0.5625,0.3125,0.0625,0.75,0.4375,0.375,0.375,0.75,0.375,0.8125,0.4375,0.4375,0.4375,0.4375,0.6875,0.625,0.5,0.5625,0.4375,0.5,0.375,0.75,0.875,0.125,0.6875,0.4375,0.25,0.8125,0.4375,0.375,0.75,0.5625,0.375,0.4375,0.0625,0.25] relative_absolute_error 0.7157766837931717 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08130420946150066 root_relative_squared_error 0.8171380504856391 total_cost 0 area_under_roc_curve 0.9820996039328082 [1,0.987342,1,1,1,0.993711,0.996835,1,1,1,0.977848,0.993671,1,0.981013,1,0.836478,0.990506,0.943038,0.89557,0.974843,0.987421,0.968553,0.962264,1,1,0.977848,0.993671,0.987342,0.993671,0.993711,1,1,0.974684,0.974684,0.990506,0.993711,1,0.981132,1,1,1,1,1,0.901899,1,0.981013,0.987421,1,1,0.829114,0.993671,1,0.993711,0.946203,1,0.968553,1,1,1,0.968354,1,1,1,0.993671,1,0.993671,0.987342,1,1,0.918239,0.993671,1,0.996835,1,1,1,1,0.920886,1,1,0.981013,1,0.981132,1,0.924051,0.984177,1,0.996835,1,0.993711,0.984177,1,1,0.981013,1,1,0.993711,0.981013,0.810127,0.993711] area_under_roc_curve 0.9825399550195046 [0.937107,0.993671,1,1,1,1,1,1,1,1,0.987342,1,1,0.927215,1,1,1,0.996835,0.971519,0.955975,1,0.968553,0.993711,1,1,0.962025,0.984177,0.946203,1,1,1,1,0.996835,0.996835,0.990506,1,1,0.893082,1,1,0.974843,1,1,0.996835,1,0.987342,1,0.993711,0.990506,1,0.996835,0.962025,1,0.990506,1,0.981132,1,1,0.993711,0.914557,0.993711,0.981132,1,0.943038,1,0.984177,0.971519,1,0.937107,0.962264,0.886076,1,0.962025,1,1,0.993671,0.990506,0.943038,1,1,0.996835,0.996835,1,1,0.996835,1,1,0.971519,0.993671,1,0.987342,1,0.987342,0.96519,0.949367,1,0.830189,0.984177,0.813291,0.993711] area_under_roc_curve 0.9872063082159059 [0.993671,1,1,1,0.996835,1,0.993711,0.996835,0.996835,1,0.987342,1,0.993711,1,1,0.968354,1,0.968354,0.918239,0.958861,0.968553,1,0.996835,1,1,0.96519,0.933544,0.981132,1,1,1,1,0.984177,0.987342,0.987421,1,1,0.968553,1,1,0.993671,0.955975,1,1,0.993671,0.987421,0.996835,0.974843,0.984177,1,0.962025,1,1,0.977848,1,0.984177,1,0.993671,0.949686,0.993671,0.993711,1,1,0.968553,0.996835,0.943038,0.981013,1,0.937107,0.920886,0.981013,1,1,1,0.993711,0.977848,0.990506,0.981013,0.993671,1,1,0.996835,0.990506,1,0.965409,1,1,0.995253,0.993671,0.955975,1,1,0.955975,0.990506,0.993671,0.993711,0.971519,0.984177,0.981132,0.968354] area_under_roc_curve 0.9809208562216384 [0.927215,1,0.987342,1,0.898734,0.955696,1,1,0.990506,1,0.990506,1,1,0.990506,1,0.984177,0.996835,0.981013,0.90566,0.936709,0.974843,1,0.996835,1,0.981132,0.990506,0.943038,0.987421,1,1,1,1,1,0.984177,0.993711,1,0.949686,0.874214,1,0.996835,0.990506,0.987421,1,0.993711,1,0.993711,0.993671,1,1,1,0.889241,1,0.993711,0.85443,1,0.981013,1,1,0.993711,0.971519,1,1,0.996835,0.993711,1,0.996835,0.955696,1,1,0.955696,0.96519,0.996835,0.962264,0.993671,0.987421,0.990506,0.993671,0.863924,0.996835,1,1,0.993671,1,0.993671,1,1,1,0.981013,1,1,1,1,0.949686,0.974684,1,1,0.933544,1,0.798742,1] area_under_roc_curve 