4905831 1 Jan van Rijn 9954 Supervised Classification 6970 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier))(1) 2941228 class_weight null 6941 criterion "gini" 6941 max_depth 4 6941 max_features null 6941 max_leaf_nodes null 6941 min_impurity_split 1e-07 6941 min_samples_leaf 1 6941 min_samples_split 2 6941 min_weight_fraction_leaf 0.0 6941 presort false 6941 random_state 35430 6941 splitter "best" 6941 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "median" 6947 strategy_nominal "most_frequent" 6947 verbose 0 6947 categorical_features [] 6948 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 6948 handle_unknown "ignore" 6948 n_values "auto" 6948 sparse false 6948 threshold 0.0 6949 algorithm "SAMME.R" 6971 learning_rate 0.31702212960522697 6971 n_estimators 76 6971 random_state 60763 6971 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 10745763 description https://api.openml.org/data/download/10745763/description.xml -1 10745764 predictions https://api.openml.org/data/download/10745764/predictions.arff area_under_roc_curve 0.9827422664141414 [0.967251,0.993016,0.998501,0.999842,0.99053,0.984099,0.996567,0.992069,0.990728,0.999961,0.994831,0.99783,0.982008,0.955019,0.999605,0.978772,0.999408,0.946851,0.971117,0.936514,0.986979,0.990096,0.982757,0.999408,0.988873,0.946674,0.951547,0.983902,0.991359,0.999448,0.997356,0.997593,0.980548,0.981258,0.985361,0.995147,0.965041,0.945549,0.995975,0.999566,0.99053,0.987413,0.999448,0.989938,0.993805,0.99491,0.997396,0.989149,0.984257,0.985953,0.971315,0.983862,0.982323,0.958452,0.988834,0.941465,1,0.998501,0.984099,0.964015,0.994949,0.989504,0.986072,0.989899,0.995857,0.973801,0.948587,0.999645,0.982876,0.970683,0.96441,0.996725,0.973051,0.990885,0.94113,0.988163,0.997909,0.932884,0.998737,0.999605,0.98256,0.993292,0.983744,0.990175,0.977865,0.994673,1,0.990412,0.9927,0.992503,0.964173,0.99925,0.986348,0.973564,0.986466,0.991911,0.969342,0.985835,0.899897,0.979522] average_cost 0 f_measure 0.5162996284510292 [0.233333,0.48,0.814815,0.857143,0.814815,0.521739,0.692308,0.689655,0.555556,0.857143,0.5,0.608696,0.411765,0.388889,0.896552,0.415094,0.222222,0.173913,0.095238,0.133333,0.222222,0.666667,0.275862,0.857143,0.5,0.157895,0.090909,0.606061,0.615385,0.814815,0.666667,0.769231,0.5,0.142857,0.4375,0.521739,0.16,0.307692,0.785714,0.4,0.487805,0.5625,0.608696,0.482759,0.62069,0.5,0.608696,0.416667,0.580645,0.538462,0.421053,0.47619,0.594595,0.238095,0.533333,0.111111,0.933333,0.72,0.390244,0.4,0.545455,0.705882,0.333333,0.608696,0.454545,0.27027,0.185185,0.814815,0.390244,0.25974,0.472727,0.545455,0.190476,0.827586,0.484848,0.580645,0.814815,0.216216,0.814815,0.857143,0.5,0.758621,0.5,0.424242,0.424242,0.8125,0.896552,0.580645,0.8125,0.6,0.521739,0.740741,0.5,0.55814,0.52381,0.631579,0.363636,0.4375,0.066667,0.518519] kappa 