10012865 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) 7937586 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 "median" 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 0.6859992664120714 8919 max_leaf_nodes null 8919 min_impurity_decrease 0.0 8919 min_impurity_split null 8919 min_samples_leaf 9 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 12504 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 20946253 description https://api.openml.org/data/download/20946253/description.xml -1 20946254 predictions https://api.openml.org/data/download/20946254/predictions.arff area_under_roc_curve 0.9891915246212121 [0.980074,0.996883,0.99854,0.999171,0.999527,0.994437,0.996883,0.996765,0.99637,0.999803,0.992069,0.994871,0.997159,0.974195,0.998895,0.992779,0.998461,0.977352,0.983467,0.977233,0.998185,0.998895,0.995305,0.999763,0.997435,0.952257,0.952671,0.994121,0.974096,0.999487,0.997041,0.959221,0.995462,0.980666,0.996528,0.994318,0.990807,0.973603,0.996054,0.998777,0.99858,0.998737,0.995857,0.989583,0.995699,0.996765,0.992937,0.998224,0.99349,0.989741,0.995778,0.984178,0.994081,0.97459,0.982481,0.971985,0.999388,0.993213,0.988163,0.966698,0.999763,0.996686,0.955196,0.990767,0.998264,0.990096,0.979956,0.995778,0.945707,0.981613,0.978693,0.996409,0.987098,0.994239,0.964725,0.996488,0.998461,0.977588,0.99783,0.995818,0.990688,0.991162,0.996488,0.98984,0.991832,0.984888,0.997001,0.97893,0.99637,0.999014,0.998185,0.998185,0.994594,0.986387,0.988479,0.992148,0.99416,0.985598,0.955926,0.998303] average_cost 0 f_measure 0.6209452522827066 [0.486486,0.594595,0.848485,0.785714,0.8,0.685714,0.555556,0.645161,0.666667,0.9375,0.551724,0.764706,0.702703,0.571429,0.769231,0.666667,0.857143,0.387097,0.384615,0.137931,0.666667,0.758621,0.578947,0.888889,0.75,0,0.076923,0.580645,0.742857,0.810811,0.8,0.742857,0.787879,0.30303,0.611111,0.6,0.432432,0.285714,0.571429,0.857143,0.777778,0.848485,0.722222,0.647059,0.615385,0.705882,0.666667,0.75,0.685714,0.727273,0.594595,0.514286,0.5625,0.416667,0.666667,0.30303,0.969697,0.727273,0.342857,0.32,0.882353,0.75,0.529412,0.628571,0.742857,0.555556,0.285714,0.551724,0.272727,0.48,0.315789,0.689655,0.6,0.827586,0.413793,0.774194,0.8,0.615385,0.848485,0.714286,0.580645,0.645161,0.514286,0.4,0.645161,0.740741,0.857143,0.580645,0.722222,0.823529,0.777778,0.8,0.689655,0.461538,0.740741,0.628571,0.413793,0.5,0.285714,0.8] kappa 0.6281565656565656 kb_relative_information_score 1130.4166676896834 mean_absolute_error 0.012659360219008937 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 0.6357760278189676 [0.428571,0.52381,0.823529,0.916667,0.736842,0.631579,0.5,0.666667,0.714286,0.9375,0.615385,0.722222,0.619048,0.666667,0.652174,0.714286,0.789474,0.4,0.5,0.153846,0.565217,0.846154,0.5,0.8,0.75,0,0.1,0.6,0.684211,0.714286,0.736842,0.684211,0.764706,0.294118,0.55,0.5,0.380952,0.263158,0.666667,1,0.7,0.823529,0.65,0.611111,0.8,0.666667,0.647059,0.75,0.631579,0.705882,0.52381,0.473684,0.5625,0.625,0.647059,0.294118,0.941176,0.705882,0.315789,0.444444,0.833333,0.625,0.5,0.578947,0.684211,0.5,0.263158,0.615385,0.5,0.666667,1,0.769231,0.642857,0.923077,0.461538,0.8,0.736842,0.8,0.823529,0.833333,0.6,0.666667,0.473684,0.368421,0.666667,0.909091,1,0.6,0.65,0.777778,0.7,0.736842,0.769231,0.6,0.909091,0.578947,0.461538,0.583333,0.6,0.736842] predictive_accuracy 