6018267 1 Jan van Rijn 9954 Supervised Classification 6969 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1) 4052955 bootstrap true 6902 class_weight null 6902 criterion "entropy" 6902 max_depth null 6902 max_features 0.14881114750042848 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 14 6902 min_samples_split 20 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 100 6902 n_jobs 1 6902 oob_score false 6902 random_state 67 6902 verbose 0 6902 warm_start false 6902 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 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 12959659 description https://api.openml.org/data/download/12959659/description.xml -1 12959660 predictions https://api.openml.org/data/download/12959660/predictions.arff area_under_roc_curve 0.9949183238636364 [0.98544,0.999842,1,1,0.999527,0.995068,0.999487,0.999092,0.995068,0.999921,0.995896,0.999842,0.998816,0.981889,0.999487,0.998264,0.999842,0.986072,0.989741,0.97672,0.997475,0.99929,0.992898,1,0.99712,0.969934,0.985795,0.999527,0.996409,0.999684,0.999921,1,0.99925,0.9884,0.999171,0.997909,0.994752,0.988913,0.999329,0.999921,0.998658,0.997988,0.999921,0.998422,0.998146,0.999724,0.99929,0.995699,0.998067,0.997356,0.997633,0.994437,0.997041,0.984336,0.994239,0.972854,1,0.999921,0.991832,0.977825,0.999645,0.999408,0.991951,0.998777,0.999014,0.993845,0.979403,1,0.993253,0.986427,0.996607,0.998106,0.992424,0.997909,0.97676,0.999329,0.998816,0.983625,1,0.998935,0.99416,0.996449,0.998106,0.99562,0.995028,0.999369,1,0.995541,0.995818,0.999921,0.999842,0.999171,0.997672,0.990846,0.999014,0.999605,0.997238,0.995226,0.960622,0.99925] average_cost 0 f_measure 0.752599293016417 [0.5,0.914286,0.969697,1,0.914286,0.571429,0.909091,0.882353,0.727273,0.888889,0.740741,0.914286,0.820513,0.521739,0.888889,0.903226,0.820513,0.48,0.5,0.25,0.717949,1,0.774194,1,0.83871,0.105263,0.333333,0.8,0.933333,0.842105,0.882353,1,0.941176,0.444444,0.9375,0.653061,0.588235,0.424242,0.882353,0.820513,0.777778,0.903226,1,0.810811,0.896552,0.833333,0.857143,0.758621,0.882353,0.823529,0.787879,0.689655,0.8,0.72,0.642857,0.190476,1,0.9375,0.482759,0.380952,0.909091,0.8125,0.583333,0.875,0.736842,0.6875,0.228571,0.914286,0.615385,0.75,0.6875,0.789474,0.536585,0.9375,0.583333,0.969697,0.888889,0.210526,0.941176,0.933333,0.64,0.764706,0.827586,0.685714,0.666667,0.875,1,0.882353,0.722222,0.933333,0.914286,0.714286,0.857143,0.56,0.882353,0.83871,0.733333,0.722222,0.380952,0.857143] kappa 0.7657828282828282 kb_relative_information_score 924.1829191810248 mean_absolute_error 0.01682356480981356 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 0.7754360820137367 [0.75,0.842105,0.941176,1,0.842105,0.666667,0.882353,0.833333,0.705882,0.8,0.909091,0.842105,0.695652,0.857143,0.8,0.933333,0.695652,0.666667,0.45,0.25,0.608696,1,0.8,1,0.866667,0.333333,0.5,0.666667,1,0.727273,0.833333,1,0.888889,0.545455,0.9375,0.484848,0.555556,0.411765,0.833333,0.695652,0.7,0.933333,1,0.714286,1,0.75,0.789474,0.846154,0.833333,0.777778,0.764706,0.769231,0.736842,1,0.75,0.4,1,0.9375,0.538462,0.8,0.882353,0.8125,0.875,0.875,0.636364,0.6875,0.210526,0.842105,0.8,0.75,0.6875,0.681818,0.44,0.9375,0.875,0.941176,0.8,0.666667,0.888889,1,0.888889,0.722222,0.923077,0.631579,0.714286,0.875,1,0.833333,0.65,1,0.842105,0.576923,1,0.777778,0.833333,0.866667,0.785714,0.65,0.8,0.789474] predictive_accuracy 0.768125 prior_entropy 6.6438561897747395 recall 0.768125 [0.375,1,1,1,1,0.5,0.9375,0.9375,0.75,1,0.625,1,1,0.375,1,0.875,1,0.375,0.5625,0.25,0.875,1,0.75,1,0.8125,0.0625,0.25,1,0.875,1,0.9375,1,1,0.375,0.9375,1,0.625,0.4375,0.9375,1,0.875,0.875,1,0.9375,0.8125,0.9375,0.9375,0.6875,0.9375,0.875,0.8125,0.625,0.875,0.5625,0.5625,0.125,1,0.9375,0.4375,0.25,0.9375,0.8125,0.4375,0.875,0.875,0.6875,0.25,1,0.5,0.75,0.6875,0.9375,0.6875,0.9375,0.4375,1,1,0.125,1,0.875,0.5,0.8125,0.75,0.75,0.625,0.875,1,0.9375,0.8125,0.875,1,0.9375,0.75,0.4375,0.9375,0.8125,0.6875,0.8125,0.25,0.9375] relative_absolute_error 