10012757 6892 Scikit-learn Bot 9954 Supervised Classification 8918 sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(2) 7937478 n_jobs null 8891 remainder "passthrough" 8891 sparse_threshold 0.3 8891 transformer_weights null 8891 memory null 8892 axis 0 8893 copy true 8893 missing_values "NaN" 8893 strategy "most_frequent" 8893 verbose 0 8893 copy true 8894 with_mean true 8894 with_std true 8894 memory null 8895 copy true 8896 fill_value -1 8896 missing_values NaN 8896 strategy "constant" 8896 verbose 0 8896 categorical_features null 8897 categories null 8897 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 8897 handle_unknown "ignore" 8897 n_values null 8897 sparse true 8897 threshold 0.0 8898 memory null 8918 bootstrap false 8919 class_weight null 8919 criterion "gini" 8919 max_depth null 8919 max_features 0.6187760943501224 8919 max_leaf_nodes null 8919 min_impurity_decrease 0.0 8919 min_impurity_split null 8919 min_samples_leaf 20 8919 min_samples_split 6 8919 min_weight_fraction_leaf 0.0 8919 n_estimators 100 8919 n_jobs null 8919 oob_score false 8919 random_state 45362 8919 verbose 0 8919 warm_start false 8919 openml-python Sklearn_0.20.2. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 20946037 description https://api.openml.org/data/download/20946037/description.xml -1 20946038 predictions https://api.openml.org/data/download/20946038/predictions.arff area_under_roc_curve 0.9876481613005049 [0.975892,0.99637,0.996173,0.999211,0.997633,0.992424,0.994515,0.992069,0.993529,0.999527,0.990294,0.995818,0.995541,0.976444,0.994081,0.990254,0.998067,0.970999,0.981376,0.970052,0.998658,0.996488,0.990925,0.999882,0.995305,0.950324,0.960109,0.991319,0.987176,0.996252,0.995857,0.997554,0.9927,0.984099,0.995226,0.992227,0.984809,0.977194,0.992109,0.996883,0.998224,0.995423,0.996409,0.989978,0.99203,0.998935,0.991753,0.993292,0.990609,0.993332,0.990372,0.983625,0.99128,0.973485,0.983902,0.975458,0.999448,0.988913,0.985993,0.972617,0.99929,0.9942,0.948686,0.990136,0.992385,0.992977,0.969894,0.985361,0.939157,0.976089,0.973445,0.996173,0.98907,0.996528,0.951389,0.98544,0.996173,0.968711,0.997711,0.992622,0.971985,0.991241,0.994318,0.994634,0.988163,0.99274,0.998185,0.98256,0.994003,0.997909,0.99633,0.996607,0.988755,0.973603,0.987098,0.991832,0.987453,0.981771,0.96512,0.994634] average_cost 0 kappa 0.5593434343434344 kb_relative_information_score 1001.5393799105361 mean_absolute_error 0.015522115299989625 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] predictive_accuracy 0.56375 prior_entropy 6.6438561897747395 recall 0.56375 [0.3125,0.9375,0.875,0.875,0.8125,0.375,0.8125,0.5,0.4375,1,0.625,0.875,0.75,0.4375,0.75,0.625,0.9375,0.375,0.125,0.125,1,0.625,0.5,1,0.5,0,0,0.3125,0.75,0.625,0.875,0.9375,0.8125,0.375,0.75,0.75,0.625,0.25,0.25,0.8125,0.875,0.75,0.8125,0.3125,0.4375,0.9375,0.875,0.5625,0.625,0.6875,0.625,0.5,0.5625,0.25,0.625,0.125,1,0.6875,0,0.25,1,0.9375,0.5625,0.6875,0.8125,0.875,0.125,0.1875,0,0.25,0,0.5,0.5625,0.8125,0.0625,0,0.8125,0.125,0.9375,0.125,0.0625,0.6875,0.4375,0.8125,0.625,0.625,0.875,0.5625,0.75,0.875,0.8125,0.8125,0.375,0.1875,0.625,0.625,0.375,0.0625,0.1875,0.8125] relative_absolute_error 0.7839452171711918 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08350070248344807 root_relative_squared_error 0.8392136359657404 total_cost 0 area_under_roc_curve 0.9866760210174352 [0.993711,0.987342,1,1,0.993671,1,1,1,0.990506,1,0.981013,0.993671,0.987342,0.949367,0.993671,0.974843,1,0.962025,0.968354,0.981132,1,1,0.987421,1,0.993711,0.987342,0.889241,1,0.968354,1,0.968354,0.993671,0.974684,0.96519,1,0.962264,1,0.993711,1,1,1,1,0.996835,0.977848,1,1,0.993711,1,1,0.96519,0.990506,1,0.987421,0.974684,1,1,1,1,0.981132,0.981013,1,1,0.990506,0.990506,1,1,0.984177,0.993711,1,1,0.949367,1,0.981013,1,1,0.990506,1,0.974684,1,1,0.962025,0.996835,0.993711,1,0.974684,0.990506,1,1,0.996835,0.993711,0.993671,1,0.990506,0.984177,1,0.981013,1,0.993671,0.85443,1] area_under_roc_curve 0.9859010528620333 [0.974843,1,1,1,1,0.987421,0.990506,1,1,1,1,0.996835,1,0.984177,1,1,0.993671,0.920886,0.977848,0.981132,1,0.993711,1,1,1,0.927215,0.974684,0.981013,1,0.981132,1,1,1,0.990506,0.993671,0.974843,0.968553,1,0.987421,1,1,1,1,0.987342,0.996835,1,1,1,0.996835,1,1,0.927215,1,1,0.987421,0.974843,1,1,1,0.914557,1,0.968553,1,0.977848,1,0.990506,0.996835,1,0.698113,0.981132,0.955696,1,1,0.996835,1,0.993671,0.990506,0.927215,1,0.984177,0.939873,0.987342,0.974843,0.993711,0.981013,1,0.993671,1,1,1,0.993671,1,0.993671,0.958861,1,1,0.993711,0.955696,0.990506,1] area_under_roc_curve 0.9903165054533873 [1,1,1,1,0.990506,1,1,0.996835,0.990506,1,1,1,1,0.990506,1,1,1,0.977848,0.968553,0.920886,1,0.981132,0.987342,1,1,0.990506,0.987342,1,1,1,1,1,0.987342,0.974684,1,1,0.993711,0.937107,1,1,0.990506,1,1,0.993711,0.993671,1,0.971519,1,0.971519,1,1,1,1,0.996835,1,1,1,1,0.968553,0.974684,1,0.987421,0.996835,1,0.984177,0.981013,0.990506,0.996835,0.987421,0.962025,0.949367,1,1,1,1,0.993671,1,0.93038,0.987342,0.996835,0.993711,1,0.996835,1,1,1,1,0.981013,1,1,1,1,1,0.962025,0.987342,1,0.984177,0.977848,0.987421,0.974684] area_under_roc_curve 0.9926498686410316 [0.977848,1,0.996835,1,0.996835,0.993671,1,0.996835,0.993671,1,0.984177,1,1,0.981013,0.996835,1,1,0.993671,0.949686,0.984177,1,1,0.996835,1,0.981132,0.974684,0.971519,1,1,1,1,1,1,0.987342,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.993671,1,0.993671,1,0.990506,1,0.993711,0.927215,1,0.990506,1,1,1,0.996835,1,0.993711,0.990506,1,0.993671,1,0.96519,0.971519,0.993711,0.987342,0.990506,0.996835,0.981132,1,0.993711,0.987342,0.996835,0.971519,1,0.993671,0.949686,0.990506,1,0.993671,1,1,1,1,1,1,0.993671,1,0.987421,0.984177,1,0.993711,0.990506,0.993671,1,1] area_under_roc_curve 