0.631875 prior_entropy 6.6438561897747395 recall 0.631875 [0.5625,0.6875,0.875,0.6875,0.875,0.75,0.625,0.625,0.625,0.9375,0.5,0.8125,0.8125,0.5,0.9375,0.625,0.9375,0.375,0.3125,0.125,0.8125,0.6875,0.6875,1,0.75,0,0.0625,0.5625,0.8125,0.9375,0.875,0.8125,0.8125,0.3125,0.6875,0.75,0.5,0.3125,0.5,0.75,0.875,0.875,0.8125,0.6875,0.5,0.75,0.6875,0.75,0.75,0.75,0.6875,0.5625,0.5625,0.3125,0.6875,0.3125,1,0.75,0.375,0.25,0.9375,0.9375,0.5625,0.6875,0.8125,0.625,0.3125,0.5,0.1875,0.375,0.1875,0.625,0.5625,0.75,0.375,0.75,0.875,0.5,0.875,0.625,0.5625,0.625,0.5625,0.4375,0.625,0.625,0.75,0.5625,0.8125,0.875,0.875,0.875,0.625,0.375,0.625,0.6875,0.375,0.4375,0.1875,0.875] relative_absolute_error 0.6393616272226724 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07403976778752809 root_relative_squared_error 0.7441276645947655 total_cost 0 area_under_roc_curve 0.9872691266618899 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[0.987421,0.993671,1,1,1,0.993711,0.987342,0.993711,1,1,0.981132,1,1,1,1,1,1,1,1,0.993711,1,0.996835,0.968553,1,1,0.861635,0.962264,1,1,1,1,1,1,1,0.996835,1,1,0.955696,1,0.993711,1,0.990506,0.996835,0.996835,0.993671,1,1,1,0.987421,1,1,0.984177,0.996835,0.946203,0.977848,0.962264,1,0.993711,1,0.993671,1,1,0.996835,0.987342,1,1,0.977848,1,0.955696,1,1,0.981132,0.987342,1,0.984177,0.987342,1,0.987421,1,0.993671,1,1,1,0.987421,1,1,1,0.943038,0.981132,1,1,1,1,0.96519,0.968553,0.993671,0.993711,0.993671,0.962025,1] area_under_roc_curve 0.9892238376721597 [0.855346,1,0.968553,1,1,0.993711,1,0.981132,0.996835,1,1,1,0.984177,0.949367,1,0.981132,1,0.981132,0.993671,1,1,1,1,1,1,0.886792,0.993711,0.987342,0.993671,1,1,1,1,0.987421,0.990506,0.993671,0.993671,0.990506,0.993671,1,1,1,1,1,1,1,1,0.996835,1,0.993711,0.996835,0.996835,1,0.977848,1,0.974843,1,1,0.993671,0.990506,1,1,0.727848,0.996835,1,1,0.977848,0.993711,0.981013,0.949686,0.955975,1,0.971519,1,1,0.996835,1,1,1,0.993671,0.996835,0.981132,1,0.968553,0.996835,0.993671,1,0.987342,1,0.996835,1,1,0.993671,1,1,1,0.974843,1,0.996835,1] 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 0.5389958951689295 kappa 0.6337083086639036 kappa 0.6463669732012473 kappa 0.6715746259819209 kappa 0.6462413139608338 kappa 0.6336938501618378 kappa 0.6651397883430737 kappa 0.6398530979741737 kappa 0.6273046705357495 kappa 0.5768198326227697 kb_relative_information_score 111.01513459820862 kb_relative_information_score 111.7130366903828 kb_relative_information_score 113.61125147465789 kb_relative_information_score 115.02774303904384 kb_relative_information_score 111.5172232194959 kb_relative_information_score 113.90773938088361 kb_relative_information_score 117.31896424477956 kb_relative_information_score 112.73595512876949 kb_relative_information_score 112.47624730195628 kb_relative_information_score 111.09337261150773 mean_absolute_error 0.01259266414569907 mean_absolute_error 0.012683801980290954 