5845518 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) 3880268 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "most_frequent" 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 19.11102575126262 6953 cache_size 200 6953 class_weight null 6953 coef0 -0.6862110190625523 6953 decision_function_shape null 6953 degree 3 6953 gamma 2.532290648685127 6953 kernel "sigmoid" 6953 max_iter -1 6953 probability true 6953 random_state 60297 6953 shrinking true 6953 tol 0.007919585831678582 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 12615017 description https://api.openml.org/data/download/12615017/description.xml -1 12615019 predictions https://api.openml.org/data/download/12615019/predictions.arff area_under_roc_curve 0.9951207386363636 [0.993963,0.999882,1,1,0.999605,0.994949,0.99925,1,0.993805,0.999842,0.998264,0.999645,0.999724,0.976523,0.999763,1,1,0.981771,0.97676,0.97242,0.993253,0.999882,0.994634,1,0.99562,0.973761,0.975063,0.998856,0.998619,1,0.999803,1,0.999369,0.989465,0.999329,0.998856,0.986269,0.975892,0.999842,1,0.997711,0.999369,1,0.998856,0.999171,0.999211,0.999566,0.997277,0.99858,0.995975,0.996488,0.996804,0.998422,0.985874,0.997751,0.972617,1,0.999684,0.993884,0.988439,0.999092,0.998935,0.994831,0.998303,0.999171,0.992898,0.986072,0.999921,0.99345,0.9942,0.991596,0.997751,0.991438,1,0.983467,0.999605,0.999842,0.99128,1,0.999487,0.994634,0.997514,0.999329,0.995265,0.99858,0.999763,1,0.998856,0.999408,0.999803,0.993095,0.999448,0.998619,0.997159,0.999408,0.997711,0.996725,0.99779,0.961608,0.99566] average_cost 0 f_measure 0.7809779092958028 [0.705882,0.857143,1,1,0.9375,0.416667,0.9375,0.967742,0.631579,0.896552,0.8,0.969697,0.967742,0.625,1,0.969697,1,0.222222,0.37037,0.121212,0.533333,0.9375,0.684211,1,0.8125,0.322581,0.341463,0.8125,0.903226,0.896552,0.967742,0.967742,0.866667,0.470588,0.896552,0.666667,0.4,0.342857,0.875,0.967742,0.647059,0.9375,0.933333,0.8125,0.9375,0.848485,0.866667,0.8125,0.756757,0.83871,0.777778,0.758621,0.866667,0.8,0.787879,0.294118,1,0.903226,0.722222,0.742857,0.733333,0.875,0.705882,0.848485,0.903226,0.625,0.3125,1,0.75,0.75,0.642857,0.8,0.611111,1,0.8125,1,0.903226,0.444444,1,0.9375,0.774194,0.933333,0.909091,0.666667,0.875,0.875,0.967742,0.83871,0.909091,1,0.785714,0.785714,0.848485,0.875,0.9375,0.823529,0.857143,0.705882,0.235294,0.733333] kappa 0.7765151515151516 kb_relative_information_score 968.8695821507412 mean_absolute_error 0.016294466058817653 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 0.7903656339274757 [0.666667,0.789474,1,1,0.9375,0.625,0.9375,1,0.545455,1,0.857143,0.941176,1,0.625,1,0.941176,1,0.272727,0.454545,0.117647,0.571429,0.9375,0.590909,1,0.8125,0.333333,0.28,0.8125,0.933333,1,1,1,0.928571,0.444444,1,0.714286,0.368421,0.315789,0.875,1,0.611111,0.9375,1,0.8125,0.9375,0.823529,0.928571,0.8125,0.666667,0.866667,0.7,0.846154,0.928571,0.857143,0.764706,0.277778,1,0.933333,0.65,0.684211,0.785714,0.875,0.666667,0.823529,0.933333,0.625,0.3125,1,0.75,0.75,0.75,0.857143,0.55,1,0.8125,1,0.933333,0.4,1,0.9375,0.8,1,0.882353,0.714286,0.875,0.875,1,0.866667,0.882353,1,0.916667,0.916667,0.823529,0.875,0.9375,0.777778,0.789474,0.666667,0.222222,0.785714] predictive_accuracy 0.77875 prior_entropy 6.6438561897747395 recall 0.77875 [0.75,0.9375,1,1,0.9375,0.3125,0.9375,0.9375,0.75,0.8125,0.75,1,0.9375,0.625,1,1,1,0.1875,0.3125,0.125,0.5,0.9375,0.8125,1,0.8125,0.3125,0.4375,0.8125,0.875,0.8125,0.9375,0.9375,0.8125,0.5,0.8125,0.625,0.4375,0.375,0.875,0.9375,0.6875,0.9375,0.875,0.8125,0.9375,0.875,0.8125,0.8125,0.875,0.8125,0.875,0.6875,0.8125,0.75,0.8125,0.3125,1,0.875,0.8125,0.8125,0.6875,0.875,0.75,0.875,0.875,0.625,0.3125,1,0.75,0.75,0.5625,0.75,0.6875,1,0.8125,1,0.875,0.5,1,0.9375,0.75,0.875,0.9375,0.625,0.875,0.875,0.9375,0.8125,0.9375,1,0.6875,0.6875,0.875,0.875,0.9375,0.875,0.9375,0.75,0.25,0.6875] relative_absolute_error 