5948393 1 Jan van Rijn 9954 Supervised Classification 6952 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1) 3983138 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "mean" 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 C 6975.68903191981 6953 cache_size 200 6953 class_weight null 6953 coef0 0.0 6953 decision_function_shape null 6953 degree 3 6953 gamma 0.04861840759955716 6953 kernel "rbf" 6953 max_iter -1 6953 probability true 6953 random_state 60621 6953 shrinking true 6953 tol 0.00012781278448085123 6953 verbose false 6953 openml-pimp openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 12820270 description https://api.openml.org/data/download/12820270/description.xml -1 12820271 predictions https://api.openml.org/data/download/12820271/predictions.arff area_under_roc_curve 0.9963604797979795 [0.994318,1,1,1,0.999684,0.996449,0.999645,0.999961,0.997475,0.999921,0.999014,1,0.999803,0.986269,0.999724,1,1,0.99274,0.979048,0.974787,0.997869,0.999803,0.996173,1,0.995423,0.972183,0.982442,0.998935,0.998974,1,0.999882,1,0.999882,0.988873,0.999763,0.999171,0.993213,0.980982,0.999961,1,0.999211,1,1,0.998935,0.999369,0.999645,0.999724,0.997199,0.998264,0.998106,0.996488,0.999014,0.999092,0.988321,0.997672,0.987689,1,0.999763,0.99345,0.994634,0.999092,0.999527,0.995975,0.998658,0.999369,0.99274,0.988163,0.999921,0.994515,0.994397,0.995265,0.998343,0.993884,0.999961,0.988873,0.999803,0.999882,0.992188,1,0.999684,0.992424,0.998146,0.999132,0.995541,0.998619,1,1,0.999645,0.999448,1,0.996133,0.999487,0.999132,0.99854,0.999487,0.997514,0.99929,0.997869,0.969736,0.996725] average_cost 0 f_measure 0.8244753064663338 [0.722222,0.9375,1,1,0.967742,0.758621,0.969697,0.967742,0.8125,0.896552,0.9375,0.941176,0.967742,0.758621,1,0.967742,1,0.516129,0.352941,0.216216,0.8125,0.9375,0.727273,1,0.848485,0.4,0.486486,0.8125,0.903226,0.896552,0.969697,1,0.875,0.470588,0.941176,0.714286,0.413793,0.470588,0.933333,0.967742,0.882353,0.969697,0.967742,0.848485,0.967742,0.903226,0.9375,0.83871,0.722222,0.903226,0.857143,0.769231,0.882353,0.758621,0.8125,0.5,1,0.9375,0.702703,0.727273,0.8,0.9375,0.756757,0.909091,0.903226,0.571429,0.484848,1,0.8125,0.764706,0.733333,0.857143,0.545455,0.967742,0.8,0.969697,0.933333,0.625,1,0.9375,0.787879,0.933333,0.903226,0.642857,0.83871,0.933333,0.933333,0.848485,0.9375,1,0.8,0.866667,0.909091,0.909091,0.969697,0.833333,0.875,0.857143,0.333333,0.866667] kappa 0.8207070707070707 kb_relative_information_score 981.9033812014559 mean_absolute_error 0.01617732739893312 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 0.8328277986633246 [0.65,0.9375,1,1,1,0.846154,0.941176,1,0.8125,1,0.9375,0.888889,1,0.846154,1,1,1,0.533333,0.333333,0.190476,0.8125,0.9375,0.705882,1,0.823529,0.428571,0.428571,0.8125,0.933333,1,0.941176,1,0.875,0.444444,0.888889,0.833333,0.461538,0.444444,1,1,0.833333,0.941176,1,0.823529,1,0.933333,0.9375,0.866667,0.65,0.933333,0.789474,1,0.833333,0.846154,0.8125,0.5,1,0.9375,0.619048,0.705882,0.857143,0.9375,0.666667,0.882353,0.933333,0.526316,0.470588,1,0.8125,0.722222,0.785714,1,0.529412,1,0.736842,0.941176,1,0.625,1,0.9375,0.764706,1,0.933333,0.75,0.866667,1,1,0.823529,0.9375,1,0.857143,0.928571,0.882353,0.882353,0.941176,0.75,0.875,0.789474,0.357143,0.928571] predictive_accuracy 0.8225 prior_entropy 6.6438561897747395 recall 0.8225 [0.8125,0.9375,1,1,0.9375,0.6875,1,0.9375,0.8125,0.8125,0.9375,1,0.9375,0.6875,1,0.9375,1,0.5,0.375,0.25,0.8125,0.9375,0.75,1,0.875,0.375,0.5625,0.8125,0.875,0.8125,1,1,0.875,0.5,1,0.625,0.375,0.5,0.875,0.9375,0.9375,1,0.9375,0.875,0.9375,0.875,0.9375,0.8125,0.8125,0.875,0.9375,0.625,0.9375,0.6875,0.8125,0.5,1,0.9375,0.8125,0.75,0.75,0.9375,0.875,0.9375,0.875,0.625,0.5,1,0.8125,0.8125,0.6875,0.75,0.5625,0.9375,0.875,1,0.875,0.625,1,0.9375,0.8125,0.875,0.875,0.5625,0.8125,0.875,0.875,0.875,0.9375,1,0.75,0.8125,0.9375,0.9375,1,0.9375,0.875,0.9375,0.3125,0.8125] relative_absolute_error 0.817036737319853 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08298186682548471 root_relative_squared_error 0.8339991414041595 total_cost 0 area_under_roc_curve 0.9962463179683146 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,0.96519,0.987421,0.993711,1,1,1,0.993711,0.990506,1,1,1,1,1,1,1,0.981013,1,0.993711,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.943396,0.993671,1,1,1,1,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,1,1,0.984177,1,0.987342,0.914557,1] area_under_roc_curve 0.9966819421224423 