10110777 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) 8036046 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 "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 10 8783 min_samples_split 11 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 49784 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 21142092 description https://api.openml.org/data/download/21142092/description.xml -1 21142093 predictions https://api.openml.org/data/download/21142093/predictions.arff area_under_roc_curve 0.8352485795454545 [0.83142,0.869969,0.905125,0.931404,0.901535,0.830749,0.769788,0.867582,0.867681,0.808712,0.931029,0.900114,0.835997,0.762449,0.83791,0.792732,0.905481,0.793935,0.796599,0.732876,0.8997,0.865964,0.834517,0.999961,0.834852,0.725379,0.76525,0.83574,0.836076,0.871843,0.960582,0.904179,0.804905,0.821634,0.900844,0.870995,0.793896,0.76164,0.995699,0.965396,0.801255,0.833037,0.871745,0.862512,0.86918,0.800742,0.935172,0.806917,0.727884,0.83724,0.956124,0.795474,0.930634,0.78123,0.772905,0.689789,0.998146,0.930181,0.796934,0.826073,0.774621,0.79796,0.928958,0.641828,0.837279,0.833886,0.791351,0.935646,0.661478,0.728555,0.756866,0.933692,0.803405,0.867345,0.649562,0.931088,0.900548,0.691742,0.902285,0.898497,0.894551,0.703934,0.766473,0.765803,0.859947,0.83578,0.828283,0.792337,0.735894,0.804076,0.965534,0.964824,0.827849,0.758108,0.865175,0.86626,0.893663,0.735361,0.652442,0.796658] average_cost 0 f_measure 0.42356028732045303 [0.333333,0.628571,0.785714,0.380952,0.571429,0.266667,0.285714,0.588235,0.322581,0.413793,0.526316,0.611111,0.470588,0.066667,0.625,0.148148,0.702703,0.232558,0.352941,0.451613,0.5,0.5625,0.358974,0.969697,0.484848,0.2,0.26087,0.545455,0.571429,0.666667,0.5,0.722222,0.5,0.205128,0.55,0.727273,0.357143,0.25,0.722222,0.666667,0.413793,0.421053,0.709677,0.457143,0.6875,0.266667,0.714286,0.514286,0.206897,0.571429,0.44898,0.27027,0.457143,0.166667,0.344828,0,0.864865,0.512821,0.2,0.296296,0.413793,0.363636,0.45,0.266667,0.592593,0.45,0.129032,0.666667,0.216216,0.1875,0.066667,0.6875,0.4,0.5625,0.076923,0.5,0.571429,0.074074,0.740741,0.533333,0.571429,0.222222,0.363636,0.171429,0.421053,0.482759,0.307692,0.071429,0.285714,0.4,0.6,0.611111,0.384615,0.214286,0.296296,0.45,0.424242,0.222222,0,0.296296] kappa 0.42803030303030304 kb_relative_information_score 875.1010045306258 mean_absolute_error 0.012986604423805387 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 0.432821536265119 [0.357143,0.578947,0.916667,0.8,0.526316,0.285714,0.333333,0.555556,0.333333,0.461538,0.454545,0.55,0.444444,0.071429,0.625,0.181818,0.619048,0.185185,0.333333,0.466667,0.5,0.5625,0.304348,0.941176,0.470588,0.214286,0.428571,0.529412,0.666667,0.6,0.416667,0.65,0.45,0.173913,0.458333,0.705882,0.416667,0.25,0.65,0.647059,0.461538,0.363636,0.733333,0.421053,0.6875,0.285714,0.833333,0.473684,0.230769,0.526316,0.333333,0.238095,0.421053,0.25,0.384615,0,0.761905,0.434783,0.214286,0.363636,0.461538,0.352941,0.375,0.285714,0.727273,0.375,0.133333,0.647059,0.190476,0.1875,0.071429,0.6875,0.368421,0.5625,0.1,0.45,0.666667,0.090909,0.909091,0.571429,0.666667,0.272727,0.352941,0.157895,0.363636,0.538462,0.4,0.083333,0.6,0.368421,0.642857,0.55,0.5,0.25,0.363636,0.375,0.411765,0.272727,0,0.363636] predictive_accuracy 0.43375 prior_entropy 6.6438561897747395 recall 0.43375 [0.3125,0.6875,0.6875,0.25,0.625,0.25,0.25,0.625,0.3125,0.375,0.625,0.6875,0.5,0.0625,0.625,0.125,0.8125,0.3125,0.375,0.4375,0.5,0.5625,0.4375,1,0.5,0.1875,0.1875,0.5625,0.5,0.75,0.625,0.8125,0.5625,0.25,0.6875,0.75,0.3125,0.25,0.8125,0.6875,0.375,0.5,0.6875,0.5,0.6875,0.25,0.625,0.5625,0.1875,0.625,0.6875,0.3125,0.5,0.125,0.3125,0,1,0.625,0.1875,0.25,0.375,0.375,0.5625,0.25,0.5,0.5625,0.125,0.6875,0.25,0.1875,0.0625,0.6875,0.4375,0.5625,0.0625,0.5625,0.5,0.0625,0.625,0.5,0.5,0.1875,0.375,0.1875,0.5,0.4375,0.25,0.0625,0.1875,0.4375,0.5625,0.6875,0.3125,0.1875,0.25,0.5625,0.4375,0.1875,0,0.25] relative_absolute_error 0.6558891123134022 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08885842016206247 root_relative_squared_error 0.8930607246706431 total_cost 0 area_under_roc_curve 0.8484916109386192 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[1,0.993711,1,0.477848,1,0.716772,0.993671,0.996835,0.493711,0.998418,0.993711,0.487421,0.742089,0.981132,0.487421,0.468354,1,0.465409,0.971519,0.490506,0.977848,0.738924,0.734177,1,0.990506,0.446541,0.462264,0.742089,1,0.742089,0.981013,0.996855,1,1,0.740506,1,0.740506,0.984177,0.993671,0.996855,0.996835,0.737342,0.993671,0.490506,1,0.740506,1,1,0.962264,0.496855,0.949686,0.719937,0.738924,1,0.743671,0.723101,1,0.993671,0.481013,0.996855,0.493671,0.734177,0.971698,0.738924,0.748418,0.984277,0.993711,0.746835,0.481013,0.737342,0.471698,1,0.996835,0.474843,0.452532,1,0.987421,0.930818,1,0.962264,0.968354,1,0.732595,0.990506,0.710443,0.738924,0.990506,0.484277,1,0.740506,1,0.996835,0.743671,0.971698,0.474843,0.987342,0.987342,0.996855,0.691456,0.726266] area_under_roc_curve 0.8546712045219328 [0.471698,1,1,1,0.990506,0.990566,0.477848,0.477987,0.981013,1,0.493711,1,0.996835,0.746835,0.996835,0.962264,1,0.981132,1,1,0.998418,1,0.993711,1,0.748418,0.449686,0.465409,0.993671,0.731013,0.996855,0.996835,1,0.484277,0.977987,0.996835,1,0.738924,0.735759,0.996835,0.996855,0.487421,0.737342,0.746835,0.737342,0.490506,0.748418,1,0.727848,0.471698,0.996855,0.987342,0.97943,0.993671,0.705696,0.740506,0.471698,1,0.981132,0.735759,0.981013,0.746835,1,0.995253,0.740506,1,0.987421,0.985759,0.5,0.734177,0.987421,0.984277,0.496855,0.745253,1,0.471519,0.738924,0.996855,0.474843,1,0.996835,1,0.990566,0.465409,0.955975,0.995253,1,0.958861,0.713608,0.471698,0.996835,1,0.746835,0.996835,0.71519,0.993711,0.726266,0.955975,0.987342,0.737342,1] area_under_roc_curve 0.8582455119019186 [0.962264,0.484177,0.496855,0.985759,1,0.987421,0.734177,0.484277,0.737342,0.996855,1,0.993711,0.993671,0.987342,0.742089,1,1,1,0.993671,1,1,0.996835,1,1,1,0.465409,1,0.743671,0.487342,0.993711,0.745253,0.75,1,0.946541,0.992089,0.734177,0.707278,0.990506,1,1,0.971698,0.981013,0.748418,0.743671,1,0.738924,1,0.72943,0.993711,0.493711,0.995253,0.732595,0.996835,0.955696,0.740506,0.468553,1,0.987421,0.477848,1,0.993671,0.721519,0.996835,0.734177,1,0.5,0.958861,1,0.987342,0.471698,0.477987,0.993711,0.740506,0.996835,0.734177,0.995253,1,0.474843,0.481132,0.743671,0.993671,0.487421,0.996855,0.490566,0.987342,1,0.734177,0.974684,0.45283,0.990506,1,0.996835,0.996835,0.737342,1,1,1,0.477848,0.995253,0.984277] 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 0.45054077524275676 kappa 0.4631932109729623 kappa 0.41273975846554584 kappa 0.4188709040663245 kappa 0.3810200536870362 kappa 0.41921483527322945 kappa 0.36234857751647404 kappa 0.3874329017998105 