10016024 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) 7940745 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 true 8919 class_weight null 8919 criterion "gini" 8919 max_depth null 8919 max_features 0.9951597585803429 8919 max_leaf_nodes null 8919 min_impurity_decrease 0.0 8919 min_impurity_split null 8919 min_samples_leaf 10 8919 min_samples_split 3 8919 min_weight_fraction_leaf 0.0 8919 n_estimators 100 8919 n_jobs null 8919 oob_score false 8919 random_state 33667 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 20952571 description https://api.openml.org/data/download/20952571/description.xml -1 20952572 predictions https://api.openml.org/data/download/20952572/predictions.arff area_under_roc_curve 0.9943777619949492 [0.989544,0.999448,1,1,0.999684,0.993845,0.998777,0.998974,0.996488,0.999961,0.998185,0.999487,0.998737,0.988084,0.999724,0.999487,0.999921,0.986585,0.988913,0.972972,0.999882,0.999961,0.994949,1,0.998224,0.970683,0.972854,0.997711,0.998027,0.999487,0.998895,0.999605,0.994437,0.988834,0.998619,0.995739,0.994239,0.981652,0.998224,0.999566,0.998856,0.998935,0.998777,0.998461,0.998461,0.999684,0.997238,0.998067,0.99637,0.994989,0.998027,0.990964,0.994673,0.981218,0.995699,0.982718,0.999961,0.998974,0.98986,0.984217,0.999803,0.998895,0.951783,0.995107,0.996607,0.995541,0.98765,0.998816,0.98256,0.993095,0.992582,0.996843,0.995107,0.999961,0.977075,0.997396,0.999171,0.988321,0.999211,0.998658,0.9927,0.99562,0.996922,0.995818,0.994594,0.997356,0.999961,0.994003,0.999211,0.999961,0.998935,0.998816,0.99708,0.992306,0.997988,0.996725,0.997514,0.991319,0.971157,0.999053] average_cost 0 f_measure 0.7312305989263116 [0.62069,0.833333,1,1,0.857143,0.774194,0.882353,0.774194,0.827586,0.969697,0.764706,0.833333,0.833333,0.615385,0.969697,0.933333,0.864865,0.588235,0.571429,0,0.83871,0.909091,0.733333,1,0.727273,0.347826,0.242424,0.666667,0.848485,0.903226,0.875,0.866667,0.8,0.428571,0.882353,0.65,0.666667,0.529412,0.8125,0.903226,0.810811,0.909091,0.717949,0.742857,0.774194,0.896552,0.727273,0.8,0.709677,0.705882,0.875,0.588235,0.777778,0.583333,0.685714,0.470588,0.969697,0.774194,0.26087,0.4,0.914286,0.744186,0.571429,0.789474,0.75,0.7,0.4375,0.733333,0.5,0.518519,0.47619,0.764706,0.5,0.967742,0.363636,0.72,0.777778,0.666667,0.941176,0.8,0.5,0.647059,0.774194,0.594595,0.6875,0.774194,1,0.702703,0.848485,1,0.842105,0.833333,0.83871,0.666667,0.933333,0.787879,0.8,0.592593,0.538462,0.8] kappa 0.7392676767676767 kb_relative_information_score 1087.1895160001407 mean_absolute_error 0.014589390335277251 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 0.7476041160764375 [0.692308,0.75,1,1,0.789474,0.8,0.833333,0.8,0.923077,0.941176,0.722222,0.75,0.75,0.8,0.941176,1,0.761905,0.555556,0.526316,0,0.866667,0.882353,0.785714,1,0.705882,0.571429,0.235294,0.647059,0.823529,0.933333,0.875,0.928571,0.736842,0.5,0.833333,0.541667,0.647059,0.5,0.8125,0.933333,0.714286,0.882353,0.608696,0.684211,0.8,1,0.705882,0.857143,0.733333,0.666667,0.875,0.555556,0.7,0.875,0.631579,0.444444,0.941176,0.8,0.428571,0.555556,0.842105,0.592593,0.526316,0.681818,0.625,0.583333,0.4375,0.785714,0.75,0.636364,1,0.722222,0.45,1,0.666667,1,0.7,1,0.888889,0.857143,0.75,0.611111,0.8,0.52381,0.6875,0.8,1,0.619048,0.823529,1,0.727273,0.75,0.866667,0.714286,1,0.764706,0.857143,0.727273,0.7,0.736842] predictive_accuracy 0.741875 prior_entropy 6.6438561897747395 recall 0.741875 [0.5625,0.9375,1,1,0.9375,0.75,0.9375,0.75,0.75,1,0.8125,0.9375,0.9375,0.5,1,0.875,1,0.625,0.625,0,0.8125,0.9375,0.6875,1,0.75,0.25,0.25,0.6875,0.875,0.875,0.875,0.8125,0.875,0.375,0.9375,0.8125,0.6875,0.5625,0.8125,0.875,0.9375,0.9375,0.875,0.8125,0.75,0.8125,0.75,0.75,0.6875,0.75,0.875,0.625,0.875,0.4375,0.75,0.5,1,0.75,0.1875,0.3125,1,1,0.625,0.9375,0.9375,0.875,0.4375,0.6875,0.375,0.4375,0.3125,0.8125,0.5625,0.9375,0.25,0.5625,0.875,0.5,1,0.75,0.375,0.6875,0.75,0.6875,0.6875,0.75,1,0.8125,0.875,1,1,0.9375,0.8125,0.625,0.875,0.8125,0.75,0.5,0.4375,0.875] relative_absolute_error 