10111153 1 Jan van Rijn 9954 Supervised Classification 8815 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,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)(1) 8036426 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "median" 8778 verbose 0 8778 copy true 8779 with_mean true 8779 with_std true 8779 memory null 8780 copy true 8781 fill_value -1 8781 missing_values NaN 8781 strategy "constant" 8781 verbose 0 8781 categorical_features null 8782 categories null 8782 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 8782 handle_unknown "ignore" 8782 n_values null 8782 sparse true 8782 class_weight null 8783 criterion "entropy" 8783 max_depth null 8783 max_features 1.0 8783 max_leaf_nodes null 8783 min_impurity_decrease 0.0 8783 min_impurity_split null 8783 min_samples_leaf 11 8783 min_samples_split 10 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 7913 8783 splitter "best" 8783 n_jobs null 8812 remainder "passthrough" 8812 sparse_threshold 0.3 8812 transformer_weights null 8812 memory null 8813 memory null 8815 threshold 0.0 8816 openml-python Sklearn_0.20.0. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 21142844 description https://api.openml.org/data/download/21142844/description.xml -1 21142845 predictions https://api.openml.org/data/download/21142845/predictions.arff area_under_roc_curve 0.8386685211489899 [0.829072,0.869792,0.904928,0.897116,0.900825,0.861328,0.80159,0.897293,0.867089,0.837851,0.931897,0.898497,0.833491,0.76233,0.837654,0.823213,0.905086,0.791746,0.794291,0.727884,0.898773,0.83361,0.833787,0.999901,0.801018,0.754399,0.82631,0.835247,0.835346,0.872238,0.987393,0.934738,0.834793,0.817827,0.900272,0.870482,0.795316,0.760989,0.9971,0.965534,0.735519,0.861545,0.871015,0.862926,0.867878,0.764639,0.935823,0.806483,0.727667,0.834497,0.955275,0.824317,0.929096,0.74785,0.76959,0.687441,0.967744,0.92949,0.829013,0.790443,0.77474,0.766927,0.927616,0.640704,0.836233,0.893841,0.788845,0.933495,0.662622,0.724452,0.749112,0.933771,0.80236,0.863991,0.648891,0.930851,0.901002,0.687165,0.900174,0.897865,0.896484,0.767657,0.766907,0.79727,0.857264,0.835938,0.826941,0.854344,0.734612,0.834261,0.965495,0.96437,0.824692,0.786813,0.863557,0.865136,0.861012,0.799025,0.680299,0.793817] average_cost 0 f_measure 0.4054536772224329 [0.333333,0.628571,0.785714,0.296296,0.5,0.275862,0.322581,0.571429,0.258065,0.428571,0.514286,0.540541,0.457143,0.068966,0.611111,0.214286,0.702703,0.217391,0.4,0.230769,0.545455,0.516129,0.35,0.941176,0.424242,0.2,0.258065,0.5,0.592593,0.685714,0.363636,0.756757,0.470588,0.210526,0.571429,0.727273,0.296296,0.216216,0.740741,0.666667,0.285714,0.333333,0.666667,0.484848,0.645161,0.142857,0.742857,0.484848,0.206897,0.47619,0.465116,0.243902,0.424242,0.16,0.333333,0,0.882353,0.487805,0.193548,0.26087,0.4,0.342857,0.461538,0.266667,0.516129,0.368421,0.068966,0.5625,0.216216,0.111111,0.0625,0.705882,0.378378,0.451613,0.083333,0.529412,0.592593,0.076923,0.666667,0.516129,0.62069,0.307692,0.4,0.133333,0.341463,0.545455,0.24,0.086957,0.2,0.378378,0.666667,0.611111,0.444444,0.173913,0.322581,0.428571,0.424242,0.16,0,0.37037] kappa 0.41035353535353536 kb_relative_information_score 865.3629284223003 mean_absolute_error 0.013382874893102177 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 0.41230647364057654 [0.357143,0.578947,0.916667,0.363636,0.5,0.307692,0.333333,0.526316,0.266667,0.5,0.473684,0.47619,0.421053,0.076923,0.55,0.25,0.619048,0.166667,0.428571,0.3,0.529412,0.533333,0.291667,0.888889,0.411765,0.214286,0.266667,0.5,0.727273,0.631579,0.352941,0.666667,0.444444,0.181818,0.526316,0.705882,0.363636,0.190476,0.909091,0.6,0.333333,0.269231,0.714286,0.470588,0.666667,0.166667,0.684211,0.470588,0.230769,0.384615,0.37037,0.2,0.411765,0.222222,0.357143,0,0.833333,0.4,0.2,0.428571,0.428571,0.315789,0.391304,0.285714,0.533333,0.318182,0.076923,0.5625,0.190476,0.1,0.0625,0.666667,0.333333,0.466667,0.125,0.5,0.727273,0.1,0.818182,0.533333,0.692308,0.4,0.428571,0.142857,0.28,0.529412,0.333333,0.142857,0.5,0.333333,0.6,0.55,0.545455,0.285714,0.333333,0.346154,0.411765,0.222222,0,0.454545] predictive_accuracy 0.41625 prior_entropy 6.6438561897747395 recall 0.41625 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[0.996835,0.993711,1,0.477848,1,0.716772,0.993671,0.996835,0.493711,0.996835,0.993711,0.487421,0.732595,0.981132,0.481132,0.468354,1,0.465409,0.971519,0.481013,0.977848,0.737342,0.732595,1,0.990506,0.440252,0.45283,0.738924,1,0.742089,0.981013,0.996855,1,1,0.740506,1,0.737342,0.984177,0.993671,0.996855,0.735759,0.735759,0.993671,0.490506,1,0.735759,1,1,0.962264,0.493711,0.974843,0.723101,0.737342,1,0.746835,0.723101,1,0.988924,0.484177,0.996855,0.493671,0.737342,0.984277,0.738924,0.746835,0.984277,0.993711,0.743671,0.481013,0.732595,0.465409,1,0.996835,0.468553,0.721519,1,0.977987,0.940252,1,0.962264,0.968354,1,0.732595,0.993671,0.471519,0.738924,0.990506,0.481132,1,0.737342,1,0.996835,0.72943,0.965409,0.474843,0.987342,0.984177,0.990566,0.697785,0.727848] area_under_roc_curve 0.8615977778441211 [0.471698,0.995253,1,1,0.996835,0.990566,0.732595,0.955975,0.974684,1,0.490566,0.996855,0.996835,0.745253,0.996835,0.968553,0.993711,0.993711,1,0.949686,0.993671,1,0.987421,1,0.748418,0.981132,0.465409,0.990506,0.742089,0.996855,0.990506,0.996835,0.949686,0.937107,0.996835,1,0.740506,0.721519,0.993671,0.996855,0.487421,0.727848,0.746835,0.735759,0.487342,0.493671,0.990566,0.97943,0.465409,0.996855,0.987342,0.982595,0.993671,0.705696,0.740506,0.471698,0.75,0.981132,0.742089,0.710443,0.745253,0.993671,0.988924,0.735759,1,0.987421,0.97943,0.5,0.732595,0.987421,0.984277,0.984277,0.742089,0.993671,0.455696,0.732595,0.996855,0.471698,1,1,1,0.990566,0.477987,0.987421,0.976266,1,0.958861,0.977848,0.471698,0.987342,1,0.746835,0.996835,0.707278,0.993711,0.726266,0.955975,0.727848,0.732595,1] area_under_roc_curve 0.863410805270281 [0.962264,0.484177,0.496855,0.966772,1,0.987421,0.737342,0.484277,0.735759,0.993711,1,0.993711,0.993671,0.987342,0.742089,1,1,1,0.993671,0.996855,1,0.996835,1,1,1,0.459119,0.996855,0.743671,0.484177,0.996855,0.985759,0.75,1,0.946541,0.992089,0.732595,0.726266,0.996835,1,1,0.959119,0.981013,0.998418,0.743671,1,0.738924,1,0.471519,0.993711,0.490566,0.995253,0.732595,0.996835,0.955696,0.738924,0.45283,1,0.987421,0.471519,1,0.993671,0.731013,0.993671,0.724684,0.990566,0.5,0.94462,0.993711,0.985759,0.462264,0.471698,0.490566,0.737342,0.996835,0.734177,0.995253,1,0.474843,0.477987,0.743671,1,0.493711,0.996855,0.484277,0.987342,1,0.72943,0.969937,0.455975,0.990506,1,0.996835,0.996835,0.737342,1,1,1,0.996835,0.976266,0.984277] 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 0.44415933046464806 kappa 0.4379317150187487 kappa 0.40003947108742843 kappa 0.40630797773654914 kappa 0.4124615020137409 kappa 0.4002998500749625 kappa 0.34954215345753076 kappa 0.35593354118157783 kappa 0.4001815240124699 