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.939873,1,1,1,0.987342,1,0.987421,1,1,1,1,1,0.977848,0.987342,1,1,1,1,1,1,1,1,0.993711,0.993711,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,0.993671,1,1,0.996835,1,1,0.996835,0.993671,1,0.949686,1,0.993671,1,1,1,1,1,1,0.993671,1,0.993671,0.974684,0.996835,0.993711,0.987421,0.996835,1,1,1,1,1,0.993671,1,1,0.990506,1,1,1,0.993671,0.996835,0.987421] area_under_roc_curve 0.9964075312475119 [1,1,1,1,1,1,0.993711,1,0.996835,1,1,1,1,1,0.996835,1,1,0.990506,0.981132,0.955696,0.993711,1,0.987342,1,1,0.955696,0.996835,0.993711,1,1,1,1,1,0.987342,1,1,0.987421,0.955975,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.984177,1,1,1,0.974843,0.996835,0.996835,0.993671,1,0.987421,0.996835,0.993671,0.996835,1,1,0.993711,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.981132,1] area_under_roc_curve 0.9956581880423533 [1,1,1,1,1,0.990506,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.90566,0.96519,1,1,1,1,0.974843,0.984177,0.981013,1,1,1,1,1,1,0.987342,1,1,1,0.949686,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.984177,1,1,0.993711,0.993671,1,1,1,1,1,0.993671,0.974684,1,1,0.990506,0.977848,0.993671,0.981132,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,0.962264,0.987342] area_under_roc_curve 0.9962475618979381 [1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.943396,1,1,1,0.996835,1,0.990506,1,1,1,1,0.993671,0.993671,0.971519,1,1,1,1,1,1,0.974684,1,1,0.987342,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.981132,1,1,0.993671,0.993671,1,0.996835,1,0.993711,1,1,1,1,1,1,0.987421,1,0.996835,1,1,1,1,1,0.936709,1,0.996835,0.981013,1,1,1,1,1,0.996835,0.987421,1,1,1,1,1,1,0.996835,1,1,0.996835,0.993711,1,1,0.987421,1] area_under_roc_curve 0.9962833870710931 [0.958861,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993671,0.993711,0.949367,1,1,0.993671,1,0.993671,0.984177,0.993671,1,1,1,1,1,1,0.993671,1,1,0.993671,0.977848,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.968354,1,1,1,1,0.993671,1,0.987421,1,1,0.987342,1,1,1,1,1,0.987342,1,1,0.987342,1,1,0.993671,1,1,0.987421,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671] area_under_roc_curve 0.995697993790303 [1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.984177,0.968354,0.996835,1,1,1,1,0.886792,0.918239,0.996835,1,1,0.996835,1,1,0.993711,1,1,0.974684,0.987342,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,1,0.993671,1,1,1,1,0.996835,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,0.933544,1] area_under_roc_curve 0.9974347683305469 [1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993711,0.974684,1,1,1,1,1,0.993671,0.974843,0.993711,1,0.993711,1,1,1,1,0.993711,1,1,0.987342,0.990506,1,1,1,1,1,1,1,1,1,0.984177,1,1,0.974843,1,1,1,1,0.971519,1,1,0.993671,1,1,1,1,1,1,0.981132,0.949686,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1] area_under_roc_curve 0.9969988953904944 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.981132,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,0.993711,1,1,0.996835,0.955696,1,1,1,1,1,0.993671,1,0.990506,1,1,0.993711,1,0.993671,1,1,0.936709,0.996835,0.949686,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.987421,0.974684,1,1,1,1,0.987421,1,0.996835,1,1,1,0.993711,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1] area_under_roc_curve 0.9977502288830508 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.981132,0.971519,0.968553,0.996835,1,1,1,1,0.993711,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,0.990506,0.987342,1,1,0.987342,1,0.993711,0.993711,0.996835,1,1,0.993711,1,1,0.990506,1,1,1,1,1,1,1,1,0.987421,1,0.993711,1,1,1,0.996835,1,1,0.993671,1,1,0.996835,1,1,0.993711,1,0.996835,1] 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 0.8294781716270625 kappa 0.8167673656359831 kappa 0.8610123983258312 kappa 0.7852518553608084 kappa 0.7915021323645554 kappa 0.8104639684106614 kappa 0.842047069973148 kappa 0.8357419252941641 kappa 0.8230857323381905 kappa 0.8105313018078472 kb_relative_information_score 98.94080858865034 kb_relative_information_score 97.23246302993917 kb_relative_information_score 98.4053391778786 kb_relative_information_score 97.18372452771322 kb_relative_information_score 97.23627284710444 kb_relative_information_score 96.04054050751265 kb_relative_information_score 100.75568685593909 kb_relative_information_score 98.66391732786633 kb_relative_information_score 98.90465551751473 kb_relative_information_score 98.53997282133689 mean_absolute_error 0.016066116524537318 