0.7368378957210718 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07801819924430729 root_relative_squared_error 0.7841124051895645 total_cost 0 area_under_roc_curve 0.992883727410238 [0.993711,1,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,1,0.990506,1,1,1,0.984177,0.981013,0.993711,1,1,0.987421,1,0.993711,0.993671,0.911392,1,1,1,0.990506,1,0.974684,0.990506,1,0.968553,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.96519,1,1,1,0.984177,1,1,1,1,0.981132,0.987342,1,1,1,0.990506,1,0.996835,0.993671,1,1,1,0.977848,1,1,1,1,1,1,0.977848,1,1,0.993671,0.996835,0.987421,1,0.984177,0.996835,1,1,0.993671,1,1,1,1,0.990506,1,0.984177,1,0.990506,0.876582,1] area_under_roc_curve 0.9936387926916647 [0.981132,1,1,1,1,0.993711,0.996835,1,1,1,1,0.996835,0.993671,0.977848,1,1,1,0.96519,0.987342,0.987421,1,1,1,1,1,0.949367,0.987342,0.993671,1,1,1,1,1,1,1,0.981132,0.981132,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.952532,1,1,0.993711,0.962264,1,1,1,0.936709,1,1,0.996835,0.981013,1,1,1,1,0.949686,1,1,1,1,1,1,1,0.996835,0.971519,1,0.996835,0.981013,0.996835,0.993711,0.993711,0.996835,1,1,0.996835,1,1,0.993671,1,1,0.987342,1,1,1,0.971519,0.987342,1] area_under_roc_curve 0.9960905779794601 [1,1,1,1,1,0.993671,0.993711,1,1,1,1,1,1,0.996835,1,1,1,0.990506,1,0.946203,1,1,0.996835,1,1,0.987342,1,1,1,1,1,1,1,0.977848,1,1,0.993711,0.955975,1,1,0.996835,1,1,1,0.996835,1,0.990506,1,0.996835,1,0.996835,1,1,1,1,0.996835,1,1,0.955975,0.993671,1,1,0.993671,1,0.996835,0.993671,0.990506,1,0.981132,0.993671,0.996835,0.996835,1,1,1,1,1,0.993671,0.993671,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.977848,1,1,1,0.990506,0.949686,1] area_under_roc_curve 0.9962463179683146 [0.987342,0.996835,1,1,1,0.987342,1,1,0.990506,1,1,1,1,0.996835,1,1,1,1,0.955975,0.984177,1,1,1,1,0.993711,0.990506,0.962025,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.936709,1,0.990506,1,1,1,1,1,1,0.990506,1,0.996835,1,0.984177,0.993671,0.993711,1,1,0.996835,0.987421,1,0.987421,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.987421,1,1,0.993711,0.987342,0.996835,1,1] area_under_roc_curve 0.9938355823580926 [0.996835,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.943396,1,1,1,0.996835,1,0.981013,1,1,1,1,1,0.996835,0.977848,1,1,1,1,1,0.996835,0.971519,1,1,1,0.933544,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.996835,0.993711,0.987342,0.974684,1,0.996835,0.996835,1,1,1,1,1,0.987342,1,0.962264,1,0.981013,0.971519,0.996835,1,0.993711,1,0.905063,1,1,0.981013,1,1,1,1,1,0.993671,0.981132,0.993711,1,1,1,1,1,0.996835,1,0.993711,0.984177,1,0.996835,1,1,0.996835] area_under_roc_curve 0.9957701417084628 [0.96519,1,1,1,1,0.984177,1,1,1,1,0.996835,1,1,1,1,1,1,0.996835,1,0.946203,1,1,0.996835,1,1,0.996835,0.968354,1,1,1,1,1,0.987342,0.993671,1,0.993671,0.987342,1,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,0.987342,1,1,0.990506,0.993711,1,1,0.987421,1,1,1,0.993711,1,0.987342,0.996835,0.987342,0.990506,1,1,0.984177,1,1,0.993671,1,1,1,0.987342,1,0.996835,1,1,1,0.993711,0.996835,1,1,0.996835,0.987421,1,1,1,1,0.981132,1,1] area_under_roc_curve 0.9970003881060423 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,0.949367,1,1,0.993671,1,0.993671,0.918239,0.987421,0.993671,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,0.987421,0.996835,0.993671,0.996835,0.993711,1,1,1,1,1,0.990506,1,1,1,0.993671,1,1,1,1,0.993671,1,1,0.993711,0.996835,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,0.962264,1,1,1,1,0.990506,1,1,1,1,1,0.990506,1] area_under_roc_curve 0.9951046393599231 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.993711,0.996835,1,0.987421,0.981013,0.987342,1,1,1,1,1,0.981132,0.987421,0.996835,0.968553,1,1,1,1,1,1,1,1,0.984177,1,1,1,1,1,1,0.993711,0.996835,1,1,0.993711,1,0.993711,0.993671,0.981013,1,0.993671,0.968354,1,1,0.974684,0.968553,1,0.996835,1,1,1,0.937107,0.993711,1,0.974684,0.977848,1,1,1,1,0.993671,1,1,1,1,1,0.993671,0.987421,0.984177,1,0.996835,0.984177,1,0.993711,1,1,1,1,1,0.987421,1,0.993671,0.993671,0.993711,0.987342,1] area_under_roc_curve 