10091902 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) 8017009 axis 0 8778 copy true 8778 missing_values "NaN" 8778 strategy "mean" 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 4 8783 min_samples_split 15 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 58200 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 21104341 description https://api.openml.org/data/download/21104341/description.xml -1 21104342 predictions https://api.openml.org/data/download/21104342/predictions.arff area_under_roc_curve 0.8102600220959596 [0.891098,0.867444,0.840475,0.901653,0.872179,0.868963,0.837851,0.899128,0.774562,0.968434,0.743115,0.899641,0.713404,0.799183,0.963542,0.806877,0.842132,0.700541,0.764639,0.764323,0.867069,0.66933,0.859572,0.936099,0.867562,0.606258,0.669882,0.830019,0.868391,0.871982,0.810152,0.902659,0.77614,0.676768,0.805516,0.839055,0.799992,0.702553,0.837101,0.931088,0.738025,0.838009,0.841442,0.7718,0.710444,0.837378,0.841185,0.902127,0.833925,0.806937,0.771208,0.742128,0.80449,0.673946,0.772451,0.733665,0.999625,0.839311,0.831696,0.637843,0.901417,0.903804,0.807134,0.801393,0.872179,0.674992,0.732915,0.931404,0.666469,0.699751,0.663569,0.872672,0.743687,0.839489,0.795908,0.741596,0.903981,0.701448,0.934028,0.742977,0.795553,0.872534,0.801156,0.835405,0.836845,0.80013,0.903389,0.834576,0.899917,0.836194,0.903527,0.840238,0.770814,0.859454,0.806739,0.710306,0.708827,0.864268,0.665621,0.775687] average_cost 0 f_measure 0.45690614701655535 [0.311111,0.44898,0.740741,0.785714,0.666667,0.529412,0.588235,0.444444,0.428571,0.9375,0.461538,0.516129,0.411765,0.357143,0.588235,0.5,0.689655,0.142857,0.228571,0.176471,0.424242,0.277778,0.243902,0.848485,0.444444,0.193548,0.0625,0.304348,0.466667,0.545455,0.666667,0.685714,0.470588,0.121212,0.5,0.545455,0.243902,0.242424,0.5,0.692308,0.413793,0.551724,0.733333,0.3125,0.090909,0.606061,0.709677,0.6875,0.444444,0.486486,0.375,0.388889,0.444444,0.296296,0.363636,0.222222,0.969697,0.580645,0.27027,0,0.647059,0.645161,0.484848,0.4,0.645161,0.142857,0.242424,0.466667,0.153846,0.171429,0.181818,0.733333,0.434783,0.666667,0.352941,0.37037,0.705882,0.214286,0.727273,0.518519,0.166667,0.592593,0.378378,0.285714,0.580645,0.357143,0.774194,0.461538,0.588235,0.551724,0.722222,0.666667,0.4375,0.368421,0.466667,0.461538,0.461538,0.45,0.148148,0.484848] kappa 0.4501262626262626 kb_relative_information_score 853.6555405021089 mean_absolute_error 0.011902027000776886 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 0.47473854081016187 [0.241379,0.333333,0.909091,0.916667,0.6,0.5,0.555556,0.4,0.5,0.9375,0.6,0.533333,0.388889,0.416667,0.555556,0.5,0.769231,0.166667,0.210526,0.166667,0.411765,0.25,0.2,0.823529,0.4,0.2,0.0625,0.233333,0.5,0.529412,0.714286,0.631579,0.444444,0.117647,0.45,0.529412,0.2,0.235294,0.583333,0.9,0.461538,0.615385,0.785714,0.3125,0.166667,0.588235,0.733333,0.6875,0.4,0.428571,0.375,0.35,0.545455,0.363636,0.352941,0.2,0.941176,0.6,0.238095,0,0.611111,0.666667,0.470588,0.428571,0.666667,0.166667,0.235294,0.5,0.2,0.157895,0.333333,0.785714,0.714286,0.818182,0.333333,0.454545,0.666667,0.25,0.705882,0.636364,0.15,0.727273,0.333333,0.263158,0.6,0.416667,0.8,0.6,0.555556,0.615385,0.65,0.714286,0.4375,0.318182,0.5,0.6,0.6,0.375,0.181818,0.470588] predictive_accuracy 