0.9854201496696119 [0.981013,1,1,1,1,0.993671,1,0.996835,1,1,0.993671,0.984177,1,0.886792,1,0.987342,1,0.993671,1,0.987342,0.996835,1,1,1,0.993671,0.968354,0.96519,1,1,0.996835,1,1,1,0.968354,1,1,0.993671,0.914557,0.990506,1,1,1,1,1,0.987421,1,1,1,0.993671,1,1,0.962264,0.996835,0.993711,0.943038,0.962025,1,0.977848,0.990506,0.974843,0.996835,1,0.987421,1,0.984177,1,0.937107,0.933544,0.870253,0.974684,0.990506,1,1,0.993711,0.870253,0.993711,0.993671,0.977848,1,0.987421,1,1,0.987342,1,0.955975,0.974843,1,1,1,0.993671,1,0.993671,0.955975,1,0.924051,1,0.993671,1,0.987421,0.990506] area_under_roc_curve 0.9913089125069657 [0.952532,1,1,1,1,0.984177,1,0.984177,1,1,0.981013,1,1,0.993711,0.993711,1,1,0.987342,0.993711,0.958861,1,1,0.993671,1,0.996835,0.984177,0.981013,1,1,0.993671,1,1,0.981013,0.996835,0.993711,0.993671,0.971519,0.993671,0.993671,0.993671,1,0.990506,0.993711,0.993711,0.993711,1,0.996835,0.996835,0.996835,0.990506,1,1,0.987342,1,0.993671,0.977848,1,1,0.993671,0.993711,1,1,0.987421,1,1,1,0.993711,1,0.977848,1,0.984177,0.984177,1,1,0.943038,0.962264,1,0.968354,1,0.987421,0.955975,0.981013,1,0.996835,0.962264,1,1,0.993711,0.977848,1,1,0.996835,0.943396,1,1,1,1,0.974843,1,0.993671] area_under_roc_curve 0.9927837154685136 [1,0.993711,0.987421,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.981013,1,1,0.990506,0.968354,0.990506,1,0.987342,1,0.990506,0.899371,0.955975,0.993671,0.993711,0.996835,1,0.981132,1,0.974843,1,1,0.990506,0.993671,1,1,1,1,1,0.987342,0.968553,0.993671,0.990506,0.990506,0.993711,1,0.955975,1,1,1,0.990506,1,1,0.990506,0.987342,0.987421,1,1,1,0.993671,0.996835,1,0.943396,1,0.987342,0.974684,1,1,1,1,1,0.974843,1,0.993711,1,0.993711,0.981013,1,1,1,1,0.990506,1,0.90566,1,1,1,1,0.984177,0.943396,0.987421,0.993671,1,0.993711,1,1] area_under_roc_curve 0.9887929404506007 [1,1,1,0.993671,1,0.990506,1,1,0.993711,1,0.981132,1,0.993671,1,0.974843,0.990506,1,0.968553,0.968354,0.984177,1,1,1,1,0.996835,0.981132,0.968553,0.971519,0.874214,0.987342,1,1,1,0.993711,1,1,0.981013,0.977848,0.993671,1,1,1,1,0.993671,0.993711,0.996835,1,0.996835,0.993711,1,0.968553,0.993671,0.981013,1,0.996835,0.949367,1,1,0.974684,0.943396,1,0.993671,0.987421,0.993671,0.996835,0.949686,0.974843,0.977848,0.955696,0.974684,0.981132,1,1,1,0.981013,1,1,0.968553,1,1,0.977848,0.962264,0.984177,1,1,0.984177,1,0.943396,1,1,1,0.990506,1,0.930818,1,0.977848,0.974684,0.981132,0.971519,1] area_under_roc_curve 0.9887071093065837 [0.968553,0.996835,1,1,1,0.993711,0.977848,0.962264,0.990506,1,0.981132,1,1,1,0.990506,0.962264,1,1,1,1,1,0.984177,0.943396,0.996835,1,0.855346,0.968553,1,0.996835,1,0.981013,1,1,1,0.996835,1,1,0.971519,0.993671,0.974843,0.993711,0.984177,0.996835,0.984177,0.987342,1,1,0.990506,0.981132,1,1,0.981013,1,0.943038,0.996835,0.968553,0.996835,0.993711,1,0.968354,1,1,1,0.984177,1,1,0.968354,1,0.952532,1,1,0.981132,0.971519,0.984177,0.93038,0.974684,1,1,1,0.993671,0.984177,1,1,0.993711,1,1,1,0.987342,0.987421,0.996835,1,0.996835,1,0.968354,0.981132,0.990506,0.993711,0.993671,0.971519,1] area_under_roc_curve 0.9852372920149669 [0.886792,1,0.955975,1,1,0.993711,1,0.968553,0.996835,1,1,1,1,0.993671,1,0.987421,1,0.962264,0.993671,1,1,1,1,1,1,0.880503,0.993711,0.987342,0.996835,1,1,0.996835,1,1,0.984177,1,0.981013,0.987342,0.981013,1,1,1,0.984177,0.993671,0.993671,1,1,0.977848,0.993711,0.993711,0.981013,0.996835,0.993671,0.974684,1,0.949686,1,0.90566,0.990506,0.984177,1,1,0.727848,0.981013,1,1,0.993671,0.974843,0.949367,0.949686,0.943396,0.993711,0.96519,1,0.924051,0.987342,1,0.981132,0.987421,0.993671,0.996835,0.987421,1,0.968553,0.993671,0.996835,0.984177,0.984177,1,0.996835,1,1,0.987342,0.990506,1,1,0.943396,1,0.996835,1] 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 0.5390140900659115 kappa 0.595895816890292 kappa 0.5768699427669233 kappa 0.5768866435112094 kappa 0.5830207305034552 kappa 0.5895330938943047 kappa 0.5641531780497434 kappa 0.5831688639772638 kappa 0.4633838383838384 kappa 0.5201830880321983 kb_relative_information_score 99.07748761130955 kb_relative_information_score 99.3136244219044 kb_relative_information_score 100.93677541799377 kb_relative_information_score 101.94017225603221 kb_relative_information_score 98.54652484480135 kb_relative_information_score 100.79989019571696 kb_relative_information_score 102.61830414745894 kb_relative_information_score 99.69593068492155 kb_relative_information_score 99.8544322527512 kb_relative_information_score 98.75623807764391 mean_absolute_error 0.015610576812441651 mean_absolute_error 0.015466778032531703 mean_absolute_error 0.015461333767641552 mean_absolute_error 0.01554187243838349 mean_absolute_error 0.015519721574568454 mean_absolute_error 0.015547212414316055 mean_absolute_error 0.015294674364841207 mean_absolute_error 0.015562526288337814 mean_absolute_error 0.015631122944982152 mean_absolute_error 0.015585334361852313 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 0.54375 predictive_accuracy 0.6 predictive_accuracy 0.58125 predictive_accuracy 0.58125 predictive_accuracy 0.5875 predictive_accuracy 0.59375 predictive_accuracy 0.56875 predictive_accuracy 0.5875 predictive_accuracy 0.46875 predictive_accuracy 0.525 