0.9807775555290185 [0.96519,1,1,1,1,0.993671,1,1,0.974843,1,1,0.993671,1,0.830189,1,0.993671,1,0.974684,0.962264,0.993671,1,1,0.977848,1,0.984177,0.990506,0.905063,0.987421,1,1,1,1,0.977848,0.971519,1,1,0.990506,0.908228,1,1,0.974684,1,1,0.993711,1,0.981132,0.996835,0.996835,0.987342,0.971519,0.90566,0.930818,0.990506,0.993711,0.993671,0.968354,1,0.977848,1,0.974843,1,1,0.993711,0.993711,0.984177,0.996835,0.955975,1,0.96519,0.911392,1,1,1,1,0.832278,0.987421,0.943038,0.984177,1,1,1,0.984177,0.996835,0.990506,0.842767,1,1,0.874214,1,1,0.987421,1,1,0.90566,0.949367,0.943396,0.993671,0.974843,0.949686,0.981013] area_under_roc_curve 0.9812042233898571 [0.924051,0.987421,0.977848,1,1,0.996835,1,0.987342,1,1,1,0.984177,0.930818,1,1,0.993671,1,0.968354,0.981132,0.876582,0.996835,1,1,1,0.993671,0.943038,0.914557,1,1,1,1,1,1,0.993671,0.955975,1,0.943038,0.949367,0.996835,1,0.993671,0.996835,1,0.90566,0.993711,0.993711,1,0.990506,0.977848,0.996835,1,1,0.993671,0.993711,0.990506,0.955696,1,1,0.996835,1,0.990506,1,0.987421,1,1,0.974684,0.886792,1,0.987342,0.96519,0.886076,0.990506,1,1,0.993671,0.981132,0.996835,0.860759,1,1,0.886792,1,0.987342,0.990506,0.924528,1,1,1,0.993671,0.993671,0.962264,1,0.993711,1,0.993671,1,1,1,0.792453,0.981013] area_under_roc_curve 0.9851136454103973 [0.996835,1,1,1,1,0.996835,0.993671,1,1,1,1,1,0.971519,1,1,0.96519,1,0.830189,0.917722,0.96519,0.987342,0.996835,0.990506,1,0.993671,0.798742,0.993711,0.990506,0.987421,1,0.987342,1,0.993711,1,1,1,0.955696,0.987342,1,0.993711,1,0.993671,1,0.993671,0.987421,0.971519,0.996835,0.996835,1,1,0.943396,1,1,0.993711,0.955696,0.971519,1,0.987342,0.996835,0.993711,0.993671,0.984177,1,0.993671,0.996835,0.993711,0.981132,0.993671,0.974684,0.971519,0.993711,1,1,1,1,0.981132,1,0.955975,1,0.987421,0.96519,1,0.977848,0.996835,0.996835,1,1,1,0.993711,0.996835,1,1,0.955696,0.955975,1,0.977848,0.96519,0.968553,0.860759,0.984177] area_under_roc_curve 0.9799058096489134 [0.993671,0.993711,1,1,0.993711,1,0.993671,1,1,1,0.949686,1,1,1,1,0.990506,1,0.924528,0.943038,0.981013,0.933544,0.996835,0.996835,0.993671,0.990506,0.993711,0.968553,0.984177,0.584906,1,0.996835,1,1,0.993711,0.955696,1,0.936709,0.927215,1,1,0.996835,1,1,0.882911,1,0.993671,1,0.993671,0.968553,0.981132,0.955975,0.990506,0.958861,0.962264,0.987342,0.892405,1,1,0.990506,0.974843,0.996835,0.952532,1,1,1,0.949686,0.918239,0.996835,0.876582,0.981013,1,1,0.984177,1,0.981013,1,1,0.955975,1,1,0.990506,1,0.958861,0.996835,0.974684,1,1,0.962264,0.955975,1,0.993711,1,0.981013,1,1,0.990506,0.993671,1,0.949367,0.996835] area_under_roc_curve 