0.455625 prior_entropy 6.6438561897747395 recall 0.455625 [0.4375,0.6875,0.625,0.6875,0.75,0.5625,0.625,0.5,0.375,0.9375,0.375,0.5,0.4375,0.3125,0.625,0.5,0.625,0.125,0.25,0.1875,0.4375,0.3125,0.3125,0.875,0.5,0.1875,0.0625,0.4375,0.4375,0.5625,0.625,0.75,0.5,0.125,0.5625,0.5625,0.3125,0.25,0.4375,0.5625,0.375,0.5,0.6875,0.3125,0.0625,0.625,0.6875,0.6875,0.5,0.5625,0.375,0.4375,0.375,0.25,0.375,0.25,1,0.5625,0.3125,0,0.6875,0.625,0.5,0.375,0.625,0.125,0.25,0.4375,0.125,0.1875,0.125,0.6875,0.3125,0.5625,0.375,0.3125,0.75,0.1875,0.75,0.4375,0.1875,0.5,0.4375,0.3125,0.5625,0.3125,0.75,0.375,0.625,0.5,0.8125,0.625,0.4375,0.4375,0.4375,0.375,0.375,0.5625,0.125,0.5] relative_absolute_error 0.6011124747867104 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.09123417885753826 root_relative_squared_error 0.9169379979594836 total_cost 0 area_under_roc_curve 0.7955793229042274 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[0.996855,0.996835,1,0.996835,1,0.993711,0.487342,0.996855,0.987342,1,0.487421,1,0.490506,0.982595,0.995253,1,1,0.487421,0.496835,0.487421,1,0.995253,0.984277,0.75,0.995253,0.481132,0.477987,0.976266,0.995253,1,0.746835,0.996835,0.493711,0.487421,0.993671,0.995253,0.735759,0.474684,0.992089,0.487421,0.481132,0.496835,0.493671,0.727848,0.743671,0.745253,1,0.995253,0.477987,0.993711,0.988924,0.743671,0.742089,0.740506,0.496835,0.971698,1,0.487421,0.748418,0.732595,0.743671,0.75,0.743671,0.742089,0.490566,0.487421,0.726266,1,0.477848,0.949686,1,0.493711,0.738924,0.745253,0.740506,0.493671,0.996855,0.487421,1,0.746835,0.981013,1,1,0.493711,1,0.995253,0.996835,0.743671,0.487421,1,0.996835,0.743671,1,0.742089,0.490566,0.987342,0.490566,0.987342,0.955696,1] area_under_roc_curve 0.8003715617785208 [0.484277,0.740506,0.493711,0.748418,1,0.996855,1,0.984277,0.996835,1,0.996855,1,0.738924,0.742089,0.996835,0.490566,0.996855,0.468553,0.727848,0.490566,0.737342,0.462025,0.990566,1,0.493671,0.487421,0.496855,0.726266,0.984177,1,0.746835,1,0.996855,0.993711,0.995253,0.990506,0.743671,0.734177,0.490506,0.996855,0.484277,0.732595,0.496835,0.745253,0.742089,0.748418,1,0.746835,0.471698,0.487421,0.748418,0.740506,0.746835,0.740506,0.746835,0.487421,1,0.490566,0.737342,0.738924,1,1,0.496835,0.742089,0.993711,0.987421,0.732595,0.981132,0.726266,0.487421,0.981132,1,0.493671,0.742089,0.977848,0.995253,1,0.971698,0.996855,0.743671,0.982595,0.496855,0.990566,0.493711,0.993671,0.990506,0.742089,0.976266,1,0.745253,1,0.996835,0.974684,0.731013,0.955975,0.993671,0.484277,0.745253,0.496835,1] 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 0.46969696969696967 kappa 0.46961325966850825 kappa 0.4062845412916469 kappa 0.45045400710619815 kappa 0.4377985708081646 kappa 0.43808697024702076 kappa 0.48827292110874204 kappa 0.46936197094125076 kappa 0.45041061276058114 kappa 0.4190314559734775 kb_relative_information_score 83.65272729246139 kb_relative_information_score 88.21442454841214 kb_relative_information_score 83.39247405634076 kb_relative_information_score 85.36146780352186 kb_relative_information_score 86.7863088157961 kb_relative_information_score 84.49462862687534 