0.9956967498606797 [0.981132,1,1,1,1,0.993711,0.996835,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.955975,1,1,0.974843,0.993711,1,1,1,1,1,1,1,1,1,1,0.962025,1,1,0.993711,1,0.996835,1,1,1,1,1,0.993711,1,0.996835,0.990506,1,0.946203,1,0.955975,1,1,1,0.981013,1,1,1,1,1,1,0.993671,1,0.996835,0.993711,1,0.974843,0.984177,1,0.996835,0.984177,1,0.993711,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,0.996835,1,1,0.987342,1,0.987342,0.993711,1,0.977848,1] area_under_roc_curve 0.9928513852400286 [0.987421,1,1,1,1,0.993711,1,0.974843,0.996835,1,1,1,0.996835,0.993671,1,1,1,1,0.993671,1,1,1,1,1,1,0.880503,0.993711,0.996835,0.996835,1,1,0.996835,1,1,0.990506,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,0.993671,0.993711,1,0.996835,1,1,0.974684,1,1,1,0.993711,0.996835,0.993671,1,0.996835,0.724684,0.993671,1,1,0.993671,1,0.996835,0.993711,0.955975,1,0.987342,1,0.990506,0.990506,1,1,1,0.993671,1,0.981132,1,0.968553,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.6967543236199952 kappa 0.7284281992579142 kappa 0.7915679772619612 kappa 0.722079665232324 kappa 0.7536031589338598 kappa 0.7409674234945706 kappa 0.7978282329713722 kappa 0.7157408504086226 kappa 0.7283209603538146 kappa 0.7157857340228161 kb_relative_information_score 106.71561150529767 kb_relative_information_score 107.79023094170374 kb_relative_information_score 109.27973161102933 kb_relative_information_score 109.43095625695159 kb_relative_information_score 108.47413558661705 kb_relative_information_score 109.02722552398117 kb_relative_information_score 110.9610032733982 kb_relative_information_score 108.09954188510591 kb_relative_information_score 109.0319064803141 kb_relative_information_score 108.3791729357429 mean_absolute_error 0.014718447261378267 mean_absolute_error 0.01463019653181396 mean_absolute_error 0.014553887741981191 mean_absolute_error 0.014599375565370557 mean_absolute_error 0.014488134631211003 mean_absolute_error 0.014609611323156043 mean_absolute_error 0.014337888977278956 mean_absolute_error 0.014638863164198292 mean_absolute_error 0.014653301206118782 mean_absolute_error 0.01466419695026537 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 0.7 predictive_accuracy 0.73125 predictive_accuracy 0.79375 predictive_accuracy 0.725 predictive_accuracy 0.75625 predictive_accuracy 0.74375 predictive_accuracy 0.8 predictive_accuracy 0.71875 predictive_accuracy 0.73125 predictive_accuracy 0.71875 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 0.7 [1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0,1,1,1,0,0.5,0,1,1,0,1,0,0.5,0,1,0.5,0,0.5,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,0,0,1,0.5,1,0,1,0.5,0.5,1,1,0,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,0.5,0.5,1,0.5,0,1] recall 0.73125 [1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,0.5,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,0.5,1,0,1,1,0,0.5,1,1,1,0.5,1,1,0,1,0,0,0,1,1,0.5,0,0.5,0.5,0.5,1,0,0,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] recall 0.79375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,0,0,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,0,0,1,1,0.5,1,1,1,0.5,0.5,0,0.5,0,1,1,1,1,0.5,1,0.5,1,1,0,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1] recall 0.725 [0.5,0.5,1,1,0.5,0,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0,0,0,0,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,0.5,0,0.5,0,1,0,0.5,1,0.5,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,0.5,1,0.5] recall 0.75625 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,1,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,0.5,0.5,0.5,1,1,1,0,1,1,0,1,1,1,1,1,0.5,0,0,1,1,1,1,1,0.5,1,1,0.5,1,1,0,0,1] recall 0.74375 