6016013 1 Jan van Rijn 9954 Supervised Classification 6969 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1) 4050702 bootstrap true 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features 0.2262708840630796 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 10 6902 min_samples_split 9 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 100 6902 n_jobs 1 6902 oob_score false 6902 random_state 12579 6902 verbose 0 6902 warm_start false 6902 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 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 12955157 description https://api.openml.org/data/download/12955157/description.xml -1 12955158 predictions https://api.openml.org/data/download/12955158/predictions.arff area_under_roc_curve 0.9949333175505052 [0.9884,0.999842,1,1,0.999211,0.992464,0.999724,0.999171,0.996133,0.999882,0.998343,1,0.999014,0.982599,0.999132,0.998737,0.999961,0.983783,0.988439,0.971117,0.99779,1,0.993056,1,0.999014,0.968632,0.989189,0.999132,0.997317,0.999921,1,1,0.999487,0.98982,0.99925,0.998658,0.994358,0.990728,0.99854,0.999803,0.997909,0.999132,1,0.999132,0.998935,0.999487,0.999527,0.99708,0.996725,0.997909,0.99562,0.991201,0.99712,0.982757,0.993726,0.976602,1,0.999961,0.993529,0.974274,0.999684,0.998422,0.992345,0.99854,0.999132,0.995344,0.979995,1,0.995423,0.987847,0.993924,0.998777,0.995384,0.999408,0.973485,0.998619,0.999132,0.985164,0.999961,0.999724,0.98982,0.996607,0.998303,0.996133,0.997199,0.996607,0.999961,0.995028,0.99779,0.999921,0.999921,0.999448,0.998501,0.988163,0.999724,0.998737,0.997238,0.994358,0.960938,0.997356] average_cost 0 f_measure 0.7578433893525908 [0.583333,0.941176,0.969697,1,0.909091,0.611111,0.9375,0.857143,0.583333,0.909091,0.8,0.9375,0.857143,0.545455,0.969697,0.896552,0.842105,0.571429,0.571429,0.16,0.756757,1,0.733333,1,0.823529,0.210526,0.333333,0.769231,0.903226,0.842105,0.888889,1,0.914286,0.606061,0.875,0.731707,0.571429,0.5,0.857143,0.864865,0.685714,0.882353,0.914286,0.909091,0.909091,0.875,0.83871,0.8,0.777778,0.903226,0.714286,0.666667,0.823529,0.56,0.642857,0.090909,1,0.941176,0.594595,0.642857,0.857143,0.823529,0.666667,0.888889,0.820513,0.6875,0.333333,0.914286,0.521739,0.64,0.733333,0.833333,0.666667,0.941176,0.666667,0.933333,0.842105,0.315789,0.969697,0.967742,0.615385,0.774194,0.733333,0.62069,0.666667,0.875,0.941176,0.666667,0.8,0.9375,0.909091,0.744186,0.758621,0.642857,0.875,0.774194,0.625,0.764706,0.363636,0.866667] kappa 0.7695707070707071 kb_relative_information_score 1013.6517729155421 mean_absolute_error 0.015761581767148394 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 0.780056968753207 [0.875,0.888889,0.941176,1,0.882353,0.55,0.9375,0.789474,0.875,0.882353,0.857143,0.9375,0.789474,1,0.941176,1,0.727273,0.666667,0.526316,0.222222,0.666667,1,0.785714,1,0.777778,0.666667,0.357143,0.652174,0.933333,0.727273,0.8,1,0.842105,0.588235,0.875,0.6,0.526316,0.416667,0.789474,0.761905,0.631579,0.833333,0.842105,0.882353,0.882353,0.875,0.866667,0.857143,0.7,0.933333,0.833333,0.818182,0.777778,0.777778,0.75,0.166667,1,0.888889,0.52381,0.75,0.789474,0.777778,0.818182,0.8,0.695652,0.6875,0.357143,0.842105,0.857143,0.888889,0.785714,0.75,0.517241,0.888889,0.818182,1,0.727273,1,0.941176,1,0.8,0.8,0.785714,0.692308,0.647059,0.875,0.888889,0.565217,0.736842,0.9375,0.882353,0.592593,0.846154,0.75,0.875,0.8,0.625,0.722222,0.666667,0.928571] predictive_accuracy 0.771875 prior_entropy 6.6438561897747395 recall 0.771875 [0.4375,1,1,1,0.9375,0.6875,0.9375,0.9375,0.4375,0.9375,0.75,0.9375,0.9375,0.375,1,0.8125,1,0.5,0.625,0.125,0.875,1,0.6875,1,0.875,0.125,0.3125,0.9375,0.875,1,1,1,1,0.625,0.875,0.9375,0.625,0.625,0.9375,1,0.75,0.9375,1,0.9375,0.9375,0.875,0.8125,0.75,0.875,0.875,0.625,0.5625,0.875,0.4375,0.5625,0.0625,1,1,0.6875,0.5625,0.9375,0.875,0.5625,1,1,0.6875,0.3125,1,0.375,0.5,0.6875,0.9375,0.9375,1,0.5625,0.875,1,0.1875,1,0.9375,0.5,0.75,0.6875,0.5625,0.6875,0.875,1,0.8125,0.875,0.9375,0.9375,1,0.6875,0.5625,0.875,0.75,0.625,0.8125,0.25,0.8125] relative_absolute_error 0.7960394831893113 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08221998218198143 root_relative_squared_error 0.8263419126282989 total_cost 0 area_under_roc_curve 0.9942286641190989 [1,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.987342,1,1,1,0.977848,0.984177,0.987421,1,1,0.987421,1,1,0.984177,0.993671,1,0.996835,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.981013,0.993671,1,1,0.996835,1,1,1,1,0.987421,0.984177,1,1,0.996835,1,1,1,0.984177,1,1,0.949686,0.990506,1,1,1,1,1,1,0.96519,1,1,0.996835,1,1,1,0.996835,0.984177,1,1,0.990506,1,1,1,1,0.984177,1,0.987342,1,0.996835,0.851266,1] area_under_roc_curve 0.9949411870074039 [0.987421,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.946203,1,1,1,0.955696,0.996835,0.993711,1,1,1,1,1,0.958861,0.993671,1,1,1,1,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.962025,1,1,1,0.955975,1,1,1,0.93038,1,1,0.993671,0.987342,1,1,1,1,0.981132,1,1,1,1,1,1,1,1,0.984177,1,0.996835,0.996835,1,0.987421,0.993711,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,0.981013,1,1] area_under_roc_curve 0.9936776032959157 [1,1,1,1,1,0.993671,1,1,0.993671,1,1,1,1,0.993671,0.990506,1,1,0.990506,0.987421,0.911392,0.993711,1,0.943038,1,1,0.974684,1,1,1,1,1,1,1,0.981013,1,1,0.993711,0.955975,1,1,0.996835,1,1,1,0.996835,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,0.987421,0.936709,1,1,0.996835,1,1,0.987342,0.984177,1,0.981132,0.984177,1,0.996835,0.993711,1,1,1,1,0.977848,1,1,1,0.993671,1,1,1,1,1,1,0.993671,1,1,1,1,0.981013,1,1,1,0.993671,0.937107,1] area_under_roc_curve 0.9949424309370272 [1,1,1,1,0.996835,0.984177,1,1,0.993671,1,0.996835,1,1,0.993671,1,1,1,1,0.937107,0.993671,1,1,1,1,1,0.981013,0.981013,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.93038,1,0.977848,1,1,1,0.993671,1,1,0.993671,1,0.996835,1,0.971519,1,1,0.987342,0.984177,1,0.987421,1,0.993711,0.990506,1,0.977848,1,1,0.968553,1,1,0.993671,1,1,1,1,1,1,1,1,0.993711,1,1,0.987421,0.990506,1,0.993711,0.968354] area_under_roc_curve 0.9932867606082316 [1,1,1,1,1,0.996835,1,0.996835,1,1,0.996835,1,1,0.880503,1,1,1,1,1,0.993671,0.977848,1,1,1,1,0.968354,0.990506,1,1,1,1,1,1,0.977848,1,1,0.996835,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.974843,0.996835,0.993711,0.958861,0.971519,1,0.996835,0.993671,0.993711,1,1,0.987421,1,0.996835,1,0.937107,1,0.984177,0.974684,1,1,1,1,0.898734,1,0.993671,0.971519,1,1,1,1,1,0.993671,0.987421,1,1,1,1,1,1,1,1,0.987421,0.996835,1,0.996835,1,0.981132,0.996835] area_under_roc_curve 0.994979997611655 [0.958861,1,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.914557,1,1,1,1,0.996835,0.981013,0.981013,0.993711,1,1,1,1,0.990506,0.993671,0.993711,0.996835,0.990506,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.981013,0.977848,1,1,0.993671,1,1,1,0.987421,1,1,1,0.987421,1,1,1,0.977848,0.996835,1,1,0.952532,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,0.987421,0.968553,0.996835] area_under_roc_curve 0.9960161909879786 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,0.993711,0.993671,0.971519,1,1,1,1,0.993671,0.867925,0.987421,1,1,1,1,1,1,0.981132,1,1,0.996835,1,1,1,1,0.996835,1,1,1,0.993671,1,0.984177,1,1,1,1,1,1,0.993671,0.990506,1,1,0.990506,0.993711,1,0.996835,1,0.996835,1,0.993711,0.968553,1,1,1,0.993711,1,1,1,0.993671,1,1,0.987421,1,1,0.96519,1,1,1,1,1,1,0.949686,1,1,1,1,0.996835,1,1,1,1,1,0.971519,1] area_under_roc_curve 