10105594 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) 8030815 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "most_frequent" 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 "gini" 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 12 8783 min_samples_split 8 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 23460 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 21131726 description https://api.openml.org/data/download/21131726/description.xml -1 21131727 predictions https://api.openml.org/data/download/21131727/predictions.arff area_under_roc_curve 0.8498674242424243 [0.858566,0.955709,0.899858,0.870068,0.834813,0.926689,0.866162,0.955216,0.804135,0.968178,0.804115,0.896484,0.803326,0.791686,0.931996,0.798256,0.966935,0.727825,0.778449,0.753018,0.957722,0.784209,0.827435,0.935093,0.89029,0.873441,0.772964,0.823587,0.899878,0.966777,0.867405,0.898615,0.894906,0.795395,0.925327,0.899384,0.826014,0.759075,0.830335,0.900075,0.830828,0.898418,0.902285,0.861841,0.794133,0.930694,0.902383,0.901061,0.8648,0.86484,0.82489,0.802616,0.860815,0.792752,0.825639,0.852766,0.968158,0.89536,0.953756,0.716501,0.898832,0.901397,0.926669,0.735105,0.931976,0.797072,0.848031,0.854976,0.713463,0.788688,0.742622,0.9317,0.830828,0.828993,0.756017,0.761324,0.964785,0.681167,0.93385,0.767479,0.822996,0.899996,0.692018,0.926551,0.793324,0.82414,0.90264,0.828973,0.895399,0.897885,0.93608,0.869318,0.821417,0.85539,0.793679,0.768663,0.825758,0.857915,0.582584,0.833097] average_cost 0 f_measure 0.41285792962854356 [0.363636,0.408163,0.714286,0.785714,0.514286,0.4375,0.571429,0.378378,0.518519,0.9375,0.457143,0.551724,0.45,0.272727,0.571429,0.4,0.774194,0.258065,0.074074,0,0.439024,0.230769,0.292683,0.8,0.421053,0.074074,0,0.137931,0.555556,0.682927,0.428571,0.666667,0.47619,0.230769,0.470588,0.514286,0.25,0.117647,0.375,0.606061,0.482759,0.625,0.65,0.4,0,0.545455,0.666667,0.580645,0.588235,0.5,0.307692,0.424242,0.451613,0.466667,0.294118,0.214286,0.9375,0.32,0.410256,0.133333,0.516129,0.611111,0.258065,0.4,0.625,0.30303,0.176471,0.1,0.1,0.1875,0,0.606061,0.470588,0.388889,0.230769,0.37037,0.722222,0.125,0.6875,0.482759,0.105263,0.709677,0.25,0.4,0.3125,0.272727,0.727273,0.384615,0.571429,0.387097,0.903226,0.628571,0.235294,0.322581,0.222222,0.451613,0.222222,0.153846,0,0.457143] kappa 0.4236111111111111 kb_relative_information_score 872.4725101568849 mean_absolute_error 0.01327696628399449 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 0.4170529767244468 [0.285714,0.30303,0.833333,0.916667,0.473684,0.4375,0.526316,0.333333,0.636364,0.9375,0.421053,0.615385,0.375,0.5,0.526316,0.368421,0.8,0.266667,0.090909,0,0.36,0.3,0.24,0.736842,0.363636,0.090909,0,0.153846,0.5,0.56,0.346154,0.647059,0.384615,0.3,0.444444,0.473684,0.208333,0.111111,0.375,0.588235,0.538462,0.625,0.541667,0.428571,0,0.428571,0.714286,0.6,0.555556,0.416667,0.26087,0.411765,0.466667,0.5,0.277778,0.25,0.9375,0.444444,0.347826,0.142857,0.533333,0.55,0.266667,0.333333,0.625,0.294118,0.166667,0.25,0.25,0.1875,0,0.588235,0.444444,0.35,0.3,0.454545,0.65,0.125,0.6875,0.538462,0.333333,0.733333,0.25,0.428571,0.3125,0.5,0.705882,0.5,0.526316,0.4,0.933333,0.578947,0.222222,0.333333,0.272727,0.466667,0.272727,0.2,0,0.421053] predictive_accuracy 