0.9868454442321474 [1,0.984177,1,1,0.949367,0.987421,1,1,0.987342,1,0.987421,1,0.996835,1,1,1,1,0.987421,0.990506,0.981132,1,1,0.937107,1,0.990506,1,0.981132,1,1,1,1,1,1,0.981132,0.971519,1,1,0.968354,1,0.987421,0.993711,0.996835,1,0.939873,0.996835,1,1,0.974684,0.993711,0.949686,0.977848,0.996835,0.996835,0.955696,0.933544,0.962264,1,1,1,0.977848,0.996835,0.984177,0.996835,0.990506,1,1,0.958861,1,0.889241,0.899371,1,0.993711,0.955696,1,1,0.971519,1,1,1,1,1,0.981132,1,0.993711,1,1,1,0.993671,1,0.958861,0.968354,0.996835,0.987342,0.984177,0.993711,0.981013,0.974843,1,0.974684,0.981132] area_under_roc_curve 0.979827193296712 [0.893082,0.981013,1,1,1,1,1,1,1,1,1,1,0.933544,0.996835,1,0.955975,0.993711,0.981132,0.974684,0.937107,0.952532,0.971519,0.993711,1,0.993671,0.993711,0.930818,0.955696,0.952532,1,1,1,0.893082,1,1,1,0.968354,0.993671,1,1,1,1,1,0.990506,1,0.962025,1,1,1,1,0.97943,1,0.977848,0.916139,1,0.949686,1,1,0.984177,0.927215,0.993671,0.993671,0.984177,0.996835,0.993711,0.987421,0.981013,1,0.993671,0.987421,0.943396,1,1,0.993671,0.860759,0.968354,1,0.962264,1,1,0.93038,0.962264,0.899371,0.993711,0.974684,0.952532,1,0.974684,1,1,0.981013,1,1,0.977848,0.987421,0.977848,0.72956,0.974684,0.981013,1] 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 0.5073619389728812 kappa 0.4505624629958555 kappa 0.49461049472894536 kappa 0.48831332912192044 kappa 0.45672773215413776 kappa 0.5199747355123954 kappa 0.5135433941404091 kappa 0.4695923280318876 kappa 0.5202209508778852 kappa 0.44440059979480706 kb_relative_information_score 99.93214619062688 kb_relative_information_score 95.71492794900219 kb_relative_information_score 93.5741710071352 kb_relative_information_score 95.54642295740484 kb_relative_information_score 93.79978249591764 kb_relative_information_score 91.14552091828078 kb_relative_information_score 99.02279769511416 kb_relative_information_score 93.71133774095098 kb_relative_information_score 98.46737378309756 kb_relative_information_score 91.60284779875849 mean_absolute_error 0.013499338376885082 mean_absolute_error 0.014425200593199766 mean_absolute_error 0.014447516699043872 mean_absolute_error 0.01428198145498614 mean_absolute_error 0.01426201588896841 mean_absolute_error 0.014825576943124452 mean_absolute_error 0.013693053947221567 mean_absolute_error 0.013986510723573053 mean_absolute_error 0.013903512845905682 mean_absolute_error 0.014399075918140495 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 0.5125 predictive_accuracy 0.45625 predictive_accuracy 0.5 predictive_accuracy 0.49375 predictive_accuracy 0.4625 predictive_accuracy 0.525 predictive_accuracy 0.51875 predictive_accuracy 0.475 predictive_accuracy 0.525 predictive_accuracy 0.45 