mean_absolute_error 0.0126441373232568 mean_absolute_error 0.012695390219869993 mean_absolute_error 0.012629846268417039 mean_absolute_error 0.01274915900816912 mean_absolute_error 0.012264311404077948 mean_absolute_error 0.01258168521052069 mean_absolute_error 0.012781169567156695 mean_absolute_error 0.012971437062630995 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 0.6375 predictive_accuracy 0.65 predictive_accuracy 0.675 predictive_accuracy 0.65 predictive_accuracy 0.6375 predictive_accuracy 0.66875 predictive_accuracy 0.64375 predictive_accuracy 0.63125 predictive_accuracy 0.58125 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 [1,0.5,1,1,0,1,0.5,1,0,1,0,0.5,0.5,0,1,0,1,0,0.5,0,0,0,0,1,0,0,0,1,0.5,1,0.5,1,0.5,0,1,0,1,1,1,0.5,1,1,1,0.5,0,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,1,0,0,1,1,0.5,0.5,1,0.5,0.5,1,0,0,0,0,0,1,0,1,0.5,0.5,1,1,0,1,1,1,0.5,0.5,0,1,0.5,0,1,1,0.5,0.5,0.5,0.5,1,0,0,1] recall 0.6375 [1,1,1,1,1,0,0.5,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,0.5,0,1,1,1,0,1,1,0,0,0,1,1,1,1,0.5,0.5,0,0,0,1,0,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,0.5,1,0,1,1,0,0,1,1,1,0.5,1,0,0,1,0,0,0,1,1,1,0,1,0.5,0.5,1,0,0.5,0.5,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,0] recall 0.65 [1,0.5,1,0,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,0,0,0,1,1,0.5,1,1,0,0,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,0,1,0.5,1,0.5,1,0.5,1,1,0,1,0,1,1,0,0.5,1,1,0.5,1,0.5,1,0.5,0.5,0,1,0,1,1,0.5,0,1,1,0.5,0,1,0,0.5,0.5,0,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,0,0.5] recall 0.675 [0.5,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0,0,0,0,1,1,1,0.5,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0,1,1,0,0,1,0.5,1,0.5,1,0.5,1,0,0.5,1,0.5,1,0.5,0.5,0,0,0.5,0.5,1,1,1,0.5,1,0.5,1,0.5,1,0.5,1,0.5,1,0,1,0,1,1,1,1,0,0.5,1,1,0,1,0,1] recall 0.65 [0,0,1,0.5,1,1,0,0.5,1,1,0.5,0.5,0,0,1,1,1,0.5,0,0,1,1,1,1,1,0,0.5,1,1,0.5,1,0,1,0,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,0,0.5,0,1,1,1,0,1,1,1,1,0.5,1,0,0,0,0.5,0.5,0.5,1,0,0.5,1,1,0.5,1,1,1,1,0,0.5,0,0,1,1,1,1,1,0,0,1,0.5,1,0.5,1,0,1] recall 0.6375 [0,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,1,1,0.5,0,0,1,1,1,1,1,0,0,0,1,1,1,1,0.5,0.5,0,0.5,0.5,0.5,0.5,0,1,1,0,1,1,1,0.5,0,1,0.5,1,0,0.5,1,0.5,0.5,1,1,0,1,1,1,0,1,1,0,1,1,0.5,0,0,0,1,1,0.5,0,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,0,1,1,1,0,1,0.5,1,1,0,0,1] recall 0.66875 [1,0,0,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,0.5,0.5,1,0,0,0,0.5,1,1,0.5,0,1,0,1,1,0.5,1,1,1,0.5,1,1,0.5,0,0.5,0,0.5,1,1,0,1,1,0,0.5,0.5,1,0.5,0,0,1,1,1,0.5,1,1,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,1,1,0,0,0,0,1,1,0,1] recall 0.64375 [1,1,1,0.5,1,0.5,1,1,1,1,0,1,1,1,0,0.5,1,0,0.5,0.5,0.5,0.5,1,1,1,0,0,0.5,0,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,0.5,1,0.5,0.5,1,1,0,0,1,1,1,1,1,0,1,0,0,0.5,0,1,0.5,1,0.5,1,1,1,1,1,0,0,0,0,0,0.5,1,0,1,1,0,1,1,0,1,0.5,0,0,0,1] recall 0.63125 [0,1,1,1,1,1,0,1,0.5,1,0,1,1,0.5,1,0,1,1,1,0,1,0.5,0,1,1,0,0,1,1,1,1,1,1,1,0.5,1,0,0,0.5,0,1,0.5,0.5,0.5,0,0.5,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,0.5,0.5,0.5,1,0.5,0.5,1,1,0,1,0.5,1,0,0,0,0.5,0,0.5,1,0,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0,0,0.5,0,0.5,0.5,1] recall 