0.4880050505050505 kb_relative_information_score 930.6339300173772 mean_absolute_error 0.01574489940503141 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 0.6224957939631001 [0.159091,0.666667,1,1,1,0.857143,0.9,0.769231,0.5,1,0.583333,1,0.388889,0.35,1,0.297297,1,0.285714,0.2,0.142857,1,0.714286,0.307692,1,0.75,0.136364,0.071429,0.588235,0.8,1,1,1,0.5,0.166667,0.4375,0.857143,0.222222,0.4,0.916667,1,0.4,0.5625,1,0.538462,0.692308,0.35,1,0.625,0.6,0.7,0.363636,1,0.52381,0.192308,0.571429,0.1,1,1,0.32,0.310345,1,0.666667,0.357143,1,0.833333,0.238095,0.131579,1,0.32,0.163934,0.333333,1,0.4,0.923077,0.470588,0.6,1,0.137931,1,1,0.5,0.846154,0.583333,0.411765,0.411765,0.8125,1,0.6,0.8125,0.642857,0.857143,0.909091,0.583333,0.444444,0.423077,0.545455,0.352941,0.4375,0.045455,0.636364] predictive_accuracy 0.493125 prior_entropy 6.6438561897747395 recall 0.493125 [0.4375,0.375,0.6875,0.75,0.6875,0.375,0.5625,0.625,0.625,0.75,0.4375,0.4375,0.4375,0.4375,0.8125,0.6875,0.125,0.125,0.0625,0.125,0.125,0.625,0.25,0.75,0.375,0.1875,0.125,0.625,0.5,0.6875,0.5,0.625,0.5,0.125,0.4375,0.375,0.125,0.25,0.6875,0.25,0.625,0.5625,0.4375,0.4375,0.5625,0.875,0.4375,0.3125,0.5625,0.4375,0.5,0.3125,0.6875,0.3125,0.5,0.125,0.875,0.5625,0.5,0.5625,0.375,0.75,0.3125,0.4375,0.3125,0.3125,0.3125,0.6875,0.5,0.625,0.8125,0.375,0.125,0.75,0.5,0.5625,0.6875,0.5,0.6875,0.75,0.5,0.6875,0.4375,0.4375,0.4375,0.8125,0.8125,0.5625,0.8125,0.5625,0.375,0.625,0.4375,0.75,0.6875,0.75,0.375,0.4375,0.125,0.4375] relative_absolute_error 0.7951969396480496 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08477810868878188 root_relative_squared_error 0.852052051383813 total_cost 0 area_under_roc_curve 0.9849044164477349 [0.968553,0.974684,1,1,0.996835,1,1,1,1,1,0.993671,1,0.984177,0.939873,1,0.974843,1,0.939873,0.968354,0.949686,0.974843,0.993711,0.987421,1,0.993711,0.958861,0.946203,1,1,1,1,1,0.974684,0.936709,0.993671,0.993711,0.993711,0.987421,1,1,0.993711,0.987421,1,1,1,0.990506,0.993711,0.968553,0.996835,0.955696,0.958861,1,0.987421,0.977848,1,0.949686,1,1,0.993711,0.987342,1,0.993711,0.981013,0.987342,1,0.987342,0.958861,1,1,0.943396,0.936709,0.993711,0.993671,1,1,0.996835,1,0.924051,1,1,0.993671,1,0.993711,1,0.968354,0.996835,1,0.990506,1,1,1,1,1,0.946203,0.993671,0.990506,1,0.993671,0.870253,1] area_under_roc_curve 0.9842744904864261 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[0.974684,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.981013,1,0.977848,0.974843,0.955696,1,0.993711,0.981013,1,0.968553,0.985759,0.981013,0.974843,1,1,1,1,0.977848,0.958861,0.943396,1,0.930818,0.993711,0.993711,1,0.996835,0.943396,1,1,0.993671,1,1,0.987421,0.946203,1,0.996835,1,1,0.990506,0.987421,0.973101,1,0.996835,0.943396,0.96519,1,1,0.990506,0.949686,0.996835,0.949367,0.971519,1,0.993711,0.984177,0.984177,0.996835,1,1,1,0.990506,1,0.955696,0.996835,1,1,0.996835,0.996835,0.990506,1,1,1,0.987342,0.996835,0.993711,0.996835,1,1,0.993671,0.968354,0.987421,0.968354,0.987342,0.949686,0.993671] area_under_roc_curve 