0.9964483321391606 [0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.990506,1,0.987421,0.990506,0.996835,1,1,1,1,1,0.987421,0.968553,1,0.993711,0.996835,1,1,1,1,1,1,0.993671,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,0.955975,1,0.990506,1,1,0.946203,1,1,0.993671,1,1,1,0.993711,1,1,0.974843,0.955975,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.990506,1,0.993671,1,1,0.993711,0.993711,1,1,1,1,0.987421,1,1,0.993671,1,0.987342,1] area_under_roc_curve 0.9962886115755113 [1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.987421,0.981132,1,1,1,1,1,1,0.993711,1,1,1,0.974684,1,1,0.981132,1,1,0.996835,1,1,1,1,0.987421,1,0.996835,1,1,0.958861,0.996835,0.955975,1,1,0.993671,0.971519,0.996835,1,1,1,1,0.993711,0.993671,1,0.996835,0.993711,1,0.993711,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.996835,1,0.993671,1,1,0.974843,1,0.949367,1] area_under_roc_curve 0.997357395907969 [0.955975,1,1,1,1,1,1,0.981132,0.996835,1,1,1,1,0.993671,1,1,1,0.987421,0.996835,0.981132,1,1,1,1,1,0.974843,1,1,0.993671,1,1,1,1,0.987421,1,1,0.996835,1,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.996835,1,1,1,1,0.987342,1,1,1,0.977848,1,0.993711,1,0.996835,1,1,1,0.987421,1,0.993671,1,0.996835,0.996835,1,1,0.993711,1,1,0.974843,1,0.987421,1,1,0.996835,0.990506,1,0.996835,1,1,1,0.993671,1,1,1,1,1,1] 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 0.7031657061656272 kappa 0.7978681405448086 kappa 0.7536809694864406 kappa 0.7473351756810107 kappa 0.785226420308737 kappa 0.7472852912142152 kappa 0.7788658979624072 kappa 0.7599020653161157 kappa 0.791559748924243 kappa 0.829397361977727 kb_relative_information_score 100.23997249385302 kb_relative_information_score 100.62241455317513 kb_relative_information_score 101.37670157083794 kb_relative_information_score 100.6325620867052 kb_relative_information_score 100.5220003877504 kb_relative_information_score 100.76454113190161 kb_relative_information_score 102.7548308555938 kb_relative_information_score 101.92315120195961 kb_relative_information_score 102.38486570421215 kb_relative_information_score 102.43073292955324 mean_absolute_error 0.015811847216295128 mean_absolute_error 0.01583371865859465 mean_absolute_error 0.015725949184601615 mean_absolute_error 0.01585130138365983 mean_absolute_error 0.01577716882371778 mean_absolute_error 0.015847920649256286 mean_absolute_error 0.015628317259338047 mean_absolute_error 0.01566616784235987 mean_absolute_error 0.015722249234953484 mean_absolute_error 0.015751177418707244 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 0.70625 predictive_accuracy 0.8 predictive_accuracy 0.75625 predictive_accuracy 0.75 predictive_accuracy 0.7875 predictive_accuracy 0.75 predictive_accuracy 0.78125 predictive_accuracy 0.7625 predictive_accuracy 0.79375 predictive_accuracy 0.83125 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 0.70625 [1,1,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,0,0.5,0,1,1,0,1,0,0.5,0,1,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,0,1,1,0,0.5,1,1,0,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,0,1,1,1,0.5,0.5,0.5,0,1,1,0,1] recall 0.8 [0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0,1,0,0,1,1,1,1,1,1,1,0,1,0.5,0,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0,1,1,0,0.5,0.5,1] recall 0.75625 [0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,0,0,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0,1,1,0.5,0.5,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1] recall 0.75 [1,1,1,1,0.5,0,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0.5,1,0,0.5,0,1,1,1,1,0.5,1,0,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,0,0.5,1,0,0] recall 0.7875 [0.5,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,0,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,0.5,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5] recall 0.75 [0,1,1,1,1,0.5,1,1,1,0.5,0,1,0,1,1,1,1,0.5,1,0,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,0.5,1,1,0,0,1] recall 0.78125 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,0,0.5,0,1,1,1,1,0.5,0,0,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0,0.5,0,1,1,0.5,1,0.5,0.5,1,1,1,1,0,1,0.5,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.7625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,1,0,1,1,0.5,1,1,0,0,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,0,1,0,1,1,1,1,1,0,1,1,0,1,1,0,1,0,1,0,0.5,1,0,1,1,1,1,1,0,1,1,0.5,1,0,1] recall 0.79375 [0,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,0.5,0,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,0.5,1] recall 0.83125 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0,0.5,1,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1] relative_absolute_error 0.7985781422371264 relative_absolute_error 0.799682760535082 relative_absolute_error 0.7942398578081611 relative_absolute_error 0.8005707769525153 relative_absolute_error 0.7968267082685735 relative_absolute_error 0.8004000327907203 relative_absolute_error 0.7893089524918193 relative_absolute_error 0.791220598098982 relative_absolute_error 0.7940529916643161 relative_absolute_error 0.7955140110458191 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 0.08247525998329107 root_mean_squared_error 0.08247228700001312 root_mean_squared_error 0.08212549758877002 root_mean_squared_error 0.08270731569438938 root_mean_squared_error 0.08236693104607742 root_mean_squared_error 0.08269331690832243 root_mean_squared_error 0.0815997314424048 root_mean_squared_error 0.08178211030395054 root_mean_squared_error 0.08189202260135123 root_mean_squared_error 0.08207729009857187 root_relative_squared_error 0.8289075510654231 root_relative_squared_error 0.8288776714592384 root_relative_squared_error 0.8253923067369306 root_relative_squared_error 0.83123979871443 root_relative_squared_error 0.8278188042815576 root_relative_squared_error 0.83109910562078 root_relative_squared_error 0.8201081581461289 root_relative_squared_error 0.8219411346717028 root_relative_squared_error 0.8230457948241999 root_relative_squared_error 0.8249078032306518 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 2214.947795997432 usercpu_time_millis 2075.140388998989 usercpu_time_millis 2080.337669001892 usercpu_time_millis 2073.997033003252 usercpu_time_millis 2067.0972510015417 usercpu_time_millis 2059.86275900068 usercpu_time_millis 2062.6896590001706 usercpu_time_millis 2063.6692450025294 usercpu_time_millis 2119.043146001786 usercpu_time_millis 2061.5033829963068 usercpu_time_millis_testing 32.819996999023715 usercpu_time_millis_testing 33.092552999733016 usercpu_time_millis_testing 33.37876800287631 usercpu_time_millis_testing 33.30232100051944 usercpu_time_millis_testing 32.82488500190084 usercpu_time_millis_testing 32.7671660015767 usercpu_time_millis_testing 32.806130999233574 usercpu_time_millis_testing 32.869538001250476 usercpu_time_millis_testing 32.89218899953994 usercpu_time_millis_testing 32.924258997809375 usercpu_time_millis_training 2182.1277989984083 usercpu_time_millis_training 2042.0478359992558 usercpu_time_millis_training 2046.958900999016 usercpu_time_millis_training 2040.6947120027326 usercpu_time_millis_training 2034.272365999641 usercpu_time_millis_training 2027.0955929991032 usercpu_time_millis_training 2029.883528000937 usercpu_time_millis_training 2030.7997070012789 usercpu_time_millis_training 2086.1509570022463 usercpu_time_millis_training 2028.5791239984974