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 0.5125 [1,0,0.5,1,0,0,0.5,1,1,1,0.5,0,1,0,1,0,0.5,0,0,0,0,0,0,1,1,0.5,1,0.5,0.5,0,0.5,0.5,0.5,0,0.5,0,0,0,1,0.5,1,1,1,0,1,0,0,0,1,0.5,1,1,0,0,1,0,1,1,1,0,1,1,0.5,0.5,0,0.5,0,1,1,0,0.5,1,0,1,1,0.5,0.5,0,1,0.5,0.5,1,0,1,0.5,0.5,0.5,0.5,0.5,0,0,1,1,0.5,1,1,1,1,0,0] recall 0.45625 [1,0,0,0,0,0,0.5,1,0.5,1,0,0.5,1,0.5,1,1,0,0,0.5,0,1,0,0,1,1,0,0.5,0,1,0,1,1,0.5,0,1,1,0,0,1,0.5,0,1,1,0,1,0.5,1,0,0.5,0.5,1,0.5,1,0.5,0,0,1,1,0,0,0,0,1,0.5,1,0,0,1,0,0,0,1,0,1,1,1,0.5,0.5,1,1,0,0.5,0,0,0.5,0.5,1,0,1,0,0,1,0,0,0,1,0,0,0,0] recall 0.5 [1,1,1,1,0.5,1,0,1,0.5,1,0.5,1,1,0,0,0.5,0.5,0,0,0.5,0,0,0.5,1,1,0.5,0,0,0.5,1,1,1,1,0,0,0,1,0,1,0.5,1,0,1,1,0.5,1,0.5,0,0.5,0.5,0,0,1,0.5,1,0.5,1,0.5,0,0.5,0,1,0.5,0,0.5,0,0,1,0,0.5,0,1,1,1,1,0,0,1,0,0.5,1,0.5,0.5,0,0,1,1,0,0.5,0,0.5,1,0,0.5,1,0,0.5,0,0,0] recall 0.49375 [0,0.5,0,1,0.5,0,1,1,0.5,0.5,0.5,1,1,0,1,0.5,0.5,0,0,0,0,1,1,1,0,0,0,1,1,1,1,0.5,0.5,0.5,1,0,0,0,0,0.5,1,0,0,0,1,1,0.5,1,1,0.5,0,0,1,0,1,0.5,1,0,0,0,1,0,0.5,0,0,1,0,1,1,0.5,0,0.5,0,0.5,0,0.5,0.5,0,0.5,0.5,1,0.5,1,0.5,1,1,1,0,1,1,0.5,1,0,0.5,1,1,0,1,0,0.5] recall 0.4625 [0.5,1,0.5,0.5,0,0,1,0.5,0,0.5,1,0.5,1,0,0,1,0.5,0.5,0,1,0.5,1,0.5,1,0,0.5,0,0,1,0.5,1,1,0,0,1,0,0,0,0.5,0.5,0.5,0,1,1,1,0,1,1,0,0.5,0,0,0.5,0,1,0.5,1,0.5,0.5,0,0,1,0,0,0.5,0.5,0,0.5,0,0,1,1,1,1,0,0,0,1,0.5,1,1,0.5,0.5,1,0,0,0,0,1,0.5,0,0.5,1,0,0.5,0,0.5,0,0,0] recall 0.525 [0,0,0,1,1,0.5,1,0.5,1,0.5,0,0,0,1,1,0.5,0.5,0,0,0,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,1,0,0,0.5,0.5,0,0.5,0.5,0.5,1,0,0,1,1,0.5,0.5,0,0.5,1,1,1,0,1,0,1,1,1,1,0.5,1,0,0,0.5,0,0,1,1,0.5,0,0.5,1,1,0,0,0.5,0,1,0,0,0.5,0.5,0.5,0,1,1,0,0.5,0,0,1,1,1,1,1,1,1,0,0.5] recall 0.51875 [0.5,1,1,1,1,0.5,0.5,0.5,0,0.5,1,1,0,1,1,0,0,0,0,0,0,1,0,0.5,0.5,0,0,1,0,1,0.5,0,0,0,1,0.5,0.5,1,1,0,1,0.5,1,0.5,0,0.5,0.5,0.5,1,0,0,1,1,0,0.5,0,1,0,1,0,0.5,0.5,1,0,0.5,0,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0,0,0,0.5,1,0.5,0.5,0.5,1,1,0,0,0.5,1,1,0,0,1,0,0.5,0,0,0.5] recall 0.475 [0,0,1,1,0,1,0,1,1,1,0,1,1,1,0,1,1,0,0,0,0.5,1,0.5,0,0.5,0,0,0,0,1,0.5,1,1,0,0.5,0.5,0,0.5,1,1,0,1,1,0,1,0.5,0.5,0,0,0,0,0,0.5,0,0.5,0,1,1,0,0,0,0.5,1,1,1,0,0,0,0,1,1,1,0.5,0,0.5,1,1,0,1,1,0.5,1,0,0.5,0,1,1,0,0,1,0,0.5,0,1,1,0.5,0,0,0,0] recall 0.525 [1,0.5,1,1,0,0,0.5,1,0.5,1,0,1,0.5,1,1,1,1,0,0.5,0,0,0.5,0,0.5,1,1,0,1,1,1,0,1,1,0,0,1,0.5,0,0,0,0,0.5,0,0.5,0.5,1,1,0.5,1,0,0.5,0.5,0.5,0,0,0,1,0,0.5,0.5,0.5,0.5,0.5,0,1,1,0.5,0,0.5,0,1,0,0,1,0.5,0.5,0,1,1,1,1,0,1,1,0.5,1,1,0.5,1,0,0.5,0.5,0.5,0.5,1,0.5,0,1,0,0] recall 0.45 [0,0.5,1,0.5,0.5,1,1,1,0.5,1,1,1,0,0.5,1,1,0,0,0,0,0,0.5,0,1,0,0,0,0,0.5,0,0.5,0.5,0,1,0.5,0.5,0,1,1,1,0,1,0.5,0.5,0.5,0.5,1,0.5,1,0,0,0.5,0,0,0.5,0,1,1,0.5,0,0.5,0.5,0,1,1,0,0,1,0.5,0,0,0,0,0.5,0.5,0,1,1,1,0.5,0,0,0,0,0.5,0.5,1,0,1,1,0,1,1,0,0,0.5,0,0,0.5,1] relative_absolute_error 