0.9844021176657909 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[0.974843,0.984177,1,1,0.96519,0.949686,0.996835,1,0.987342,1,0.968553,1,0.993671,0.993671,1,0.981132,1,0.90566,0.958861,0.886792,0.993671,1,0.955975,1,1,0.91195,0.981132,1,1,1,1,1,1,1,0.993671,1,0.984177,0.977848,1,1,1,1,0.996835,0.977848,0.987342,1,1,0.990506,0.987421,0.993711,0.993671,0.993671,0.996835,0.924051,0.96519,0.930818,1,0.993711,1,0.96519,1,1,0.993671,0.993671,1,1,0.892405,1,0.984177,0.899371,1,0.974843,0.89557,1,1,0.990506,1,0.987421,1,1,0.984177,0.974843,1,0.974843,0.990506,1,1,1,1,1,0.949367,1,0.977848,0.977848,0.993711,0.993671,0.981132,1,0.936709,0.993711] area_under_roc_curve 0.9885108172120048 [1,1,1,1,1,1,1,1,0.987342,1,1,1,0.974684,0.993671,1,0.949686,1,0.91195,0.990506,0.955975,0.968354,0.987342,1,1,0.990506,0.993711,0.943396,0.987342,0.993671,1,1,1,1,1,0.996835,0.996835,0.971519,0.981013,1,1,1,1,1,1,0.993671,1,1,0.993671,0.993711,0.993711,1,1,1,0.939873,0.996835,0.974843,1,1,0.968354,0.981013,0.993671,0.993671,0.971519,0.993671,0.987421,0.987421,0.958861,1,0.993671,0.962264,1,1,0.971519,0.993671,0.952532,0.984177,1,0.981132,1,1,0.996835,0.974843,0.974843,0.981132,0.993671,1,1,0.993671,1,1,0.946203,0.996835,0.984177,0.971519,1,0.993671,0.880503,0.993671,0.987342,1] 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 0.4947700809157292 kappa 0.5074591522614255 kappa 0.5008687411151477 kappa 0.47569979075368163 kappa 0.4947900221029365 kappa 0.48211889160811555 kappa 0.4884950862375183 kappa 0.5012626262626262 kappa 0.457284846572533 kappa 0.4759067050791271 kb_relative_information_score 94.74603407250119 kb_relative_information_score 94.16007862480836 kb_relative_information_score 92.44043200415622 kb_relative_information_score 90.37776993458885 kb_relative_information_score 98.59478610770441 kb_relative_information_score 91.65775580327907 kb_relative_information_score 91.45692519960846 kb_relative_information_score 91.66605023742837 kb_relative_information_score 93.50251576735285 kb_relative_information_score 92.03158226594972 mean_absolute_error 0.015511259505929062 mean_absolute_error 0.015403934267935807 mean_absolute_error 0.01586508890224021 mean_absolute_error 0.016223722287000534 mean_absolute_error 0.015342704953842027 mean_absolute_error 0.015866180651320325 mean_absolute_error 0.016070598937701037 mean_absolute_error 0.015850664948693794 mean_absolute_error 0.015460742041679423 mean_absolute_error 0.015854097553971668 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 0.5 predictive_accuracy 0.5125 predictive_accuracy 0.50625 predictive_accuracy 