10094018 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) 8019135 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 2 8783 min_samples_split 18 8783 min_weight_fraction_leaf 0.0 8783 presort false 8783 random_state 57601 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 21108573 description https://api.openml.org/data/download/21108573/description.xml -1 21108574 predictions https://api.openml.org/data/download/21108574/predictions.arff area_under_roc_curve 0.8222107796717171 [0.799598,0.870956,0.842428,0.933811,0.903192,0.865491,0.804747,0.86922,0.806049,0.841067,0.900055,0.869003,0.805319,0.829841,0.838226,0.734888,0.904928,0.733152,0.764165,0.698982,0.838029,0.866477,0.834182,0.999487,0.804628,0.696141,0.734848,0.805358,0.838029,0.87212,0.902186,0.935705,0.80593,0.789161,0.86847,0.870522,0.766256,0.730804,0.996903,0.934935,0.802064,0.836076,0.840376,0.833116,0.869732,0.770202,0.934422,0.744851,0.702612,0.838108,0.895241,0.705275,0.930496,0.753906,0.80524,0.724708,0.968513,0.899167,0.801057,0.796757,0.778626,0.799361,0.804924,0.646899,0.838522,0.836056,0.762074,0.93602,0.634193,0.698647,0.665246,0.933692,0.771622,0.868943,0.594085,0.900016,0.871133,0.690617,0.903389,0.93241,0.927951,0.706084,0.737196,0.802004,0.830196,0.839331,0.892657,0.766533,0.735105,0.80666,0.934896,0.965179,0.829684,0.729778,0.900864,0.870068,0.862827,0.770912,0.720309,0.769156] average_cost 0 f_measure 0.44959991486936735 [0.375,0.628571,0.758621,0.692308,0.684211,0.285714,0.451613,0.666667,0.363636,0.6,0.5625,0.645161,0.424242,0.27027,0.628571,0.137931,0.787879,0.2,0.375,0.230769,0.5,0.580645,0.307692,0.941176,0.424242,0.193548,0.133333,0.533333,0.642857,0.647059,0.518519,0.756757,0.5625,0.195122,0.511628,0.645161,0.344828,0.228571,0.777778,0.75,0.4375,0.465116,0.666667,0.470588,0.6875,0.352941,0.592593,0.482759,0.176471,0.588235,0.454545,0.266667,0.387097,0.16,0.516129,0.058824,0.909091,0.473684,0.322581,0.266667,0.64,0.363636,0.461538,0.333333,0.625,0.363636,0.129032,0.727273,0.235294,0.171429,0.102564,0.606061,0.4,0.580645,0.083333,0.555556,0.551724,0.071429,0.769231,0.6,0.6,0.222222,0.413793,0.242424,0.380952,0.62069,0.384615,0.076923,0.25,0.473684,0.685714,0.588235,0.482759,0.1875,0.5,0.588235,0.375,0.388889,0.095238,0.333333] kappa 0.44696969696969696 kb_relative_information_score 871.4229066534085 mean_absolute_error 0.012417587059590644 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 0.46254892186790497 [0.375,0.578947,0.846154,0.9,0.590909,0.333333,0.466667,0.714286,0.352941,0.642857,0.5625,0.666667,0.411765,0.238095,0.578947,0.153846,0.764706,0.166667,0.375,0.3,0.5,0.6,0.26087,0.888889,0.411765,0.2,0.142857,0.571429,0.75,0.611111,0.636364,0.666667,0.5625,0.16,0.407407,0.666667,0.384615,0.210526,0.7,0.75,0.4375,0.37037,0.714286,0.444444,0.6875,0.333333,0.727273,0.538462,0.166667,0.555556,0.357143,0.285714,0.4,0.222222,0.533333,0.055556,0.882353,0.409091,0.333333,0.285714,0.888889,0.352941,0.391304,0.5,0.625,0.352941,0.133333,0.705882,0.222222,0.157895,0.086957,0.588235,0.368421,0.6,0.125,0.5,0.615385,0.083333,1,0.642857,0.642857,0.272727,0.461538,0.235294,0.307692,0.692308,0.5,0.1,0.375,0.409091,0.631579,0.555556,0.538462,0.1875,0.583333,0.555556,0.375,0.35,0.2,0.5] predictive_accuracy 0.4525 prior_entropy 6.6438561897747395 recall 0.4525 [0.375,0.6875,0.6875,0.5625,0.8125,0.25,0.4375,0.625,0.375,0.5625,0.5625,0.625,0.4375,0.3125,0.6875,0.125,0.8125,0.25,0.375,0.1875,0.5,0.5625,0.375,1,0.4375,0.1875,0.125,0.5,0.5625,0.6875,0.4375,0.875,0.5625,0.25,0.6875,0.625,0.3125,0.25,0.875,0.75,0.4375,0.625,0.625,0.5,0.6875,0.375,0.5,0.4375,0.1875,0.625,0.625,0.25,0.375,0.125,0.5,0.0625,0.9375,0.5625,0.3125,0.25,0.5,0.375,0.5625,0.25,0.625,0.375,0.125,0.75,0.25,0.1875,0.125,0.625,0.4375,0.5625,0.0625,0.625,0.5,0.0625,0.625,0.5625,0.5625,0.1875,0.375,0.25,0.5,0.5625,0.3125,0.0625,0.1875,0.5625,0.75,0.625,0.4375,0.1875,0.4375,0.625,0.375,0.4375,0.0625,0.25] relative_absolute_error 0.6271508615954859 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08917336728307118 root_relative_squared_error 0.8962260623348509 total_cost 0 area_under_roc_curve 0.8306902316694529 [1,0.992089,1,0.996855,0.995253,0.993711,0.745253,1,0.996835,1,0.995253,0.745253,0.738924,0.734177,1,0.949686,0.996835,0.731013,0.487342,0.477987,0.996855,0.481132,0.987421,1,0.984277,0.738924,0.490506,0.990506,0.745253,0.5,0.984177,1,0.743671,0.721519,1,0.487421,1,0.487421,1,1,1,0.493711,0.75,0.993671,0.996835,0.990506,1,1,0.474684,0.737342,0.742089,0.740506,0.984277,0.726266,1,1,0.75,1,0.974843,0.731013,0.490566,0.996855,0.746835,0.493671,1,0.993671,0.982595,1,0.474843,0.977987,0.71519,1,0.743671,0.737342,0.455975,0.992089,0.748418,0.976266,0.746835,0.996835,1,0.490506,0.984277,0.996855,0.735759,0.746835,0.958861,0.726266,1,0.484277,0.740506,1,0.996835,0.984177,0.981013,0.737342,0.996855,0.490506,0.462025,0.996855] area_under_roc_curve 0.8051573322187725 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[0.477987,1,1,1,1,0.996855,0.740506,0.996855,0.738924,1,0.490566,0.996855,0.996835,0.745253,0.996835,0.484277,0.996855,0.981132,1,0.968553,0.748418,1,0.987421,1,0.75,0.45283,0.465409,0.743671,0.742089,0.996855,0.996835,1,0.481132,0.981132,0.993671,1,0.738924,0.735759,1,0.5,0.487421,0.743671,0.746835,0.738924,0.490506,0.748418,0.996855,0.487342,0.484277,0.996855,0.740506,0.726266,0.996835,0.719937,0.746835,0.481132,1,0.977987,0.742089,0.724684,0.745253,0.996835,0.734177,0.742089,1,0.987421,0.988924,0.5,0.731013,0.984277,0.984277,0.987421,0.745253,1,0.471519,0.484177,0.996855,0.481132,1,0.996835,1,0.996855,0.481132,0.987421,0.985759,1,0.962025,0.738924,0.462264,0.996835,1,0.75,1,0.468354,1,0.734177,0.943396,0.998418,0.731013,1] area_under_roc_curve 