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,1,0.5,1,1,0,0.5,0,1,1,1,1,0.5,0,1,0.5,0.5,1,0.5,0.5,1,1,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,0.5,1,1,1,0,1,1,1,1,0,0,1] recall 0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,0,0,0.5,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,0,1,1,1,1,0.5,1,0,0.5,0.5,1,0.5,0,0,1,1,1,1,1,1,0,0.5,1,0.5,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.71875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,0.5,0,1,0.5,0,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,0.5,1,1,0.5,1,1,0.5,0,1,1,0,1,1,0,1,0.5,0,0.5,1,1,0.5,1,0.5,0,1,1,1,1,0.5,0,0,0.5,0,0.5,1,0,1,1,1,1,1,0,1,0.5,0,0,1,0.5] recall 0.73125 [0,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0,1,0.5,0.5,0.5,1,1,1,0,1,1,0.5,1,0.5,0.5,0,1,0,0.5,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0,1,0.5,0,1,0,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1] recall 0.71875 [0,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,0,0,1,1,1,1,1,0,0,0.5,1,0,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0.5,1,0.5,0,1,0.5,1,1,0,0.5,1,1,0,0,0,1,1,0,1,1,1,0.5,0,0,0,0,1,0,1,0,0.5,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1] relative_absolute_error 0.7433559222918305 relative_absolute_error 0.7388988147380776 relative_absolute_error 0.7350448354535942 relative_absolute_error 0.7373422002712391 relative_absolute_error 0.7317239712732817 relative_absolute_error 0.7378591577351525 relative_absolute_error 0.7241358069332794 relative_absolute_error 0.739336523444357 relative_absolute_error 0.7400657174807453 relative_absolute_error 0.7406160075891589 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 0.07879941032168541 root_mean_squared_error 0.07821339672670702 root_mean_squared_error 0.07760250650270503 root_mean_squared_error 0.07797824768287119 root_mean_squared_error 0.07775391130970724 root_mean_squared_error 0.07819838033907739 root_mean_squared_error 0.07663394751605682 root_mean_squared_error 0.07823440055021731 root_mean_squared_error 0.07818402782025576 root_mean_squared_error 0.07856317554884877 root_relative_squared_error 0.7919638719342086 root_relative_squared_error 0.7860742137021158 root_relative_squared_error 0.7799345359411741 root_relative_squared_error 0.7837108768893453 root_relative_squared_error 0.7814562115056666 root_relative_squared_error 0.7859232933279522 root_relative_squared_error 0.7702001518622681 root_relative_squared_error 0.7862853100710445 root_relative_squared_error 0.7857790450863525 root_relative_squared_error 0.7895896231344088 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 3288.3053689993176 usercpu_time_millis 3226.9820939991405 usercpu_time_millis 3282.4867310000627 usercpu_time_millis 3230.602139000439 usercpu_time_millis 3281.181889000436 usercpu_time_millis 3272.9788030001146 usercpu_time_millis 3192.8698789997725 usercpu_time_millis 3203.591401000267 usercpu_time_millis 3216.980889000297 usercpu_time_millis 3242.5182010001663 usercpu_time_millis_testing 39.833365000049525 usercpu_time_millis_testing 39.62104599941085 usercpu_time_millis_testing 40.35154700068233 usercpu_time_millis_testing 39.20623100020748 usercpu_time_millis_testing 38.95539800032566 usercpu_time_millis_testing 39.449784000680665 usercpu_time_millis_testing 39.404541999829235 usercpu_time_millis_testing 39.11822200007009 usercpu_time_millis_testing 39.25799500029825 usercpu_time_millis_testing 39.14837299998908 usercpu_time_millis_training 3248.472003999268 usercpu_time_millis_training 3187.3610479997296 usercpu_time_millis_training 3242.1351839993804 usercpu_time_millis_training 3191.3959080002314 usercpu_time_millis_training 3242.2264910001104 usercpu_time_millis_training 3233.529018999434 usercpu_time_millis_training 3153.4653369999432 usercpu_time_millis_training 3164.473179000197 usercpu_time_millis_training 3177.7228939999986 usercpu_time_millis_training 3203.369828000177