0.48125 predictive_accuracy 0.5 predictive_accuracy 0.4875 predictive_accuracy 0.49375 predictive_accuracy 0.50625 predictive_accuracy 0.4625 predictive_accuracy 0.48125 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 0.5 [0,0.5,1,1,0.5,1,0.5,1,1,1,0.5,0.5,0.5,0,0.5,1,0,0.5,0,0,0,1,0,0,0,0.5,0,1,0.5,0,0.5,0.5,0.5,0,0,0,0,1,1,0.5,0,0,0,0.5,1,1,0,0,1,0,0.5,0,1,0,0,0,1,1,1,1,1,1,0.5,0,0,0,0.5,1,1,1,0.5,0,0,1,1,1,0.5,0,1,0.5,0.5,1,0,1,0.5,1,0.5,0.5,0,0,0.5,1,0.5,0.5,0.5,1,1,1,0,1] recall 0.5125 [1,1,0.5,0,1,1,0.5,1,0.5,1,0,0.5,0.5,0.5,1,1,0,0,0,0,0,0,0,1,1,0,0,0,0,1,0,1,0.5,0.5,0,0,0,0,0,1,1,0,0.5,0,0.5,0.5,1,1,0,0.5,0.5,0,1,0,1,0,1,1,1,0,1,1,0,0.5,1,1,0,1,0,1,1,1,0,1,1,1,0.5,1,1,1,0.5,0.5,0,0,1,1,1,0.5,1,0,0,1,0.5,0.5,0.5,1,1,0.5,0,0] recall 0.50625 [0.5,0,1,0,1,1,0,1,1,0.5,0.5,0.5,1,1,0.5,0.5,0,0,0,0,0,0,0,1,0,0.5,0,0,1,1,0,1,0.5,0,0,1,0,0,1,0,0.5,0,1,1,1,1,0,0,0.5,0.5,1,1,1,1,0,0,1,0.5,0,0.5,0,1,0,0,0,0,0,1,0,0.5,1,0.5,1,1,1,0.5,1,0.5,0,0.5,1,1,0.5,0,1,1,1,0.5,1,0,1,0,0,1,0.5,0,0,0.5,0,1] recall 0.48125 [0,0,0.5,1,0.5,0,1,1,0.5,0.5,0.5,0,0,1,1,0.5,0,0,0,0,0,0,1,1,0,0,0,1,0.5,1,1,0.5,0.5,0,1,1,0,0,1,0,0.5,1,0,1,1,1,0.5,0,0.5,1,0.5,0,1,0.5,1,0,1,0.5,0,0,0,1,0.5,0,0,0.5,1,0.5,1,1,0,0,0,1,1,0,1,0,1,1,1,1,0,0.5,0,1,1,0,1,1,0,1,0,1,1,0,0,0.5,0,0.5] recall 0.5 [0.5,1,0.5,0.5,1,0,0,0.5,1,1,1,0.5,0,0,1,1,0.5,0,0,0.5,0,1,0,1,0,0,0,1,1,0,0,1,0,0,0,0.5,0,0,1,0,0.5,0.5,0,1,1,1,1,1,1,0.5,1,0,1,1,0.5,0.5,1,0.5,0,1,0.5,1,0,0,0.5,0.5,0,0.5,0.5,0.5,1,0.5,0,0,0.5,0,0,1,1,1,1,0.5,1,0.5,0,1,0,0,1,0.5,0,0.5,1,1,0.5,1,0.5,0,0,0] recall 0.4875 [0,1,0,1,1,0,1,0,0,0.5,0.5,0,0,0,1,1,0.5,0,0,0,0.5,0,0.5,1,1,0.5,0,0,1,0.5,1,1,0.5,0,1,0,0.5,0.5,1,0,0.5,1,0,0,1,1,0.5,0,0.5,0.5,0,0,0.5,0,1,0,1,0.5,0.5,1,0.5,1,1,1,0.5,0,1,1,1,0.5,1,0,0,1,0.5,0,1,0.5,0,0,0,0.5,0.5,0,0,1,1,1,1,0.5,0,0.5,0,1,0.5,1,0.5,1,0,1] recall 0.49375 [0.5,1,1,1,1,0,0.5,0.5,1,1,0,0,0.5,0,1,0.5,0,1,0,0,0.5,1,0,0.5,0.5,0,0,0.5,1,1,0.5,0,0,0,0.5,0,0,0,0.5,0,0.5,0,1,0,0,1,0,0,1,1,0,0.5,1,0,0.5,0,0.5,0.5,0.5,1,0.5,1,1,0.5,0,0,0,1,0.5,1,1,0.5,0.5,0,0.5,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,0.5,1,0.5,0.5,0,1,0.5,0.5,0,0,0.5] recall 0.50625 [0.5,0,1,0.5,0,1,0.5,0.5,1,0.5,0,1,0.5,1,0,0.5,0,0,0.5,0.5,0,1,0.5,1,0,0,1,0.5,0,1,0,1,1,0,0.5,0.5,0,0.5,0.5,1,1,0.5,1,1,0,1,0.5,0,0,0,0,0.5,0,0,0.5,0,1,1,0.5,0,0,0,1,1,0.5,0,0,0,0,0.5,1,0.5,0,1,0,1,1,1,1,1,0.5,1,0,1,0,0.5,1,1,1,1,0,1,1,1,1,1,0,0,0.5,0] recall 0.4625 [1,0,1,1,0,0,0.5,0,0,1,0,1,1,0,1,1,0,0,0,0,0,1,0,0.5,1,0,1,1,0.5,1,1,0,1,0,0.5,1,0.5,0.5,0.5,0,1,1,0.5,0,0,0.5,0,0.5,0,0,0,0.5,0.5,0,0,0,0.5,0,1,0.5,0.5,0.5,0,0.5,1,1,0,0,0.5,0,1,0,0,0.5,0.5,0.5,0,1,1,1,0.5,0,1,0,0.5,1,1,0.5,1,0.5,0.5,0,0,0.5,1,1,1,0.5,0,0] recall 0.48125 [1,0,1,1,1,0,1,1,0.5,1,1,1,0,0.5,1,0,0,0,0,0,0,0.5,0,0.5,0,0,0,1,0,0,1,0.5,1,1,1,0,0,0,0.5,0,1,1,0,0.5,0,1,1,0.5,1,0,1,0.5,0.5,0.5,0.5,1,1,0,0.5,1,0,0.5,0,0.5,0,0,0.5,1,0.5,0,1,1,0,0.5,0,0.5,1,0,0,0.5,0.5,0,0,0,0.5,0,0.5,1,1,1,0.5,1,0.5,1,1,0.5,0,0,0.5,0] relative_absolute_error 