0.8444418736565561 [0.962264,0.493671,0.496855,0.996835,1,0.987421,0.740506,0.481132,0.737342,1,0.996855,0.484277,0.996835,0.987342,0.742089,1,1,1,0.996835,1,1,0.996835,0.496855,1,1,0.455975,1,0.743671,0.487342,0.993711,0.996835,0.75,1,0.927673,0.992089,0.740506,0.484177,0.735759,0.996835,1,0.971698,0.987342,0.748418,0.743671,1,0.738924,0.993711,0.481013,0.993711,0.490566,0.995253,0.737342,0.996835,0.724684,0.746835,0.468553,1,0.993711,0.493671,1,1,0.731013,0.487342,0.742089,0.993711,0.5,0.721519,0.996855,0.987342,0.471698,0.468553,0.987421,0.490506,0.996835,0.734177,0.996835,1,0.471698,0.493711,0.996835,0.993671,0.490566,0.996855,0.493711,0.982595,1,0.982595,0.982595,0.45283,0.998418,1,0.996835,0.996835,0.990506,1,1,1,0.743671,0.988924,0.996855] 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 0.43150414528227393 kappa 0.43806479618010336 kappa 0.4694667035092566 kappa 0.44418127269856306 kappa 0.4253236501420903 kappa 0.41907731165397216 kappa 0.425414364640884 kappa 0.39374802652352386 kappa 0.5073230429118472 kappa 0.5136586136112428 kb_relative_information_score 88.48376028611763 kb_relative_information_score 84.48031536579148 kb_relative_information_score 87.94237016229745 kb_relative_information_score 88.69699178341618 kb_relative_information_score 80.12347993731437 kb_relative_information_score 85.7055598725114 kb_relative_information_score 88.60185665605373 kb_relative_information_score 81.6982627042156 kb_relative_information_score 91.34458627400423 kb_relative_information_score 94.34572361169575 mean_absolute_error 0.012548486888928068 mean_absolute_error 0.012338642148292887 mean_absolute_error 0.01193583088684192 mean_absolute_error 0.012462215521406693 mean_absolute_error 0.012801459947323924 mean_absolute_error 0.012962306770027358 mean_absolute_error 0.012627452510724576 mean_absolute_error 0.013238205758702094 mean_absolute_error 0.01159651331103537 mean_absolute_error 0.0116647568526245 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 0.4375 predictive_accuracy 0.44375 predictive_accuracy 0.475 predictive_accuracy 0.45 predictive_accuracy 0.43125 predictive_accuracy 0.425 predictive_accuracy 0.43125 predictive_accuracy 0.4 predictive_accuracy 0.5125 predictive_accuracy 0.51875 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 0.4375 [1,0.5,1,1,1,1,0.5,1,0,1,0.5,0.5,0,0,1,0,1,0,0,0,1,0,1,1,0,0.5,0,0.5,0.5,0,0,1,0.5,0,1,0,0,0,1,1,1,0,0.5,1,1,0.5,1,1,0,0.5,0.5,0.5,0,0,1,0,0.5,1,0,0,0,1,0.5,0,1,0.5,0,1,0,0,0,1,0.5,0.5,0,1,0.5,0,0.5,0.5,1,0,0,1,0.5,0.5,0,0,0,0,0.5,1,1,0,0,0.5,0,0,0,1] recall 0.44375 [1,1,0.5,1,1,1,0.5,1,0.5,1,0.5,1,0,0.5,1,1,1,0,0,0,1,0,0,1,1,0,0,0,0.5,1,0.5,1,0.5,0.5,0.5,1,0,1,1,1,0,1,1,0.5,0.5,0,0,0,0,1,0,0,1,0.5,1,0,1,0,0,0,1,1,0.5,0,1,0,0.5,0,0,0,0,1,0.5,0.5,0,1,0,0.5,0.5,0.5,0.5,0,0,0,0.5,0,0.5,0,0,0,0.5,1,0.5,0,0.5,0.5,1,0,0,0] recall 0.475 [0,0.5,0,1,0.5,0,0,1,0.5,0.5,0.5,0.5,1,0.5,0,0,1,0,0,0,1,0,0,1,0,1,0,1,1,1,1,1,1,0,0,1,1,0,1,1,0,1,1,1,0,0,0.5,1,0.5,0.5,1,1,0,0,1,0.5,1,0.5,1,0,0,0,0.5,1,0.5,0.5,0,1,0,0,0.5,1,0,0.5,0,0,1,0,0.5,1,0,0,1,1,1,0,1,0,0.5,1,0.5,1,0,0,0.5,1,0,0.5,0,0.5] recall 0.45 [0.5,1,1,0,0.5,0,1,0.5,0,0,0.5,0.5,0,0,1,0,0.5,0.5,0,0,0,1,0.5,1,0,0,0.5,0,0.5,0.5,1,1,0.5,0.5,1,0,1,0,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,1,0,0,1,0,1,1,0,0,1,0,1,0,0.5,1,0,1,0,0.5,0,0,1,0.5,0,0.5,0,0,1,0.5,0,0,1,0,0,0,1,0,0,1,1,0,0,0,1,0,0,1,0,0] recall 0.43125 [0.5,1,0.5,1,0,0.5,0,0,0,0.5,1,0.5,1,0,1,0,0.5,0.5,1,1,0,0,1,1,0.5,0,0,1,1,0,0,1,0,0,1,1,0.5,0,0.5,1,1,0.5,0,1,1,0,0.5,0,0.5,0.5,0,0,0,0,0.5,0,1,0,0.5,0,0.5,0.5,1,0,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0.5,0,0.5,0,1,1,0,0.5,0,1,0,1,1,0,1,1,0,1,0.5,1,0.5,1,0,0] recall 0.425 [0,1,1,0,1,0,0,0.5,1,0.5,0.5,1,0,1,1,0,1,0.5,0,0,0.5,0.5,0,1,0.5,0,0,1,1,1,1,1,0.5,0,0,0,0,0.5,1,0,0.5,0.5,1,1,1,0,1,0.5,0,0.5,1,1,0,0,0,0,1,1,1,1,0.5,0,0,0,0.5,0.5,0,1,0.5,0.5,0,1,0,1,0,0,0.5,0,0.5,0,1,0,0,0,0,1,0,0,0,1,1,0,0,1,0.5,0,0.5,0,0,0.5] recall 0.43125 [0.5,0,1,0.5,1,0,1,1,1,0,1,1,0.5,1,0,0,0,0,0,0,0.5,1,0.5,1,0,0,0,0.5,0,1,0.5,0,1,1,1,0.5,0,0.5,1,0,0,1,0.5,0,1,0.5,0,0.5,0,1,1,0,0.5,0,0.5,0,1,0.5,0,0,1,0,1,0,0.5,1,0,1,0.5,0,0,1,0,0,0,1,1,0,1,1,0.5,0,0.5,0,1,0.5,0,0,0,0,1,0.5,0.5,0,0,0.5,0.5,0,0.5,0] recall 0.4 [0.5,1,1,0,1,0,0.5,1,0,1,0,0,0,0,0,0,1,0,1,0,0,0.5,0,1,0.5,0,0,0.5,1,0.5,0,1,1,1,0.5,1,0.5,0,0.5,1,1,0.5,1,0,1,0.5,0.5,1,0,0,0,0,0,1,0.5,0,1,0.5,0.5,1,0,0.5,1,0.5,0.5,0,0,0.5,0,0,0,0.5,1,0,0,1,0,0,1,0,0,0,0,0,0,0.5,1,0,0,0.5,0,1,0,0,0,1,0.5,0,0,0] recall 0.5125 [0,1,1,1,1,1,0.5,0,0.5,1,0,1,1,0.5,1,0,1,0,1,0,0.5,1,1,1,0.5,0,0,0.5,0.5,1,1,1,0,0,1,1,0.5,0,1,0,0,0.5,0,0.5,0,0.5,1,0,0,1,0.5,0,1,0,0,0,1,0,0,0,0,1,0.5,0.5,1,0,0.5,0,0.5,1,1,0,0.5,1,0,0,1,0,1,0.5,1,1,0,0,0.5,1,0,0,0,1,1,0,0.5,0,1,0.5,0,1,0,1] recall 0.51875 [0,0,0,0.5,1,0,0,0,0.5,1,1,0,1,0,0.5,1,1,1,0.5,1,1,1,0,1,1,0,1,0.5,0,1,0,0.5,1,0,0.5,0.5,0,0.5,1,1,0,0.5,0.5,0.5,1,0.5,0,0,1,0,1,0,1,0,0.5,0,1,1,0,1,1,0,0,0.5,1,0,0,1,0.5,0,0,0,0,1,0.5,1,1,0,0,1,0.5,0,1,0,1,1,0,0,0,1,1,1,1,0.5,0,1,1,0.5,0,0] relative_absolute_error 0.6337619640872751 