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 0.54375 [1,1,1,1,0.5,1,1,1,0,1,0.5,0.5,0.5,0,1,0,1,0,0,0,1,0,0,1,0,0,0,1,0.5,0,0.5,1,0.5,0,1,0,1,1,0,0.5,1,1,1,0,0,1,1,1,1,0.5,1,0,1,0,1,0,1,1,0,0,1,1,0.5,0.5,1,1,0,0,0,1,0,1,0,1,0,0,0.5,0.5,1,0,0,1,0,1,0.5,0.5,1,1,0.5,1,0.5,1,1,0.5,1,0.5,1,0,0,1] recall 0.6 [1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,0.5,0,0,0,1,1,1,1,1,0,0,0.5,1,0,1,1,0.5,0,0,0,0,1,0,1,1,1,1,0.5,1,1,1,0,0.5,1,1,0,0,0.5,1,0,1,1,0,0.5,1,1,1,0.5,1,0.5,0,1,0,0,0,1,1,0.5,0,0,0.5,0.5,1,0,0,0.5,0,1,0.5,1,0.5,1,1,1,1,1,0.5,0,0.5,0.5,0,0.5,0.5,1] recall 0.58125 [0,1,1,1,0.5,0.5,1,0,1,1,1,1,1,0.5,1,1,1,0,0,0,1,0,0,1,1,0,0,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,0,1,0.5,1,0.5,0.5,1,1,1,0,1,0,1,1,0,0,1,1,0.5,1,0.5,1,0.5,0,0,0,0,1,1,0.5,0,0,1,0,0.5,0.5,0,1,0.5,1,1,1,1,0.5,1,1,1,1,0,0,0.5,1,0,0,0,0.5] recall 0.58125 [0,1,1,1,1,0,1,0.5,0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,0,0,1,1,1,1,0,0,0,0,1,0.5,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0.5,0,0,1,0,0,1,0.5,1,0.5,0,1,1,0,0.5,1,1,1,0,0.5,0,0.5,0,0.5,1,1,0,0,1,0,1,0,0,0.5,1,1,1,0,1,1,0.5,1,0,1,0,0,1,0,0,0,0,0.5] recall 0.5875 [0.5,0,1,1,1,1,1,0.5,0,1,1,0.5,0,0,1,0.5,1,0.5,0,0.5,1,1,0.5,1,0,0,0,0,1,1,1,1,1,0,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,0,0,1,0.5,0,1,1,1,1,1,0.5,1,0,0,0,0.5,0,0.5,1,1,0,0,0.5,0,1,0,1,1,0,1,0,0,1,0,1,1,1,1,0,0,0,1,0.5,0,0,1] recall 0.59375 [0,1,1,1,1,0,1,0,1,1,0,1,0,1,1,1,1,0.5,0,0,1,1,0.5,1,1,0,0,0,0,1,1,1,0.5,0.5,1,0.5,0.5,0.5,0,0.5,1,0.5,0,1,1,1,1,0,0.5,0.5,1,1,0.5,1,0.5,0.5,1,1,0,0,1,1,0,1,1,1,1,0,0,0,0,0.5,1,1,0.5,0,1,0,1,0,0,0.5,0.5,0.5,0,1,1,1,0.5,1,1,0.5,0,1,1,1,1,0,0,1] recall 0.56875 [0.5,1,0,0.5,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0,1,0.5,0.5,1,0.5,0,0,0,0,0.5,1,0,1,1,1,1,0.5,0.5,0,1,1,0.5,1,0,0,0.5,0.5,0.5,1,1,0,1,1,0,0.5,0,1,0.5,0,0,1,1,1,0.5,0.5,1,0,0.5,0,0,0,0,0.5,1,0,0,1,0,1,0,0,1,0.5,1,1,0.5,1,0,1,1,1,1,0.5,0,0,0.5,1,0,0,1] recall 0.5875 [0.5,1,1,0.5,1,0,1,1,1,1,0,1,1,0,0,0.5,1,1,0,0,1,0.5,0.5,1,0.5,0,0,0.5,0,0.5,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,0,1,1,0.5,1,1,0,0.5,0.5,1,1,0,1,1,0,0,1,1,1,1,1,0,0,0,0,0.5,0,0.5,0.5,1,0,0,1,0,1,1,0,0,0,1,0,0.5,1,0,1,1,1,0.5,1,0,1,0.5,0,0,0.5,0.5] recall 0.46875 [0,1,1,1,0.5,0,0.5,0,0,1,0,1,1,0.5,0.5,0,1,1,1,1,1,0,0,1,0.5,0,0,0,1,1,0.5,1,1,1,0.5,0.5,0.5,0,0,0,1,0.5,0.5,0,0,1,1,1,0,1,0.5,0.5,0.5,0.5,0,0,1,0,0,0,1,1,0.5,0.5,1,1,0,0,0,0,0,0,0,0.5,0,0,1,0,1,0,0,1,1,0,1,1,1,0.5,0,0.5,1,0.5,0,0,0,0.5,0,0,0,1] recall 0.525 [0,1,0,1,1,1,1,0,0,1,1,1,1,0.5,0,0,1,0,0,0,1,1,1,1,0.5,0,0,0,1,0,1,1,1,1,0.5,1,0.5,0,0,1,1,1,0.5,0,0.5,1,1,0.5,1,1,0.5,0.5,0.5,0,1,0,1,0,0,0,1,1,0,0.5,1,1,0,0,0,0,0,0,0.5,1,0,0,1,0,1,0,0,0,1,0,1,0.5,0.5,0,1,0.5,1,1,0,0.5,1,1,0,0,0.5,1] relative_absolute_error 