0.8496749903946226 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08671424552327625 root_relative_squared_error 0.871510958725644 total_cost 0 area_under_roc_curve 0.9947045915930263 [1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.990506,1,0.987421,1,0.981013,0.974684,0.987421,1,1,1,1,1,0.984177,0.993671,1,0.993671,0.987421,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,0.993711,1,1,0.984177,0.996835,1,1,1,0.993711,1,1,1,0.987421,0.981013,1,1,0.987342,1,1,1,0.990506,1,1,0.943396,0.996835,1,1,1,1,1,1,0.977848,1,1,0.996835,1,1,1,0.984177,1,1,1,0.981013,1,1,1,1,0.990506,0.996835,1,1,0.996835,0.876582,1] area_under_roc_curve 0.9943888822545975 [0.968553,1,1,1,1,1,1,1,1,1,0.993671,1,0.993671,0.933544,1,1,1,0.981013,0.996835,0.981132,1,1,1,1,1,0.971519,0.990506,1,1,1,1,1,1,1,0.996835,0.993711,0.993711,1,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,1,0.971519,1,1,1,0.962264,1,1,1,0.908228,1,1,0.993671,0.996835,1,1,0.984177,1,0.974843,1,1,1,1,1,1,1,1,0.977848,1,1,0.993671,0.996835,0.993711,0.981132,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.984177,0.990506,1] area_under_roc_curve 0.9944274440729242 [1,1,1,1,0.996835,0.993671,1,1,0.987342,1,1,1,1,0.993671,0.993671,1,1,0.990506,0.987421,0.936709,0.993711,0.993711,0.939873,1,1,0.968354,1,1,0.996835,1,1,1,0.996835,0.974684,1,1,0.993711,0.962264,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.987421,0.974684,1,1,1,1,1,0.974684,0.990506,1,0.993711,0.955696,1,0.996835,0.993711,1,1,1,1,0.990506,1,1,1,0.993671,1,1,0.993711,1,1,1,0.996835,1,1,1,0.993711,0.987342,1,1,1,0.993671,0.981132,1] area_under_roc_curve 0.9955350390096329 [0.990506,0.996835,1,1,1,0.996835,1,1,0.993671,1,1,1,1,0.987342,1,1,1,1,0.943396,1,1,1,1,1,0.993711,0.955696,0.984177,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,0.974684,1,1,1,0.987342,1,1,0.996835,1,0.996835,0.993671,0.984177,1,1,0.981013,0.993671,0.996835,0.974843,1,0.993711,0.996835,1,0.987342,1,1,0.987421,0.990506,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.996835,1,0.993711,0.987342,0.996835,1,0.996835] area_under_roc_curve 0.9935226096648353 [0.990506,1,1,1,1,1,1,1,1,1,0.990506,0.996835,1,0.937107,1,1,1,1,1,0.990506,0.968354,1,1,1,1,0.981013,0.987342,1,1,1,1,1,0.996835,0.971519,1,1,0.996835,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.990506,0.974843,0.96519,0.977848,1,0.996835,0.996835,0.993711,1,1,0.981132,1,1,1,0.924528,1,0.984177,0.996835,1,1,0.993711,1,0.886076,1,0.993671,0.984177,1,1,1,1,0.996835,0.993671,0.981132,1,1,1,1,1,1,0.996835,1,0.987421,1,1,1,1,0.987421,0.993671] area_under_roc_curve 0.9951795239232546 [0.968354,1,1,1,1,0.984177,1,1,1,1,0.990506,1,1,0.987421,1,1,1,1,1,0.933544,1,0.993671,1,1,0.996835,0.987342,0.971519,1,1,1,1,1,0.996835,0.996835,0.993711,0.996835,0.987342,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.993671,0.955696,1,1,0.987342,1,1,1,0.987421,1,1,0.993671,1,1,1,1,0.993671,0.990506,1,1,0.984177,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.987421,0.930818,0.996835] area_under_roc_curve 0.995856470424329 [1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,1,0.993711,0.993671,0.974684,0.996835,1,1,1,0.993671,0.918239,0.987421,1,1,1,1,1,1,0.987421,1,1,0.996835,1,1,1,1,0.993671,1,1,0.993711,0.996835,0.996835,0.987342,1,1,1,1,1,1,0.993671,0.977848,1,1,0.987342,0.993711,0.996835,1,1,0.990506,0.996835,0.987421,0.974843,1,0.990506,1,1,1,1,1,1,1,1,0.968553,1,1,0.981013,1,1,1,1,1,1,0.955975,1,1,1,1,0.993671,1,1,1,1,1,0.955696,1] area_under_roc_curve 0.9958564704243292 [1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,0.987342,1,0.974843,0.990506,0.990506,1,1,1,1,0.993671,0.968553,0.987421,1,0.981132,0.996835,1,1,1,0.993711,1,1,0.996835,0.981013,1,1,1,1,1,1,0.993711,1,1,0.990506,1,0.987421,0.974843,1,1,1,1,0.962025,1,1,0.993671,1,1,1,1,1,1,0.968553,0.962264,1,0.990506,0.990506,1,1,1,0.993711,0.993671,1,1,0.993711,1,1,0.990506,1,0.990506,1,0.990506,1,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.987342,1,0.977848,1] area_under_roc_curve 0.9960114640554091 [0.981132,1,1,1,1,0.993711,1,1,0.996835,1,0.993711,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.993711,1,1,0.996835,0.96519,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,1,0.993671,0.996835,0.943038,0.996835,0.937107,1,1,0.993671,0.971519,0.996835,1,1,0.993671,1,1,0.996835,1,0.996835,0.993711,1,0.993711,0.981013,0.987342,1,1,1,0.993711,1,0.996835,0.996835,0.993711,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.987421,1,0.984177,1] area_under_roc_curve 0.996882712363665 [0.949686,1,1,1,1,1,1,0.974843,0.993671,1,1,1,0.996835,0.990506,1,1,1,0.968553,0.993671,0.981132,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.993671,1,0.993671,1,1,1,1,0.987342,1,1,1,0.981013,1,0.993711,0.993711,0.996835,1,1,1,0.987421,1,0.981013,1,0.990506,1,1,1,1,1,1,0.974843,1,0.981132,0.996835,1,1,0.993671,1,1,1,1,0.993671,0.993671,1,1,1,1,1,1] 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 0.7284710711184781 kappa 0.7789619104006316 kappa 0.7663127146409822 kappa 0.7283745903904615 kappa 0.7852094602598018 kappa 0.7852179406190777 kappa 0.7662758103359786 kappa 0.7915185974887466 kappa 0.7600442023837714 kappa 0.7662296635602591 kb_relative_information_score 91.31086715014611 kb_relative_information_score 92.03722732479142 kb_relative_information_score 91.43708184324609 kb_relative_information_score 92.6753217842159 kb_relative_information_score 92.50934311546808 kb_relative_information_score 92.41573877259485 kb_relative_information_score 93.12180340264739 kb_relative_information_score 92.87445639523496 kb_relative_information_score 92.7553447290902 kb_relative_information_score 93.04573466358814 mean_absolute_error 0.01688553495747075 mean_absolute_error 0.016841884793088745 mean_absolute_error 0.016883980093425847 mean_absolute_error 0.01681843546169818 mean_absolute_error 0.016761544674351876 mean_absolute_error 0.016836854722287325 mean_absolute_error 0.016768009295241568 mean_absolute_error 0.016768694480574425 mean_absolute_error 0.016836515241310954 mean_absolute_error 0.01683419437868582 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 0.73125 predictive_accuracy 0.78125 predictive_accuracy 0.76875 predictive_accuracy 0.73125 predictive_accuracy 0.7875 predictive_accuracy 0.7875 predictive_accuracy 0.76875 predictive_accuracy 0.79375 predictive_accuracy 0.7625 predictive_accuracy 0.76875 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 0.73125 [1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0.5,1,0,1,1,0,0,1,1,0,1,1,0.5,0.5,1,0,0,0.5,1,1,1,1,1,1,0,1,1,0,1,1,1,0.5,0.5,1,1,0,1,1,1,1,0.5,1,0,1,1,0,1] recall 0.78125 [0,1,1,1,1,1,0.5,1,0.5,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,1,1,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0.5,0,1] recall 0.76875 [0.5,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0.5,1,0,0,1,0,1,1,0.5,0,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,0,0,1,1,0.5,1,1,0,1,1,1,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,0,1] recall 0.73125 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0.5,1,1,1,0,0,0.5,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,0,1,1,0.5,1,0.5,0.5,0,1,0,1,0,1,0,1,0,1,1,0.5,1,0.5,1,0.5,0.5,1,0,1,1,1,1,1,1,1,0,1,0.5,0,0.5,1,0,0.5] recall 0.7875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,0,0,1,1,0.5,1,1,1,0,1,0.5,1,0,1,0.5,0.5,0.5,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1] recall 0.7875 [0,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,0,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0,0,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,1,1,1,0.5,1,0.5,0,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,0,1] recall 0.76875 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0,1,1,1,1,0.5,0,0,1,1,1,0.5,1,1,0,1,1,0.5,1,1,1,1,0.5,1,0.5,0,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,0.5,0,0.5,0,1,0.5,1,1,0,1,0.5,0.5,1,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0,1,1,1,1,1,0,1] recall 0.79375 [0.5,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0.5,1,0,0.5,0,1,1,0.5,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0,1,1,0,1,0,1,0,0.5,1,1,1,1,1,1,1,0,1,1,0.5,1,0,1] recall 0.7625 [0,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0.5,0.5,0,1,1,0,0,1,1,0,0.5,1,1,0,1,0.5,1,1,1,0.5,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1] recall 0.76875 [0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,0.5,0,1,1,0.5,0,1,1,0,1,1,1,0.5,1,0.5,1,0,0,0.5,1,0,1,1,0,1,1,1,0,1,0,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1] relative_absolute_error 0.8528047958318545 relative_absolute_error 0.8506002420751878 relative_absolute_error 0.8527262673447383 relative_absolute_error 0.8494159324089976 relative_absolute_error 0.8465426603208005 relative_absolute_error 0.8503461980953181 relative_absolute_error 0.8468691563253303 relative_absolute_error 0.8469037616451714 relative_absolute_error 0.8503290525914609 relative_absolute_error 0.8502118373073632 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 0.08701729177459605 root_mean_squared_error 0.08674284059366635 root_mean_squared_error 0.08704877834339618 root_mean_squared_error 0.08669525885509777 root_mean_squared_error 0.08647873576070692 root_mean_squared_error 0.08678989986323458 root_mean_squared_error 0.08644919146512434 root_mean_squared_error 0.08644685632699589 root_mean_squared_error 0.08674224038675932 root_mean_squared_error 0.0867290043035668 root_relative_squared_error 0.8745566881491337 root_relative_squared_error 0.8717983499963649 root_relative_squared_error 0.8748731400723025 root_relative_squared_error 0.8713201355305928 root_relative_squared_error 0.8691439965531953 root_relative_squared_error 0.8722713134511102 root_relative_squared_error 0.8688470652103384 root_relative_squared_error 0.868823596189109 root_relative_squared_error 0.8717923176899792 root_relative_squared_error 0.8716592900486353 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 1424.0809889997763 usercpu_time_millis 1267.849130000286 usercpu_time_millis 1273.9489690002301 usercpu_time_millis 1268.6637440001505 usercpu_time_millis 1265.2709610001693 usercpu_time_millis 1270.7248910001 usercpu_time_millis 1274.4365280004786 usercpu_time_millis 1278.133341999819 usercpu_time_millis 1269.6478880006907 usercpu_time_millis 1270.079171999896 usercpu_time_millis_testing 34.25661900018895 usercpu_time_millis_testing 32.947992000117665 usercpu_time_millis_testing 33.00683999987086 usercpu_time_millis_testing 32.561248000092746 usercpu_time_millis_testing 32.53676199983602 usercpu_time_millis_testing 32.56759400028386 usercpu_time_millis_testing 32.63373400022829 usercpu_time_millis_testing 32.46321799997531 usercpu_time_millis_testing 32.66354800052795 usercpu_time_millis_testing 32.619235000311164 usercpu_time_millis_training 1389.8243699995874 usercpu_time_millis_training 1234.9011380001684 usercpu_time_millis_training 1240.9421290003593 usercpu_time_millis_training 1236.1024960000577 usercpu_time_millis_training 1232.7341990003333 usercpu_time_millis_training 1238.157296999816 usercpu_time_millis_training 1241.8027940002503 usercpu_time_millis_training 1245.6701239998438 usercpu_time_millis_training 1236.9843400001628 usercpu_time_millis_training 1237.459936999585