0.429375 prior_entropy 6.6438561897747395 recall 0.429375 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[1,1,1,0.743671,0.993711,0.746835,1,0.993671,0.487421,1,0.481132,1,0.740506,0.45283,0.496855,1,1,0.468553,0.710443,0.721519,0.982595,0.474684,0.732595,0.996835,0.993671,0.996855,0.965409,0.995253,0.496855,0.732595,0.987342,1,1,0.962264,0.743671,0.998418,0.474684,0.962025,0.740506,0.996855,0.973101,1,0.993671,0.726266,0.468553,1,1,0.981013,0.484277,0.981132,0.977987,0.735759,0.977848,1,0.984177,0.727848,1,0.990506,0.735759,0.455975,0.993671,0.993671,0.987421,0.740506,0.996835,0.990566,0.959119,0.732595,0.721519,0.726266,0.930818,0.996835,0.988924,0.987421,0.72943,1,1,0.45283,0.996855,0.996855,0.724684,0.974843,0.976266,0.981013,0.455696,0.743671,0.990506,0.487421,0.996855,0.996835,0.487421,0.993671,0.993671,0.481132,0.984277,0.743671,0.740506,0.474843,0.462025,0.990506] area_under_roc_curve 0.8289229808534351 [0.987421,0.996835,1,0.996835,0.477848,0.996855,0.471519,0.996855,0.740506,1,0.984277,1,0.742089,0.977848,0.988924,0.468553,1,0.993711,0.481013,0.481132,1,0.995253,0.977987,0.75,0.742089,0.471698,0.437107,0.971519,0.996835,0.996855,0.988924,0.993671,0.981132,0.981132,0.992089,0.988924,0.974684,0.468354,0.993671,0.5,1,0.738924,0.745253,0.96519,0.732595,0.742089,1,1,0.962264,0.990566,0.992089,0.742089,0.731013,0.734177,0.723101,0.940252,1,0.481132,0.996835,0.46519,0.740506,0.75,0.993671,0.738924,0.490566,0.996855,0.713608,0.990566,0.702532,0.996855,0.930818,0.490566,0.988924,0.484177,0.723101,0.474684,0.996855,0.45283,1,0.740506,0.977848,1,0.471698,0.484277,0.993671,0.984177,0.996835,0.740506,0.481132,1,1,0.996835,0.996835,0.72943,0.474843,0.974684,0.95283,0.985759,0.685127,0.487421] area_under_roc_curve 0.8442674747233496 [0.465409,0.737342,0.993711,0.745253,1,0.984277,0.990506,0.977987,1,1,0.996855,0.996855,0.987342,0.726266,0.996835,0.490566,0.996855,0.481132,0.976266,0.974843,0.742089,0.677215,0.481132,1,0.992089,0.943396,0.487421,0.710443,0.981013,1,0.745253,1,0.990566,0.984277,0.982595,0.996835,0.742089,0.710443,0.477848,0.996855,0.484277,0.982595,0.740506,0.971519,0.740506,0.740506,0.996855,0.743671,0.996855,0.977987,0.743671,0.995253,0.735759,0.738924,0.996835,0.987421,1,0.487421,0.993671,0.735759,1,1,0.493671,0.743671,0.993711,0.987421,0.96519,0.981132,0.719937,0.487421,0.462264,0.987421,0.468354,0.721519,0.971519,0.977848,0.996855,0.940252,0.996855,0.471519,0.97943,0.984277,0.990566,0.484277,0.96519,0.735759,0.743671,0.977848,1,0.738924,1,0.996835,0.949367,0.72943,0.993711,0.996835,0.459119,0.987342,0.471519,1] 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 0.4063782759709504 kappa 0.48853545917360597 kappa 0.4191690013021347 kappa 0.43795389958951686 kappa 0.4315714680456322 kappa 0.3748766723232961 kappa 0.4567062818336163 kappa 0.41866434974921996 kappa 0.4063782759709504 kappa 0.39374802652352386 kb_relative_information_score 83.03157720951032 kb_relative_information_score 88.95910149620867 kb_relative_information_score 87.55314058158736 kb_relative_information_score 86.3136973545084 kb_relative_information_score 90.98639294542276 kb_relative_information_score 