kappa 0.4885152735022496 kappa 0.4946703513620213 kb_relative_information_score 91.59672097718149 kb_relative_information_score 90.90545589253892 kb_relative_information_score 83.19553710387359 kb_relative_information_score 89.40375024172317 kb_relative_information_score 77.83506802820685 kb_relative_information_score 86.37149423714054 kb_relative_information_score 85.04823860235508 kb_relative_information_score 82.24399276536087 kb_relative_information_score 93.41155547373995 kb_relative_information_score 95.0891912085144 mean_absolute_error 0.012511882054456556 mean_absolute_error 0.012513646645859309 mean_absolute_error 0.012988840676437432 mean_absolute_error 0.013197422320434127 mean_absolute_error 0.013749039643384652 mean_absolute_error 0.013130426488393788 mean_absolute_error 0.013636540336598394 mean_absolute_error 0.013498344359136738 mean_absolute_error 0.01236395074599893 mean_absolute_error 0.012275950967354798 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 0.45625 predictive_accuracy 0.46875 predictive_accuracy 0.41875 predictive_accuracy 0.425 predictive_accuracy 0.3875 predictive_accuracy 0.425 predictive_accuracy 0.36875 predictive_accuracy 0.39375 predictive_accuracy 0.49375 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 0.45625 [0,0.5,1,0,0.5,1,0.5,1,0,0,1,0.5,0,0,1,1,1,0,0,0,1,0,1,1,0,0.5,0.5,0.5,0.5,0,1,1,0.5,0,1,0,0,0,1,1,1,0,1,1,1,0.5,1,1,0,0.5,0.5,0.5,1,0,1,0,1,1,0,0,0,1,0,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,0.5,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,0.5,1,1,0,0.5,0.5,0,0,0,1] recall 0.46875 [1,1,0.5,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,0,1,0,0.5,1,1,0,0,1,1,0,0,0,0.5,1,1,1,0.5,0.5,1,1,0,0,0,1,0,1,1,0.5,0.5,0,0,0,0,1,0,0.5,1,0.5,1,0,1,1,0,0,1,1,1,0,1,1,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0,0,0,0,0,1,0,0,0,0.5,1,0,0,0] recall 0.41875 [0,0.5,0,1,0.5,0,0,1,0.5,0,0.5,0.5,1,0,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,0,1,1,0,0,1,1,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,1,0,1,0.5,0,0,0,0,0.5,1,0,0.5,0,1,0,0,0,1,0,0.5,0,0,1,0,0.5,1,0,0,1,0.5,1,0,1,0,0.5,1,0.5,1,0,0,0,1,0,0.5,0,0.5] recall 0.425 [0.5,1,1,0,0.5,0,1,0.5,0,0,1,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0.5,0.5,1,1,0.5,0.5,1,0,1,0,1,1,0,0,1,0,1,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,1,0,1,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0,0,1,0.5,0,0,1,0,0,0,1,0,0,0,1,0,0,0,0.5,0,0.5,0,0,0] recall 0.3875 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,0,0.5,0,0,1,0,0.5,0.5,1,1,0,0,1,1,1,0,0,1,1,0.5,1,0,0,0,0,1,0.5,0,0.5,0.5,1,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,0,1,0,0.5,0,0,0,1,1,0,0,1,0,1,0.5,0,0.5,1,0,0] recall 0.425 [0,1,1,0,1,0,0,0.5,1,0.5,0.5,1,1,0,0,0,1,1,0,0.5,1,0.5,0,1,0.5,0,0,1,1,1,1,1,0.5,0,0,1,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0.5,1,0,0,0,0,0,1,1,1,1,0.5,0,0,0,0.5,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0.5,0,0,0.5] recall 0.36875 [0.5,0,1,0,1,0,0,1,1,0,1,1,0.5,0,0,0,0,0,0,0.5,0,1,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,1,0,0,0.5,0.5,0,1,0,0,0.5,0,1,1,0,0.5,0,0,0,1,0.5,0,0,0,0,1,0,0,1,0,0.5,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,0.5,0,0,0,0,1,1,0.5,1,0,0.5,0.5,0,0,0] recall 0.39375 