0.6817847665093464 relative_absolute_error 0.7285454845050374 relative_absolute_error 0.7296725605577702 relative_absolute_error 0.7213121946962684 relative_absolute_error 0.7203038327761812 relative_absolute_error 0.7487665122790115 relative_absolute_error 0.6915683811728053 relative_absolute_error 0.7063894304834863 relative_absolute_error 0.7021976184800838 relative_absolute_error 0.7272260564717411 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 0.0789476685746642 root_mean_squared_error 0.08180809554371081 root_mean_squared_error 0.0817671377716792 root_mean_squared_error 0.08185941582701657 root_mean_squared_error 0.08232751466498978 root_mean_squared_error 0.08332974326821468 root_mean_squared_error 0.07974314473149292 root_mean_squared_error 0.08102461719383212 root_mean_squared_error 0.08005390055228827 root_mean_squared_error 0.08208146326697262 root_relative_squared_error 0.793453923440888 root_relative_squared_error 0.8222022961576794 root_relative_squared_error 0.8217906550604739 root_relative_squared_error 0.8227180844118006 root_relative_squared_error 0.8274226547462203 root_relative_squared_error 0.8374954312039746 root_relative_squared_error 0.8014487596283876 root_relative_squared_error 0.8143280424670297 root_relative_squared_error 0.8045719731404998 root_relative_squared_error 0.8249497451511738 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 2335.837574 usercpu_time_millis 2050.0537259999996 usercpu_time_millis 2122.1149429999996 usercpu_time_millis 2011.9290050000006 usercpu_time_millis 2171.192028000002 usercpu_time_millis 2162.8867619999996 usercpu_time_millis 2017.0129160000022 usercpu_time_millis 2169.742200000002 usercpu_time_millis 2084.3363170000016 usercpu_time_millis 2204.006263000004 usercpu_time_millis_testing 81.79997800000027 usercpu_time_millis_testing 80.84314999999975 usercpu_time_millis_testing 70.06167299999966 usercpu_time_millis_testing 80.08237900000026 usercpu_time_millis_testing 73.30018300000063 usercpu_time_millis_testing 84.38974099999896 usercpu_time_millis_testing 71.41446600000023 usercpu_time_millis_testing 83.09562700000228 usercpu_time_millis_testing 80.15734900000027 usercpu_time_millis_testing 70.7624640000013 usercpu_time_millis_training 2254.0375959999997 usercpu_time_millis_training 1969.210576 usercpu_time_millis_training 2052.05327 usercpu_time_millis_training 1931.8466260000005 usercpu_time_millis_training 2097.8918450000015 usercpu_time_millis_training 2078.4970210000006 usercpu_time_millis_training 1945.598450000002 usercpu_time_millis_training 2086.6465729999995 usercpu_time_millis_training 2004.1789680000015 usercpu_time_millis_training 2133.2437990000026