0.7884129703253346 relative_absolute_error 0.781150405683418 relative_absolute_error 0.7808754428101782 relative_absolute_error 0.7849430524436092 relative_absolute_error 0.7838243219479004 relative_absolute_error 0.7852127481977792 relative_absolute_error 0.7724583012546051 relative_absolute_error 0.7859861761786762 relative_absolute_error 0.7894506537869761 relative_absolute_error 0.7871380990834489 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 0.08414249535706407 root_mean_squared_error 0.08333648644211775 root_mean_squared_error 0.08280067637166075 root_mean_squared_error 0.08312388523081848 root_mean_squared_error 0.08353760766549144 root_mean_squared_error 0.08344979210918092 root_mean_squared_error 0.08238616488243461 root_mean_squared_error 0.08338646669777439 root_mean_squared_error 0.08436671808707281 root_mean_squared_error 0.0844528598727954 root_relative_squared_error 0.8456638970412201 root_relative_squared_error 0.8375632026516491 root_relative_squared_error 0.8321781088255894 root_relative_squared_error 0.8354264800823924 root_relative_squared_error 0.8395845470010667 root_relative_squared_error 0.8387019674524657 root_relative_squared_error 0.8280121116102724 root_relative_squared_error 0.8380655231211617 root_relative_squared_error 0.8479174202682145 root_relative_squared_error 0.8487831777894661 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 2172.017302000313 usercpu_time_millis 2178.7210019974736 usercpu_time_millis 2221.4590159928775 usercpu_time_millis 2167.2233319986844 usercpu_time_millis 2212.205746996915 usercpu_time_millis 2156.113565994019 usercpu_time_millis 2172.904360006214 usercpu_time_millis 2176.9417250034166 usercpu_time_millis 2179.642208000587 usercpu_time_millis 2252.253820995975 usercpu_time_millis_testing 37.79978599777678 usercpu_time_millis_testing 37.551556997641455 usercpu_time_millis_testing 37.60833299747901 usercpu_time_millis_testing 37.558080002781935 usercpu_time_millis_testing 38.3263559997431 usercpu_time_millis_testing 37.842792000446934 usercpu_time_millis_testing 37.57642400159966 usercpu_time_millis_testing 37.576430004264694 usercpu_time_millis_testing 37.567617000604514 usercpu_time_millis_testing 37.70575799717335 usercpu_time_millis_training 2134.2175160025363 usercpu_time_millis_training 2141.169444999832 usercpu_time_millis_training 2183.8506829953985 usercpu_time_millis_training 2129.6652519959025 usercpu_time_millis_training 2173.879390997172 usercpu_time_millis_training 2118.270773993572 usercpu_time_millis_training 2135.3279360046145 usercpu_time_millis_training 2139.365294999152 usercpu_time_millis_training 2142.0745909999823 usercpu_time_millis_training 2214.5480629988015