kappa 0.4947102479077846 kb_relative_information_score 89.77149562201704 kb_relative_information_score 90.1624253792219 kb_relative_information_score 82.81890028795964 kb_relative_information_score 87.51139141986737 kb_relative_information_score 80.24493357077337 kb_relative_information_score 86.09231858690346 kb_relative_information_score 82.96544121447403 kb_relative_information_score 80.46088414880147 kb_relative_information_score 89.92012874213064 kb_relative_information_score 95.41500945015949 mean_absolute_error 0.012833338202475206 mean_absolute_error 0.012949762941844018 mean_absolute_error 0.013452081632785001 mean_absolute_error 0.013511929535033254 mean_absolute_error 0.013703870554862243 mean_absolute_error 0.01345262408246638 mean_absolute_error 0.014090985156484387 mean_absolute_error 0.013789927364946719 mean_absolute_error 0.013501989062171909 mean_absolute_error 0.01254224039795344 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 0.45 predictive_accuracy 0.44375 predictive_accuracy 0.40625 predictive_accuracy 0.4125 predictive_accuracy 0.41875 predictive_accuracy 0.40625 predictive_accuracy 0.35625 predictive_accuracy 0.3625 predictive_accuracy 0.40625 predictive_accuracy 0.5 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 0.45 [0,0.5,1,0,0.5,1,0.5,1,0,0,1,0.5,0,0,1,1,1,0,0,0,1,0,1,1,0,0.5,0.5,0.5,0.5,0,1,1,0.5,0,1,0,0,0,1,1,1,0,0.5,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,1,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,0.5,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,1,1,1,0,0.5,0.5,0,0,0,1] recall 0.44375 [1,1,0.5,1,0,1,0.5,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,0,0.5,1,1,0,0,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0,1,0,0,0,1,0,1,1,0.5,0.5,0,1,0,0,1,0,0.5,1,0.5,1,0,1,1,0,0,1,1,1,0,1,1,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0,0,0,0,0.5,1,0,0,0.5,0.5,1,0,0,0] recall 0.40625 [0,0.5,0,1,0.5,0,0,1,0.5,0,0.5,0.5,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,0,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,1,0,1,0.5,0,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0,1,0,0.5,0,0,1,0,0,1,0,0,1,0,1,0,0,0,0,1,0.5,1,0,0.5,0.5,1,0,0.5,0,0.5] recall 0.4125 [0.5,1,1,0,0.5,0,1,0.5,0,0,1,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0,0.5,1,1,0.5,0.5,1,0,1,0,1,1,0,0,1,0,0.5,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,1,0,1,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0,0,1,0.5,0,0.5,1,0,0,0,1,0,0,0,1,0,0,0,0,0,0.5,0,0,0] recall 0.41875 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,0,0.5,0,0,1,0.5,0.5,1,1,1,0,0,1,1,0.5,0,0,1,1,0.5,1,1,0,0,1,1,0.5,0,0.5,1,1,0.5,0,1,1,0,1,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0.5,0,0,0,0,0,0,1,1,0,0,1,0.5,0,0.5,0,0,1,0,0.5,0,1,0,1,1,0.5,0,1,0,0,0.5,0,0.5,1,0,0] recall 0.40625 [0,1,1,0,1,0,0,0.5,1,0.5,0.5,1,1,0,1,0,1,0.5,0,0,1,0.5,0,1,0.5,0,0.5,1,1,1,0,1,0,0,0,1,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0.5,1,0,0,0,0,0,1,1,1,0,0.5,0,0,1,0,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0.5,0,0,0.5] recall 0.35625 [0.5,0,1,0,1,0,0,1,0,0,1,1,0.5,0,0,0,0,0,0,0,0.5,0.5,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,0.5,0,0,0,0.5,0,1,0,0.5,0.5,0,1,1,0,0.5,0,0,0,1,0.5,0,0,0,0,1,0,0,0,0,0.5,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,1,0,0,0,0.5,1,1,0.5,0,0,0.5,0.5,0,0,0.5] recall 0.3625 