0.8229528312534155 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08372503767012587 root_relative_squared_error 0.8414682894247847 total_cost 0 area_under_roc_curve 0.9939554971737922 [0.981132,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,0.952532,1,1,1,0.996835,0.968354,0.981132,0.981132,1,1,1,0.993711,0.990506,0.984177,1,1,1,1,1,0.996835,0.977848,0.996835,0.987421,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.96519,1,1,1,1,1,0.981132,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.918239,0.984177,1,1,1,1,1,1,0.974684,1,1,1,1,1,1,0.996835,1,1,1,0.996835,1,0.996835,1,1,0.990506,1,0.987342,0.993711,0.990506,0.879747,1] area_under_roc_curve 0.9949434260807257 [1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.920886,1,1,1,0.974684,0.993671,0.981132,1,1,1,1,1,0.977848,0.977848,1,1,1,1,1,0.996835,1,1,0.993711,0.993711,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,0.968553,1,1,1,0.977848,1,1,0.996835,1,1,0.993671,0.993671,1,0.937107,1,0.984177,1,1,1,1,1,1,0.990506,1,0.993671,0.977848,1,1,1,0.993671,1,1,1,1,1,0.977848,1,1,0.990506,1,1,1,0.993671,0.984177,0.987421] area_under_roc_curve 0.9944346588647399 [1,1,1,1,1,0.993671,0.993711,1,0.990506,1,1,1,1,0.996835,1,1,1,0.955696,0.987421,0.949367,0.974843,1,0.974684,1,1,0.974684,0.977848,0.993711,1,1,1,1,1,0.987342,1,1,0.987421,0.899371,1,1,0.993671,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.974843,0.971519,1,1,1,0.968553,0.996835,0.996835,0.996835,1,0.993711,0.996835,0.987342,0.996835,1,1,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.924528,1] area_under_roc_curve 0.9943169831223628 [0.996835,1,1,1,1,0.990506,1,1,0.990506,1,1,1,1,1,1,1,1,1,0.91195,0.971519,1,1,0.996835,1,0.974843,0.977848,0.971519,1,1,1,1,1,1,0.993671,1,1,0.993711,0.930818,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.971519,1,0.962025,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.968354,1,1,0.993671,0.981013,0.996835,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,0.987342,0.993671,0.893082,0.977848] area_under_roc_curve 0.995457169015206 [1,1,1,1,1,1,1,1,0.987421,1,0.993671,1,1,0.943396,1,1,1,0.993671,0.987421,0.987342,1,1,1,1,0.990506,0.993671,0.981013,1,1,1,1,1,1,0.981013,1,1,0.993671,0.968354,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.974843,1,0.993711,0.987342,0.987342,1,0.996835,1,1,1,1,1,1,1,1,0.987421,1,0.993671,0.990506,1,1,1,1,0.914557,1,0.993671,0.987342,1,1,1,1,1,0.990506,0.987421,1,1,1,1,1,1,0.996835,1,1,0.993671,0.993711,1,1,0.993711,1] area_under_roc_curve 0.995494984475758 [0.96519,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.981132,0.946203,1,1,0.990506,1,0.993671,0.981013,0.981013,1,1,1,1,1,1,0.993671,1,0.996835,0.987342,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.974684,1,1,0.996835,0.993711,0.993671,1,0.987421,1,1,0.990506,1,1,1,1,0.996835,0.990506,1,1,0.990506,1,1,0.990506,1,1,0.987421,0.993671,1,0.987342,1,1,1,1,1,1,0.974843,1,1,1,1,1,1,1,0.962264,0.987342] area_under_roc_curve 0.9953457129209455 [1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,0.993711,0.974684,0.96519,0.993671,1,1,1,1,0.861635,0.893082,0.996835,1,1,0.996835,1,1,0.993711,1,1,0.96519,0.984177,1,1,1,1,1,1,0.987421,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,0.984177,1,1,0.996835,1,1,0.996835,1,0.996835,1,0.968553,1,1,0.996835,1,1,1,0.996835,1,1,1,1,0.981132,1,1,0.981013,1,1,1,1,1,1,0.993711,1,1,1,1,0.990506,1,1,1,1,1,0.993671,1] area_under_roc_curve 0.9965670030252369 [1,1,1,1,1,1,1,1,0.987421,1,0.987421,1,1,1,1,1,1,0.949686,0.971519,0.993671,0.993671,1,0.993671,1,0.993671,0.981132,0.987421,1,0.993711,1,1,1,1,1,1,1,0.981013,0.993671,1,1,0.996835,1,1,1,1,1,1,0.990506,1,0.993711,0.968553,0.996835,1,1,1,0.962025,1,1,0.990506,1,1,1,1,1,1,0.974843,0.962264,1,0.996835,1,1,1,0.996835,1,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1] area_under_roc_curve 0.9958534849932328 [1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993711,0.996835,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.984177,1,1,0.993711,1,1,0.990506,1,0.993671,1,0.993671,0.993711,0.993711,0.993671,0.996835,1,0.936709,0.996835,0.918239,1,1,0.993671,1,0.996835,1,1,1,1,0.993711,0.981013,1,0.993671,1,1,0.987421,0.955696,1,1,1,1,0.993711,1,0.996835,1,1,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,0.993711,1,0.984177,1] area_under_roc_curve 0.9967244845155637 [1,1,1,1,1,0.993711,1,1,0.993671,1,1,1,1,0.990506,1,1,1,0.943396,0.977848,0.974843,0.981013,1,1,1,1,0.981132,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.987421,1,1,0.987342,0.990506,1,1,0.984177,1,0.993711,0.993711,0.977848,1,1,0.993711,1,1,0.984177,1,0.993671,0.996835,1,1,1,1,1,0.974843,1,0.987421,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,0.987421,1,1,1] 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 0.772592680326898 kappa 0.7914938988271532 kappa 0.8041538340045803 kappa 0.7662850375049349 kappa 0.7598925835242082 kappa 0.7283745903904615 kappa 0.7852094602598018 kappa 0.7599210266535044 kappa 0.7978521794061908 kappa 0.797876120168963 kb_relative_information_score 97.00995450111265 kb_relative_information_score 95.95643910005656 kb_relative_information_score 96.55196942745981 kb_relative_information_score 96.24450918683971 kb_relative_information_score 96.41800065766522 kb_relative_information_score 95.00941681627236 kb_relative_information_score 99.17628659926217 kb_relative_information_score 97.47743887847894 kb_relative_information_score 97.45186595804782 kb_relative_information_score 97.57370102554525 mean_absolute_error 0.01622953481837832 mean_absolute_error 0.016383614862576147 mean_absolute_error 0.016330818020234575 mean_absolute_error 0.016331575389392287 mean_absolute_error 0.016394130127676942 mean_absolute_error 0.016521320287470893 mean_absolute_error 0.016004703914971784 mean_absolute_error 0.016264450269625787 mean_absolute_error 0.016244283232621472 mean_absolute_error 0.01624022966522825 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 0.775 predictive_accuracy 0.79375 predictive_accuracy 0.80625 predictive_accuracy 0.76875 predictive_accuracy 0.7625 predictive_accuracy 0.73125 predictive_accuracy 0.7875 predictive_accuracy 0.7625 predictive_accuracy 0.8 predictive_accuracy 0.8 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 0.775 [1,0.5,1,1,1,0,1,1,0,1,0.5,1,1,0,1,1,1,0,0.5,0,0,1,1,1,0,0.5,0.5,1,1,0,1,1,0.5,0,0,0,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0.5,1,0.5,1,1,0,1] recall 0.79375 [1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,0.5,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,0,0,0.5,1,0.5,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0,0] recall 0.80625 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,1] recall 0.76875 [1,1,1,1,0.5,0,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,0.5,0,0,0,1,1,1,0,0.5,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0,0.5,0,0,1,1,0.5,0,0.5,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,0] recall 0.7625 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0,0,0.5,1,1,0.5,1,1,0.5,0,1,1,0.5,1,1,0.5,0,1,1,0.5,0.5,1,1,1,1,1,0,1,0,1,1,1,1,0,0,0.5,1,0.5,0,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1] recall 