0.58125 [0,0.5,0,0.5,1,1,1,0,1,1,1,1,0.5,0.5,1,0,1,0,0,0,1,0.5,1,1,1,0,0,0.5,0.5,1,1,1,1,0,0.5,0.5,0.5,0,0,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0.5,1,0,0.5,1,0,1,0,1,0,1,1,0,0.5,1,1,0,0,0,0,0,1,0,0.5,0,1,1,0,1,0,1,0,1,0,1,0.5,0,0.5,1,0.5,1,1,1,0,0,1,0,0.5,0.5,1] relative_absolute_error 0.6359931386716692 relative_absolute_error 0.6405960596106531 relative_absolute_error 0.6385927941038776 relative_absolute_error 0.6411813242358572 relative_absolute_error 0.6378710236574251 relative_absolute_error 0.6438969196044999 relative_absolute_error 0.6194096668726226 relative_absolute_error 0.6354386469959934 relative_absolute_error 0.6455136145028624 relative_absolute_error 0.6551230839712613 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 0.07494129447472285 root_mean_squared_error 0.07374265712618128 root_mean_squared_error 0.07329513022420653 root_mean_squared_error 0.07392022742253912 root_mean_squared_error 0.07403674704233813 root_mean_squared_error 0.07371306952124551 root_mean_squared_error 0.07191248675350417 root_mean_squared_error 0.07358500869144913 root_mean_squared_error 0.07489780118024496 root_mean_squared_error 0.07627202959320385 root_relative_squared_error 0.7531883487157266 root_relative_squared_error 0.7411415900950635 root_relative_squared_error 0.7366437754967592 root_relative_squared_error 0.7429262387221277 root_relative_squared_error 0.744097304963304 root_relative_squared_error 0.7408442234768295 root_relative_squared_error 0.7227476857659881 root_relative_squared_error 0.7395571637108412 root_relative_squared_error 0.7527512246591218 root_relative_squared_error 0.7665627398773952 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 3534.766575998219 usercpu_time_millis 3467.673150000337 usercpu_time_millis 3700.740494999991 usercpu_time_millis 3527.171427000212 usercpu_time_millis 3644.5042499999545 usercpu_time_millis 3563.5151459991903 usercpu_time_millis 3552.3842929997045 usercpu_time_millis 3541.2195960016106 usercpu_time_millis 3512.4516359992413 usercpu_time_millis 3544.4597250007064 usercpu_time_millis_testing 40.266738998980145 usercpu_time_millis_testing 40.90976299994509 usercpu_time_millis_testing 40.57716799979971 usercpu_time_millis_testing 40.53089800072485 usercpu_time_millis_testing 40.41951299950597 usercpu_time_millis_testing 41.11317100068845 usercpu_time_millis_testing 40.4178190001403 usercpu_time_millis_testing 40.61031700075546 usercpu_time_millis_testing 41.47617799935688 usercpu_time_millis_testing 40.76773300039349 usercpu_time_millis_training 3494.499836999239 usercpu_time_millis_training 3426.763387000392 usercpu_time_millis_training 3660.163327000191 usercpu_time_millis_training 3486.6405289994873 usercpu_time_millis_training 3604.0847370004485 usercpu_time_millis_training 3522.401974998502 usercpu_time_millis_training 3511.966473999564 usercpu_time_millis_training 3500.609279000855 usercpu_time_millis_training 3470.9754579998844 usercpu_time_millis_training 3503.691992000313