kb_relative_information_score 91.23798607878759 kb_relative_information_score 85.00394042510219 kb_relative_information_score 84.69599095976523 kb_relative_information_score 80.81559189505757 mean_absolute_error 0.011695018523143526 mean_absolute_error 0.011394473235098235 mean_absolute_error 0.012371085164835163 mean_absolute_error 0.011924138361638364 mean_absolute_error 0.01202818986568987 mean_absolute_error 0.01223025550838051 mean_absolute_error 0.011234278568653566 mean_absolute_error 0.01180158244533245 mean_absolute_error 0.011951020854145853 mean_absolute_error 0.012390227480852486 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 0.475 predictive_accuracy 0.475 predictive_accuracy 0.4125 predictive_accuracy 0.45625 predictive_accuracy 0.44375 predictive_accuracy 0.44375 predictive_accuracy 0.49375 predictive_accuracy 0.475 predictive_accuracy 0.45625 predictive_accuracy 0.425 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 0.475 [1,0.5,1,1,0,1,0.5,1,0,1,0,0,0.5,0,0.5,0,0,0,0.5,0,0,0,1,1,0,0,0,1,0.5,1,0.5,0.5,0.5,0,0,0,0,0,1,0.5,1,1,1,0.5,0,1,1,1,1,0.5,0,0.5,0,0,1,1,1,1,0,0,1,1,0.5,0.5,1,0,0.5,1,0,0,0,1,0,1,0,1,0.5,0,1,1,0,1,1,1,0,0.5,0,1,0.5,0,1,1,0.5,0.5,0.5,0,1,1,0,1] recall 0.475 [1,1,0.5,1,1,0,0.5,0,0.5,1,0.5,0.5,0.5,0,0.5,1,0.5,0,0.5,0,1,0,0,1,1,0,0,0,1,0,1,1,0.5,0,1,1,0,1,0,1,1,1,1,0,0,1,1,1,0,0.5,1,0.5,1,0.5,0,0,1,1,1,0,1,0,0.5,0.5,0,0,0.5,1,0,0,0.5,1,0,0.5,0,0.5,0,0,1,0,0,0,0,1,0.5,0.5,1,0,0.5,0,0.5,1,0.5,1,1,0,0,0,0.5,0] recall 0.4125 [0.5,0.5,1,0,1,0.5,0,0.5,0,1,0.5,0.5,1,0.5,1,0.5,1,0,0,0,0,0,0.5,1,0,0.5,0,0,0.5,1,1,1,0,0,1,0,1,0,1,0,0.5,0,1,1,0,0,0.5,1,0.5,0.5,0.5,0,0,0,1,0,1,1,0,0,0,1,0.5,0,0.5,0,0.5,0.5,0,0,0,1,1,0,0,0,1,0,0,0.5,0,0.5,0,0,1,0,1,0.5,1,1,0.5,1,0,0,0.5,1,0,0.5,0,0] recall 0.45625 [0,1,1,1,0.5,0.5,1,1,0.5,1,0,0,0,0,1,0.5,0.5,0.5,0,0,1,0,1,0,0,0.5,0,0,0.5,1,1,0.5,1,0,1,0,1,0,0,1,0.5,0,1,1,0,0,0.5,1,0.5,0,0,1,0,0,0,0.5,1,0.5,1,0,1,0,0.5,0,0.5,0,0.5,0,0,0,0,0.5,0,1,0,0,1,0.5,1,0.5,0,0.5,0.5,0,1,0,1,0.5,1,1,1,1,0,0.5,0.5,0,0,1,1,0] recall 0.44375 [0.5,0,0.5,0.5,1,0,0,0,0,1,0.5,0.5,0,0,0,1,0.5,0.5,0,0.5,0.5,1,0,1,1,0,0,1,0,0.5,0,0,0,0,0,1,0.5,0,0.5,1,0.5,1,1,0,0,1,1,1,0.5,1,0,0,0.5,0,0.5,0,1,0,0.5,0,1,1,0,1,0.5,1,0,0,0,0,0,0.5,1,0,0.5,0,0.5,0.5,0.5,1,0,1,0,0.5,0,0,1,0,1,0.5,1,0,0,1,0,0,0.5,1,0,0.5] recall 0.44375 [0,1,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,0,0,0.5,0.5,0,0,0,0.5,0.5,0.5,1,0.5,0.5,0,0,1,0.5,1,1,0.5,0.5,0,0.5,0,0,0,0,0.5,1,0,0,0,1,0.5,0.5,1,0.5,0,1,0,0,0,0.5,1,0.5,0,0,1,0.5,1,0,1,0,0,1,0.5,0.5,0,0,1,1,0.5,0,1,0,1,0,1,0.5,0,0.5,0,1,1,1,0,0,1,0,0,1,0.5,1,0.5,1,0,0.5] recall 0.49375 [1,0,0,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0,1,0,0.5,0,0.5,0,0,1,0,0,0,0.5,0,0,0.5,0,1,0,0.5,0.5,0.5,1,1,1,0,0,1,0,0,0.5,0,0.5,1,1,0,0,1,0,0.5,0.5,1,0.5,0,0,0,1,1,0,1,0,0,0.5,0.5,0.5,0,1,1,1,0.5,0,1,0,1,0,0.5,0,1,0.5,1,0,1,0,0,0.5,1,1,0,0,0,0,1,0,0,1] recall 0.475 [0.5,1,0,0.5,0,0.5,1,0.5,0,1,0,1,0.5,1,0,0.5,1,0,0.5,1,0,0,0,1,1,0,1,0.5,0,0,0.5,1,1,1,0.5,0.5,0,0,0.5,1,0,1,1,0.5,0,0.5,1,0,0,1,1,0.5,0,1,0.5,0,1,1,0,0,0.5,0.5,1,1,0.5,0,0,0,0,0.5,0,1,0,1,0.5,1,1,1,0,1,0,0,0.5,0,0,0,1,0,1,1,0,0.5,1,0,1,0.5,0.5,0,0,0.5] recall 0.45625 [0,1,1,1,1,1,0,1,0.5,1,0,0,0,0.5,0.5,1,1,0,0,0,1,1,0,0.5,1,0,0,0.5,0.5,1,0.5,1,0,0,1,1,0,0,0.5,0,0,0,0,0,0,0.5,1,1,0,1,0.5,0.5,0.5,0.5,0,0,1,0,0.5,0,0.5,0,0.5,0,0,0,0,1,0,0,1,0,0,0.5,0.5,0,1,0,1,0.5,0,1,1,0,1,0.5,1,0.5,0,1,1,0.5,1,0.5,0,0.5,0,0.5,0,1] recall 0.425 [0,0.5,0,0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,1,0,0,0,0,0,0,1,0,0,0,0.5,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,0,0,0,0,0,0.5,0.5,0.5,1,0.5,0,0,0.5,0.5,0.5,0.5,0.5,0,1,0,0.5,0,1,1,0,0.5,1,0,0,0,0,0,0,1,0,0,0.5,0.5,1,0,1,0,0.5,0,1,0,1,0.5,0,0,1,0,1,1,0.5,0,0,1,0,0.5,0,1] relative_absolute_error 0.590657501168864 relative_absolute_error 0.5754784462170816 relative_absolute_error 0.62480228105228 relative_absolute_error 0.6022292101837547 relative_absolute_error 0.6074843366510025 relative_absolute_error 0.6176896721404287 relative_absolute_error 0.5673878064976539 relative_absolute_error 0.5960395174410319 relative_absolute_error 0.6035869118255471 relative_absolute_error 0.6257690646895185 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 0.09165774625466784 root_mean_squared_error 0.08911242419051243 root_mean_squared_error 0.09187285459687641 root_mean_squared_error 0.09015256099356123 root_mean_squared_error 0.0913736894602902 root_mean_squared_error 0.09182063465321705 root_mean_squared_error 0.08835727817978106 root_mean_squared_error 0.09016726096548602 root_mean_squared_error 0.0927992273591295 root_mean_squared_error 0.09485494942019562 root_relative_squared_error 0.9211950104737454 root_relative_squared_error 0.8956135612088473 root_relative_squared_error 0.9233569306567188 root_relative_squared_error 0.9060673294099157 root_relative_squared_error 0.918340132273437 root_relative_squared_error 0.922832100475835 root_relative_squared_error 0.8880240582405756 root_relative_squared_error 0.9062150696865925 root_relative_squared_error 0.9326673272276237 root_relative_squared_error 0.9533281113179641 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 101.77267600010964 usercpu_time_millis 77.18336200014164 usercpu_time_millis 80.7915749996937 usercpu_time_millis 79.6530139996321 usercpu_time_millis 60.142099000131566 usercpu_time_millis 56.97177899992312 usercpu_time_millis 56.829711999853316 usercpu_time_millis 54.95950900012758 usercpu_time_millis 53.96747499980847 usercpu_time_millis 55.460294000567956 usercpu_time_millis_testing 1.7554890000610612 usercpu_time_millis_testing 1.731514999846695 usercpu_time_millis_testing 1.7302609999205742 usercpu_time_millis_testing 1.724067999930412 usercpu_time_millis_testing 1.3282019999678596 usercpu_time_millis_testing 1.2701680002464855 usercpu_time_millis_testing 1.2511290001384623 usercpu_time_millis_testing 1.2512640000750253 usercpu_time_millis_testing 1.2486569999055064 usercpu_time_millis_testing 1.2522370002443495 usercpu_time_millis_training 100.01718700004858 usercpu_time_millis_training 75.45184700029495 usercpu_time_millis_training 79.06131399977312 usercpu_time_millis_training 77.92894599970168 usercpu_time_millis_training 58.813897000163706 usercpu_time_millis_training 55.70161099967663 usercpu_time_millis_training 55.578582999714854 usercpu_time_millis_training 53.70824500005256 usercpu_time_millis_training 52.71881799990297 usercpu_time_millis_training 54.208057000323606