relative_absolute_error 0.6231637448632761 relative_absolute_error 0.602819741759692 relative_absolute_error 0.6294048243134682 relative_absolute_error 0.6465383811779749 relative_absolute_error 0.6546619580821887 relative_absolute_error 0.6377501268042706 relative_absolute_error 0.6685962504394984 relative_absolute_error 0.5856824904563309 relative_absolute_error 0.5891291339709334 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 0.08919832698161988 root_mean_squared_error 0.08891968063359783 root_mean_squared_error 0.08969157069354591 root_mean_squared_error 0.0875444435254731 root_mean_squared_error 0.0924612532843368 root_mean_squared_error 0.09072799389695693 root_mean_squared_error 0.08954843008577576 root_mean_squared_error 0.09209685241399575 root_mean_squared_error 0.08609051564249966 root_mean_squared_error 0.08517191477003264 root_relative_squared_error 0.8964769167438402 root_relative_squared_error 0.8936764155753798 root_relative_squared_error 0.901434202570085 root_relative_squared_error 0.879854762589249 root_relative_squared_error 0.9292705599701844 root_relative_squared_error 0.9118506476904865 root_relative_squared_error 0.8999955853330032 root_relative_squared_error 0.9256081934241231 root_relative_squared_error 0.8652422375587682 root_relative_squared_error 0.8560099514191737 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 285.75902600096015 usercpu_time_millis 273.99368199985474 usercpu_time_millis 216.50593600134016 usercpu_time_millis 216.64433700061636 usercpu_time_millis 217.74785299930954 usercpu_time_millis 210.36406500206795 usercpu_time_millis 214.48399500150117 usercpu_time_millis 222.27989400016668 usercpu_time_millis 221.17503799927363 usercpu_time_millis 225.8276680004201 usercpu_time_millis_testing 1.8022340009338222 usercpu_time_millis_testing 1.7890369999804534 usercpu_time_millis_testing 1.4632580005127238 usercpu_time_millis_testing 1.4572180007235147 usercpu_time_millis_testing 1.4073229995119618 usercpu_time_millis_testing 1.389356000800035 usercpu_time_millis_testing 1.455780000469531 usercpu_time_millis_testing 1.7724749995977618 usercpu_time_millis_testing 1.5014439995866269 usercpu_time_millis_testing 1.6333120001945645 usercpu_time_millis_training 283.9567920000263 usercpu_time_millis_training 272.2046449998743 usercpu_time_millis_training 215.04267800082744 usercpu_time_millis_training 215.18711899989285 usercpu_time_millis_training 216.34052999979758 usercpu_time_millis_training 208.9747090012679 usercpu_time_millis_training 213.02821500103164 usercpu_time_millis_training 220.50741900056892 usercpu_time_millis_training 219.673593999687 usercpu_time_millis_training 224.19435600022553