86.25375882448641 kb_relative_information_score 94.59098668497134 kb_relative_information_score 87.54916499549903 kb_relative_information_score 83.56161202939639 kb_relative_information_score 83.6730780353042 mean_absolute_error 0.013660487174438827 mean_absolute_error 0.012684433032959918 mean_absolute_error 0.013378012354810839 mean_absolute_error 0.013411961834362222 mean_absolute_error 0.012915120169204462 mean_absolute_error 0.013625074567156914 mean_absolute_error 0.012602354766607395 mean_absolute_error 0.013335416954056314 mean_absolute_error 0.013485368441328189 mean_absolute_error 0.01367143354502059 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 0.4125 predictive_accuracy 0.49375 predictive_accuracy 0.425 predictive_accuracy 0.44375 predictive_accuracy 0.4375 predictive_accuracy 0.38125 predictive_accuracy 0.4625 predictive_accuracy 0.425 predictive_accuracy 0.4125 predictive_accuracy 0.4 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 0.4125 [1,0.5,1,1,0,0,0.5,1,0,1,0,0,0.5,0,0.5,0,0,0,0.5,0,0,0,0,1,0,0,0,0,0.5,1,0.5,0.5,0.5,0,0.5,0,1,1,1,0.5,0,1,1,0.5,0,1,1,1,1,0.5,0,0.5,0,0,1,1,1,1,0,0,1,1,0,0.5,1,0,1,0,0,0,0,1,0,1,0,1,0.5,0,1,1,0,1,0,1,0,0,0,1,0.5,0,1,1,0,0.5,0,0,0,0,0,1] recall 0.49375 [0,1,0.5,1,0,0,1,1,0.5,1,0.5,0.5,0.5,0.5,0.5,1,0.5,0.5,0,0,1,0,0,1,1,0,0,0,1,1,0.5,1,0.5,0.5,0,0,0,1,0,1,1,1,1,0.5,0,1,0,1,0.5,1,1,0.5,1,0.5,0,0,1,1,1,0,1,0,0.5,0.5,1,0,0,0,0,1,0,1,0.5,0.5,0,0.5,0.5,0.5,1,0,0,0.5,0,1,0.5,0.5,1,0,1,0,0.5,1,0.5,1,0.5,0,0,0,0,1] recall 0.425 [1,0,1,0,1,0.5,1,0,0.5,1,1,0.5,1,0.5,1,1,1,0,0,0,0,0,0,1,0,0,0,0,0.5,1,1,1,0,0.5,1,0,1,0,1,1,0.5,0,1,1,0,1,0.5,1,0.5,1,0,0,0,1,0,0,1,0,0,0.5,0,1,0.5,1,0.5,0,0.5,0.5,0,0,0,1,1,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0,0.5,0,1,1,0,0,0,1,0,0,0,0] recall 0.44375 [0.5,1,0.5,1,0.5,0.5,1,0.5,0.5,1,0.5,0,0,0,1,0.5,0.5,0.5,0,0,0,0,1,0,0,0,0,0,1,1,1,0,1,0,0,1,1,0,0,1,0.5,0,1,1,0,1,0,1,0.5,0,1,1,0,0.5,0,0.5,1,0.5,1,0,1,0,0.5,0,0,1,0,0,0,0,0,0.5,1,0.5,0,0,1,0.5,1,0.5,0,0.5,0.5,0.5,1,0,1,0,1,1,1,1,0,0,0.5,1,0,0,0,0] recall 0.4375 [0.5,0,0.5,0.5,1,0,0,0,0,1,0.5,0,0,0,0,0,1,0.5,0,0,0.5,1,1,1,0.5,0,0,0,1,0.5,1,0,1,0,1,0.5,0.5,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0.5,1,0,0,0.5,0,0.5,0,1,0,0.5,0,0,1,0,1,0.5,1,0,0,0,0,0,1,1,1,0.5,0,0.5,0,0.5,1,0,1,0,0.5,0,0,1,1,1,0.5,1,0,0,0,0,0,0.5,1,0,0.5] recall 0.38125 [0,1,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,0,0,0.5,1,0,0,0,0.5,0.5,0.5,1,0.5,0,0,0,1,1,0,1,0.5,0.5,0,0.5,0,0,0,0,0.5,1,0,0,0,1,0.5,0,1,0.5,0,0,0.5,0,0,0,1,0,0.5,0,1,0.5,0,0,1,0,0,0,0,0,0,0,1,1,0.5,0,1,0,0.5,1,0,0.5,0,0.5,0,0,1,1,0,0.5,1,0,0,1,0.5,1,0,0,0,0.5] recall 0.4625 [0.5,0,0,0.5,1,1,0.5,0.5,0,1,1,1,1,1,1,0,1,0,0,0,1,0,0.5,1,0.5,0,0,0.5,0,1,0.5,0,1,0,1,1,0,0,0,1,0,0,1,1,0,1,0.5,0.5,1,1,0,0.5,0.5,0,0.5,0,0.5,0.5,0,0,0,1,1,0.5,0.5,0,0,0,0.5,0,0,0.5,1,1,0.5,0,1,0,1,0,0.5,1,0.5,0.5,0,0,1,0,0,0.5,1,1,0,0,0,0,1,0,0,1] recall 0.425 [1,1,1,0.5,1,0.5,1,1,0,1,0,1,0.5,0,0,1,1,0,0,0,0.5,0,0,1,1,1,0,0.5,0,0.5,0,1,1,0,0.5,0.5,0,0,0.5,1,0,1,1,0,0,0,1,0,0,0,0,0.5,0.5,1,0,0.5,1,0,0,0,0.5,1,0,0.5,1,0,0,0,0,0.5,0,1,0.5,0,0,1,1,0,0,1,0,0,0.5,0,0,0.5,1,0,1,1,0,0.5,0.5,0,0,0.5,0,0,0,0.5] recall 0.4125 [0,1,1,1,0,1,0,1,0.5,1,0,1,0.5,0,0.5,0,1,1,0,0,1,0,0,0.5,0.5,0,0,0,1,1,1,1,0,0,0.5,0.5,0,0,1,0,1,0.5,0.5,0,0,0.5,1,1,0,1,0.5,0.5,0.5,0.5,0,0,1,0,1,0,0,0,0,0.5,0,1,0,0,0,1,0,0,0,0,0,0,1,0,1,0.5,0,1,0,0,1,0.5,1,0.5,0,0,1,1,1,0.5,0,0.5,0,0,0,0] recall 0.4 [0,0.5,0,0.5,1,0,0.5,0,1,1,1,1,1,0,1,0,1,0,0,0,0.5,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0.5,1,0.5,0,0,0,0,0.5,0.5,0,0,0.5,1,0.5,1,0,0.5,0.5,0.5,0.5,1,0,1,0,1,0.5,1,1,0,0.5,1,0,0,0,0,0,0,0,0,0,0,0.5,1,0,1,0,0,0,1,0,0,0,0,0,1,0,1,1,0,0,0,1,0,0.5,0,1] relative_absolute_error 0.6899235946686265 relative_absolute_error 0.6406279309575705 relative_absolute_error 0.6756571896369098 relative_absolute_error 0.6773718098162728 relative_absolute_error 0.6522787964244666 relative_absolute_error 0.6881350791493379 relative_absolute_error 0.6364825639700694 relative_absolute_error 0.6735059067705198 relative_absolute_error 0.6810792142084933 relative_absolute_error 0.6904764416677054 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 0.09245885884161026 root_mean_squared_error 0.08610972901595285 root_mean_squared_error 0.08929802028345613 root_mean_squared_error 0.0895951291328895 root_mean_squared_error 0.08781003514015691 root_mean_squared_error 0.09048409057896747 root_mean_squared_error 0.08584226554272087 root_mean_squared_error 0.08892369312334071 root_mean_squared_error 0.09068558351321693 root_mean_squared_error 0.092549235998263 root_relative_squared_error 0.9292464949153181 root_relative_squared_error 0.8654353392275597 root_relative_squared_error 0.8974788721265748 root_relative_squared_error 0.900464928415862 root_relative_squared_error 0.8825240587509788 root_relative_squared_error 0.9093993271120208 root_relative_squared_error 0.8627472301795727 root_relative_squared_error 0.8937167426146292 root_relative_squared_error 0.9114244072963031 root_relative_squared_error 0.930154819516034 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 111.52186000072106 usercpu_time_millis 111.4599909997196 usercpu_time_millis 117.1666419995745 usercpu_time_millis 114.82076700121979 usercpu_time_millis 118.69550900155446 usercpu_time_millis 114.30325699984678 usercpu_time_millis 97.97134199834545 usercpu_time_millis 98.37971299930359 usercpu_time_millis 96.14630000214675 usercpu_time_millis 98.06697499880102 usercpu_time_millis_testing 1.516405000074883 usercpu_time_millis_testing 1.6003090004232945 usercpu_time_millis_testing 1.5775869997014524 usercpu_time_millis_testing 1.5581570005451795 usercpu_time_millis_testing 1.5728670005046297 usercpu_time_millis_testing 1.3350490007724147 usercpu_time_millis_testing 1.570779999383376 usercpu_time_millis_testing 1.3682389999303268 usercpu_time_millis_testing 1.3382490014919313 usercpu_time_millis_testing 1.32158199994592 usercpu_time_millis_training 110.00545500064618 usercpu_time_millis_training 109.8596819992963 usercpu_time_millis_training 115.58905499987304 usercpu_time_millis_training 113.26261000067461 usercpu_time_millis_training 117.12264200104983 usercpu_time_millis_training 112.96820799907437 usercpu_time_millis_training 96.40056199896208 usercpu_time_millis_training 97.01147399937327 usercpu_time_millis_training 94.80805100065481 usercpu_time_millis_training 96.7453929988551