0.7833969447438908 relative_absolute_error 0.7779764781785748 relative_absolute_error 0.8012671162747567 relative_absolute_error 0.8193799134848742 relative_absolute_error 0.7748840885778787 relative_absolute_error 0.8013222551171869 relative_absolute_error 0.8116464109950006 relative_absolute_error 0.8005386337724124 relative_absolute_error 0.7808455576605756 relative_absolute_error 0.8007119976753354 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 0.08412994687530786 root_mean_squared_error 0.08322904796122227 root_mean_squared_error 0.08493336282218565 root_mean_squared_error 0.0861709157069149 root_mean_squared_error 0.08288074248159837 root_mean_squared_error 0.08568754517692352 root_mean_squared_error 0.08609684953098935 root_mean_squared_error 0.08477290672923764 root_mean_squared_error 0.08428145272700405 root_mean_squared_error 0.0855289573631874 root_relative_squared_error 0.8455377800543294 root_relative_squared_error 0.8364834052905096 root_relative_squared_error 0.8536124141342739 root_relative_squared_error 0.8660502886096343 root_relative_squared_error 0.8329828035076693 root_relative_squared_error 0.8611922319954018 root_relative_squared_error 0.865305895533266 root_relative_squared_error 0.8519997697232589 root_relative_squared_error 0.8470604711562066 root_relative_squared_error 0.8595983644969611 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 1962.6054760000002 usercpu_time_millis 1734.5176350000004 usercpu_time_millis 1807.3303790000004 usercpu_time_millis 1792.5932159999993 usercpu_time_millis 1736.6713889999996 usercpu_time_millis 1821.3697339999992 usercpu_time_millis 1699.9004739999996 usercpu_time_millis 1810.1651099999997 usercpu_time_millis 1728.1233740000027 usercpu_time_millis 1784.982444000004 usercpu_time_millis_testing 100.16089900000003 usercpu_time_millis_testing 100.1164990000003 usercpu_time_millis_testing 99.15288500000052 usercpu_time_millis_testing 105.73705399999866 usercpu_time_millis_testing 96.8647960000002 usercpu_time_millis_testing 107.86137200000034 usercpu_time_millis_testing 100.62922899999904 usercpu_time_millis_testing 96.95341400000146 usercpu_time_millis_testing 102.9686500000011 usercpu_time_millis_testing 95.23296800000125 usercpu_time_millis_training 1862.4445770000002 usercpu_time_millis_training 1634.4011360000002 usercpu_time_millis_training 1708.177494 usercpu_time_millis_training 1686.8561620000007 usercpu_time_millis_training 1639.8065929999993 usercpu_time_millis_training 1713.508361999999 usercpu_time_millis_training 1599.2712450000006 usercpu_time_millis_training 1713.2116959999983 usercpu_time_millis_training 1625.1547240000016 usercpu_time_millis_training 1689.749476000003