0.73125 [0.5,1,1,1,1,0,1,0.5,1,0.5,0.5,1,1,1,1,1,1,0,0,0.5,0.5,0.5,1,1,1,0,1,0,1,0.5,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0,1,0,1,1,0.5,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0] recall 0.7875 [1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0.5,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,1] recall 0.7625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0,0,0.5,1,0.5,1,0.5,0,1,1,0,1,0.5,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,0,1,1,0,0.5,1,1,1,1,1,0,0.5,1] recall 0.8 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,0,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,0,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1] recall 0.8 [1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,0.5,1,1,1,1,0,0,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,0,1,0.5,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] relative_absolute_error 0.8196734756756713 relative_absolute_error 0.827455296089703 relative_absolute_error 0.8247887889007347 relative_absolute_error 0.8248270398682961 relative_absolute_error 0.8279863700846927 relative_absolute_error 0.8344101155288315 relative_absolute_error 0.8083183795440282 relative_absolute_error 0.8214368823043313 relative_absolute_error 0.8204183450818913 relative_absolute_error 0.8202136194559708 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 0.08352163699474857 root_mean_squared_error 0.08397869074649027 root_mean_squared_error 0.08389582354608668 root_mean_squared_error 0.08390108252781628 root_mean_squared_error 0.0841408701356158 root_mean_squared_error 0.08495543740280602 root_mean_squared_error 0.08239437227687238 root_mean_squared_error 0.08349946909053187 root_mean_squared_error 0.08345844768795199 root_mean_squared_error 0.08348182641619283 root_relative_squared_error 0.8394240357207504 root_relative_squared_error 0.8440175987618153 root_relative_squared_error 0.8431847520613127 root_relative_squared_error 0.8432376068163927 root_relative_squared_error 0.8456475629510833 root_relative_squared_error 0.8538342720170686 root_relative_squared_error 0.8280945990280196 root_relative_squared_error 0.8392012399005221 root_relative_squared_error 0.8387889592922445 root_relative_squared_error 0.8390239243517915 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 1626.7307749999995 usercpu_time_millis 1639.908868 usercpu_time_millis 1611.3324339999995 usercpu_time_millis 1614.3888859999995 usercpu_time_millis 1612.8514119999995 usercpu_time_millis 1627.3316020000016 usercpu_time_millis 1619.9294959999993 usercpu_time_millis 1637.5569799999994 usercpu_time_millis 1642.6952340000032 usercpu_time_millis 1647.1016330000018 usercpu_time_millis_testing 166.4726489999997 usercpu_time_millis_testing 164.8102109999998 usercpu_time_millis_testing 163.83091299999995 usercpu_time_millis_testing 161.9228029999995 usercpu_time_millis_testing 163.33141500000005 usercpu_time_millis_testing 167.65259100000128 usercpu_time_millis_testing 160.76099899999895 usercpu_time_millis_testing 170.82822399999918 usercpu_time_millis_testing 163.33295300000117 usercpu_time_millis_testing 167.85321300000078 usercpu_time_millis_training 1460.258126 usercpu_time_millis_training 1475.0986570000002 usercpu_time_millis_training 1447.5015209999995 usercpu_time_millis_training 1452.466083 usercpu_time_millis_training 1449.5199969999994 usercpu_time_millis_training 1459.6790110000004 usercpu_time_millis_training 1459.1684970000003 usercpu_time_millis_training 1466.7287560000002 usercpu_time_millis_training 1479.362281000002 usercpu_time_millis_training 1479.248420000001