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,0,0,0,0.5,0,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,1,0.5,1,0,1,0.5,1,1,0,0,0,0,0.5,1,0,0,1,0.5,0,1,0,0.5,0,0.5,0.5,0,0,0.5,0,0.5,0,1,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0] recall 0.49375 [0,1,1,0,0,1,0,0,0,1,0,1,1,0,1,0,1,0,1,1,0.5,1,1,1,0.5,0,0,1,0,1,1,1,0,0,1,1,0.5,0,1,0,0,0.5,0,0.5,0,0.5,1,0.5,0,1,1,0.5,1,0,0,0,1,0,0,0,0,1,1,0.5,1,1,0.5,0,0.5,0,1,0,0.5,1,0,0.5,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,1,1,0,0,0,1,0.5,0,0.5,0,1] recall 0.5 [0,0,0,0,1,0,0,0,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,1,1,1,0,0.5,0.5,0.5,1,0,1,0.5,1,0,1,0,1,0,0,0,1,1,0,1,1,0,0,0.5,1,0,0,1,0.5,0,0,0,0,1,0.5,0.5,1,0,0,0.5,0.5,0,1,0,1,1,0,0,0,0.5,0.5,1,1,0.5,0,1,1,0,0,0] relative_absolute_error 0.6319132350735623 relative_absolute_error 0.6320023558514792 relative_absolute_error 0.6560020543655258 relative_absolute_error 0.6665364808300054 relative_absolute_error 0.6943959415850823 relative_absolute_error 0.6631528529491801 relative_absolute_error 0.6887141584140593 relative_absolute_error 0.6817345635927634 relative_absolute_error 0.6244419568686318 relative_absolute_error 0.6199975236037767 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 0.0868560085014353 root_mean_squared_error 0.08611485713271444 root_mean_squared_error 0.09133408677793396 root_mean_squared_error 0.08808813749129232 root_mean_squared_error 0.09317843932904535 root_mean_squared_error 0.08984988527116687 root_mean_squared_error 0.09197413995648933 root_mean_squared_error 0.09060190878340893 root_mean_squared_error 0.08517711632861692 root_mean_squared_error 0.08496296398968788 root_relative_squared_error 0.8729357302641803 root_relative_squared_error 0.8654868787402243 root_relative_squared_error 0.9179421103398998 root_relative_squared_error 0.8853190925450147 root_relative_squared_error 0.9364785509252671 root_relative_squared_error 0.9030253239422436 root_relative_squared_error 0.9243748868221497 root_relative_squared_error 0.9105834446199168 root_relative_squared_error 0.8560622290499286 root_relative_squared_error 0.8539099170614296 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 224.52256999895326 usercpu_time_millis 215.0591669997084 usercpu_time_millis 164.20795700105373 usercpu_time_millis 154.9991140018392 usercpu_time_millis 155.1703059958527 usercpu_time_millis 154.606321000756 usercpu_time_millis 154.6444590021565 usercpu_time_millis 155.13975599969854 usercpu_time_millis 154.73020300123608 usercpu_time_millis 154.08571399893845 usercpu_time_millis_testing 1.6978540006675757 usercpu_time_millis_testing 1.7321800005447585 usercpu_time_millis_testing 1.2945179987582378 usercpu_time_millis_testing 1.2513729998318013 usercpu_time_millis_testing 1.2529409978014883 usercpu_time_millis_testing 1.2593760002346244 usercpu_time_millis_testing 1.2514689988165628 usercpu_time_millis_testing 1.276208000490442 usercpu_time_millis_testing 1.2684410030487925 usercpu_time_millis_testing 1.2497940006142017 usercpu_time_millis_training 222.82471599828568 usercpu_time_millis_training 213.32698699916364 usercpu_time_millis_training 162.9134390022955 usercpu_time_millis_training 153.7477410020074 usercpu_time_millis_training 153.9173649980512 usercpu_time_millis_training 153.34694500052137 usercpu_time_millis_training 153.39299000333995 usercpu_time_millis_training 153.8635479992081 usercpu_time_millis_training 153.46176199818728 usercpu_time_millis_training 152.83591999832424