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0.5,0,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,0,0.5,1,0,1,0,1,1,0,0,0,0,0.5,1,0,0,1,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0,0,0,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0] recall 0.40625 [0,1,1,0,0,1,0.5,0,0,1,0,1,1,0,1,0,1,0,1,0,0.5,1,1,1,0.5,0,0,0.5,0.5,1,0,1,0,0,1,1,0.5,0,0,0,0,0.5,0,0.5,0,0,1,0.5,0,1,0.5,0.5,1,0,0,0,0.5,0,0,0,0,1,0.5,0.5,1,0,0,0,0.5,0,1,0,0.5,0,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,0,1,0,0.5,0,1,0.5,0,0,0,1] recall 0.5 [0,0,0,0,1,0,0,0,0.5,0,1,1,1,0,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,1,1,1,0,0.5,0.5,0.5,1,0,1,0,1,0,1,0,1,0,0,0,1,1,0,1,1,0,1,0,1,0,0,1,0.5,0,0,0,0,1,0.5,0.5,1,0,0,0.5,1,0,1,0,0.5,1,0,0,0,0.5,1,1,1,0.5,0,1,1,0,0,0] relative_absolute_error 0.6481483940644033 relative_absolute_error 0.6540284314062624 relative_absolute_error 0.6793980622618676 relative_absolute_error 0.6824206835875369 relative_absolute_error 0.692114674487991 relative_absolute_error 0.6794254587104221 relative_absolute_error 0.7116659169941597 relative_absolute_error 0.6964609780276109 relative_absolute_error 0.6819186395036305 relative_absolute_error 0.6334464847451222 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 0.08711950530460409 root_mean_squared_error 0.08660146021397032 root_mean_squared_error 0.09196972716135479 root_mean_squared_error 0.0891680601602747 root_mean_squared_error 0.09172444527430204 root_mean_squared_error 0.08990751006324724 root_mean_squared_error 0.09236885888622237 root_mean_squared_error 0.09129114699200075 root_mean_squared_error 0.08903085574423 root_mean_squared_error 0.08452962992109363 root_relative_squared_error 0.8755839727780258 root_relative_squared_error 0.8703774237170626 root_relative_squared_error 0.9243305365623377 root_relative_squared_error 0.8961727237438442 root_relative_squared_error 0.9218653608434764 root_relative_squared_error 0.9036044748936156 root_relative_squared_error 0.9283419613299532 root_relative_squared_error 0.9175105492534797 root_relative_squared_error 0.8947937674783895 root_relative_squared_error 0.849554745805656 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 166.3528689987288 usercpu_time_millis 151.44001800035767 usercpu_time_millis 147.96078199924523 usercpu_time_millis 111.17120600010821 usercpu_time_millis 112.92195000078209 usercpu_time_millis 113.8778500007902 usercpu_time_millis 112.87329900005716 usercpu_time_millis 116.3152720000653 usercpu_time_millis 113.78908000096999 usercpu_time_millis 112.84121800053981 usercpu_time_millis_testing 1.8056189992421423 usercpu_time_millis_testing 1.8047410003418918 usercpu_time_millis_testing 1.7540290000397363 usercpu_time_millis_testing 1.4057970001886133 usercpu_time_millis_testing 1.5626530002919026 usercpu_time_millis_testing 1.623064000341401 usercpu_time_millis_testing 1.3697089998458978 usercpu_time_millis_testing 1.4346580001074472 usercpu_time_millis_testing 1.2619960007214104 usercpu_time_millis_testing 1.275041000553756 usercpu_time_millis_training 164.54724999948667 usercpu_time_millis_training 149.63527700001578 usercpu_time_millis_training 146.2067529992055 usercpu_time_millis_training 109.7654089999196 usercpu_time_millis_training 111.35929700049019 usercpu_time_millis_training 112.2547860004488 usercpu_time_millis_training 111.50359000021126 usercpu_time_millis_training 114.88061399995786 usercpu_time_millis_training 112.52708400024858 usercpu_time_millis_training 111.56617699998606