mean_absolute_error 0.016291882357247642 mean_absolute_error 0.016162281687750885 mean_absolute_error 0.016266587715870046 mean_absolute_error 0.01630331444304117 mean_absolute_error 0.01642840642729982 mean_absolute_error 0.0158664633533644 mean_absolute_error 0.01614123271123941 mean_absolute_error 0.0160936170614776 mean_absolute_error 0.016153371707502855 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 0.83125 predictive_accuracy 0.81875 predictive_accuracy 0.8625 predictive_accuracy 0.7875 predictive_accuracy 0.79375 predictive_accuracy 0.8125 predictive_accuracy 0.84375 predictive_accuracy 0.8375 predictive_accuracy 0.825 predictive_accuracy 0.8125 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 0.83125 [1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1] recall 0.81875 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,1,1,1,0.5,0.5,1,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0] recall 0.8625 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.7875 [1,1,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,0.5,0,0.5,1,1,0.5,0,0.5,0,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,0.5] recall 0.79375 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,0,1,0,1,0.5,1,1,0,0,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,1,0,1] recall 0.8125 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0,1,0,1,0.5,1,1,1,0.5,1,0,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5] recall 0.84375 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.8375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1] recall 0.825 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,0.5,0,0,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0.5,1] recall 0.8125 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] relative_absolute_error 0.8114200264917824 relative_absolute_error 0.8228223412751321 relative_absolute_error 0.81627685291671 relative_absolute_error 0.8215448341348496 relative_absolute_error 0.8233997193455121 relative_absolute_error 0.8297174963282724 relative_absolute_error 0.8013365329982008 relative_absolute_error 0.8152137732949184 relative_absolute_error 0.8128089424988673 relative_absolute_error 0.8158268539142842 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 0.08248120744432266 root_mean_squared_error 0.08338728748407379 root_mean_squared_error 0.08286440069273505 root_mean_squared_error 0.0834781168288494 root_mean_squared_error 0.0835657185559881 root_mean_squared_error 0.08427296586877404 root_mean_squared_error 0.08154791138420762 root_mean_squared_error 0.08273306369977088 root_mean_squared_error 0.08256220205259167 root_mean_squared_error 0.08289576014124318 root_relative_squared_error 0.8289673252978386 root_relative_squared_error 0.838073772333853 root_relative_squared_error 0.8328185623499033 root_relative_squared_error 0.8389866415962001 root_relative_squared_error 0.8398670720807643 root_relative_squared_error 0.8469751750216674 root_relative_squared_error 0.8195873469653582 root_relative_squared_error 0.8314985759051889 root_relative_squared_error 0.8297813517392632 root_relative_squared_error 0.8331337366660666 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 1390.4889670000002 usercpu_time_millis 1394.1362740000004 usercpu_time_millis 1398.472871 usercpu_time_millis 1396.0131499999998 usercpu_time_millis 1385.6656430000003 usercpu_time_millis 1404.9358539999996 usercpu_time_millis 1411.9353340000007 usercpu_time_millis 1412.917010000001 usercpu_time_millis 1393.9252470000001 usercpu_time_millis 1387.8165909999982 usercpu_time_millis_testing 157.98679900000013 usercpu_time_millis_testing 153.0356580000003 usercpu_time_millis_testing 153.36287099999967 usercpu_time_millis_testing 154.1039780000002 usercpu_time_millis_testing 153.03833499999973 usercpu_time_millis_testing 151.24827200000013 usercpu_time_millis_testing 162.6207940000004 usercpu_time_millis_testing 153.34652599999998 usercpu_time_millis_testing 154.35475600000004 usercpu_time_millis_testing 151.00122899999846 usercpu_time_millis_training 1232.502168 usercpu_time_millis_training 1241.1006160000002 usercpu_time_millis_training 1245.1100000000004 usercpu_time_millis_training 1241.9091719999997 usercpu_time_millis_training 1232.6273080000005 usercpu_time_millis_training 1253.6875819999996 usercpu_time_millis_training 1249.3145400000003 usercpu_time_millis_training 1259.570484000001 usercpu_time_millis_training 1239.5704910000002 usercpu_time_millis_training 1236.8153619999998