6040817 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) 4075498 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 true 6948 threshold 0.0 6949 C 549.4264198404019 6953 cache_size 200 6953 class_weight null 6953 coef0 -0.10540573726025548 6953 decision_function_shape null 6953 degree 3 6953 gamma 0.05765092375524838 6953 kernel "sigmoid" 6953 max_iter -1 6953 probability true 6953 random_state 58613 6953 shrinking false 6953 tol 7.89373514574744e-05 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 13004671 description https://api.openml.org/data/download/13004671/description.xml -1 13004672 predictions https://api.openml.org/data/download/13004672/predictions.arff area_under_roc_curve 0.994649226641414 [0.992898,0.999684,1,1,0.999566,0.994673,0.99925,1,0.993766,0.999921,0.998382,0.999605,0.999329,0.976444,0.999763,1,1,0.981889,0.976405,0.97021,0.992385,0.999803,0.994081,1,0.995581,0.971473,0.972854,0.998737,0.998619,1,0.999803,1,0.999329,0.989938,0.999211,0.998816,0.985085,0.97017,0.999803,1,0.998106,0.998501,1,0.998816,0.999171,0.998935,0.999527,0.996686,0.998658,0.996133,0.994673,0.996567,0.998146,0.983704,0.997751,0.971275,1,0.999684,0.991911,0.984414,0.999014,0.998935,0.994713,0.997909,0.999171,0.992582,0.985953,0.999882,0.993056,0.993253,0.989978,0.997751,0.991319,1,0.982481,0.999645,0.999842,0.988676,1,0.999487,0.994515,0.997672,0.999369,0.994871,0.99779,0.999763,1,0.998422,0.999408,0.999329,0.992543,0.999408,0.998343,0.996528,0.999487,0.997514,0.994555,0.997475,0.958767,0.995384] average_cost 0 f_measure 0.7751972484169525 [0.705882,0.857143,1,1,0.9375,0.5,0.9375,1,0.588235,0.896552,0.866667,0.941176,0.933333,0.625,1,0.969697,1,0.258065,0.222222,0.176471,0.5,0.9375,0.682927,1,0.8125,0.382979,0.277778,0.8125,0.903226,0.896552,0.967742,0.967742,0.875,0.470588,0.933333,0.6875,0.375,0.357143,0.848485,0.967742,0.647059,0.9375,0.933333,0.8125,0.9375,0.882353,0.866667,0.83871,0.789474,0.758621,0.823529,0.758621,0.866667,0.8,0.787879,0.166667,1,0.903226,0.651163,0.666667,0.774194,0.875,0.742857,0.875,0.903226,0.6,0.285714,1,0.75,0.764706,0.62069,0.827586,0.545455,0.969697,0.83871,0.967742,0.903226,0.5,1,0.9375,0.709677,0.933333,0.909091,0.347826,0.882353,0.875,0.967742,0.857143,0.909091,1,0.740741,0.827586,0.848485,0.896552,0.9375,0.8,0.833333,0.666667,0.263158,0.733333] kappa 0.7720959595959597 kb_relative_information_score 956.3261835226582 mean_absolute_error 0.016426197989538097 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 0.7861669699713566 [0.666667,0.789474,1,1,0.9375,0.583333,0.9375,1,0.555556,1,0.928571,0.888889,1,0.625,1,0.941176,1,0.266667,0.272727,0.166667,0.5,0.9375,0.56,1,0.8125,0.290323,0.25,0.8125,0.933333,1,1,1,0.875,0.444444,1,0.6875,0.375,0.416667,0.823529,1,0.611111,0.9375,1,0.8125,0.9375,0.833333,0.928571,0.866667,0.681818,0.846154,0.777778,0.846154,0.928571,0.857143,0.764706,0.25,1,0.933333,0.518519,0.6,0.8,0.875,0.684211,0.875,0.933333,0.642857,0.333333,1,0.75,0.722222,0.692308,0.923077,0.529412,0.941176,0.866667,1,0.933333,0.45,1,0.9375,0.733333,1,0.882353,0.571429,0.833333,0.875,1,1,0.882353,1,0.909091,0.923077,0.823529,1,0.9375,0.736842,0.75,0.647059,0.227273,0.785714] predictive_accuracy 0.774375 prior_entropy 6.6438561897747395 recall 0.774375 [0.75,0.9375,1,1,0.9375,0.4375,0.9375,1,0.625,0.8125,0.8125,1,0.875,0.625,1,1,1,0.25,0.1875,0.1875,0.5,0.9375,0.875,1,0.8125,0.5625,0.3125,0.8125,0.875,0.8125,0.9375,0.9375,0.875,0.5,0.875,0.6875,0.375,0.3125,0.875,0.9375,0.6875,0.9375,0.875,0.8125,0.9375,0.9375,0.8125,0.8125,0.9375,0.6875,0.875,0.6875,0.8125,0.75,0.8125,0.125,1,0.875,0.875,0.75,0.75,0.875,0.8125,0.875,0.875,0.5625,0.25,1,0.75,0.8125,0.5625,0.75,0.5625,1,0.8125,0.9375,0.875,0.5625,1,0.9375,0.6875,0.875,0.9375,0.25,0.9375,0.875,0.9375,0.75,0.9375,1,0.625,0.75,0.875,0.8125,0.9375,0.875,0.9375,0.6875,0.3125,0.6875] relative_absolute_error 0.8296059590675792 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.0843716472326461 root_relative_squared_error 0.847966960045195 total_cost 0 area_under_roc_curve 0.9936390414775896 [0.974843,1,1,1,1,1,1,1,0.993671,1,1,0.993671,1,0.952532,1,1,1,0.996835,0.96519,0.987421,0.981132,1,1,1,0.993711,0.990506,0.977848,1,1,1,1,1,0.996835,0.981013,0.990506,0.987421,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.974684,0.996835,1,1,0.990506,1,0.981132,1,1,1,0.996835,1,1,1,0.996835,1,1,1,1,1,0.918239,0.977848,1,1,1,1,1,1,0.974684,1,1,1,1,1,1,0.993671,1,1,1,0.996835,1,0.996835,1,1,0.990506,1,0.984177,0.993711,0.990506,0.889241,1] area_under_roc_curve 0.9938360799299417 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.924051,1,1,1,0.968354,0.993671,0.981132,1,1,1,1,1,0.968354,0.96519,1,1,1,1,1,0.996835,1,0.996835,0.993711,0.993711,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.962264,1,1,1,0.952532,1,1,0.996835,1,1,0.993671,0.996835,1,0.937107,1,0.987342,1,1,1,1,1,1,0.984177,1,0.993671,0.977848,1,1,1,0.990506,1,1,1,1,1,0.974684,1,1,0.984177,1,1,1,0.990506,0.968354,0.987421] area_under_roc_curve 0.9942368740546133 [0.996835,1,1,1,1,0.993671,0.993711,1,0.990506,1,1,1,1,0.996835,1,1,1,0.962025,0.981132,0.943038,0.974843,1,0.974684,1,1,0.971519,0.974684,0.993711,1,1,1,1,1,0.987342,1,1,0.987421,0.893082,1,1,0.993671,0.993711,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.987342,1,1,0.981132,0.968354,1,1,1,0.968553,0.996835,0.996835,1,1,0.993711,0.996835,0.984177,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,1,0.930818,1] area_under_roc_curve 0.9936833253721838 [0.996835,1,1,1,1,0.990506,1,1,0.987342,1,0.996835,1,1,1,1,1,1,1,0.91195,0.971519,1,1,0.996835,1,0.974843,0.977848,0.952532,1,1,1,1,1,1,0.993671,1,1,0.993711,0.949686,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.96519,1,0.958861,1,1,0.993711,0.987342,1,1,1,1,0.996835,0.996835,0.971519,1,1,0.990506,0.981013,0.996835,0.974843,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.993711,0.984177,0.990506,0.893082,0.974684] area_under_roc_curve 0.9951797727091792 [1,1,1,1,1,1,1,1,0.987421,1,0.993671,1,1,0.937107,1,1,1,0.996835,1,0.984177,1,1,0.996835,1,0.990506,0.987342,0.977848,1,1,1,1,1,1,0.981013,1,1,0.977848,0.962025,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.974843,1,0.993711,0.987342,0.996835,1,0.996835,0.996835,1,1,1,1,1,1,1,0.987421,1,0.996835,0.990506,1,1,1,1,0.911392,1,0.993671,0.987342,1,1,1,1,1,0.990506,0.987421,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.993711,1] area_under_roc_curve 0.9949404406496296 [0.962025,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,0.990506,0.993711,0.946203,1,1,0.990506,1,0.993671,0.971519,0.981013,1,1,1,1,1,1,0.993671,1,0.996835,0.984177,0.977848,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.962025,1,1,0.993671,1,0.993671,1,0.987421,1,1,0.990506,1,1,1,1,0.996835,0.990506,1,1,0.987342,1,1,0.984177,1,1,0.993711,0.993671,1,0.987342,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,0.962264,0.987342] area_under_roc_curve 0.9947914178807418 [0.996835,1,1,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,1,1,0.993711,0.990506,0.962025,0.993671,1,1,1,1,0.861635,0.899371,0.993671,1,1,0.996835,1,1,0.993711,1,1,0.958861,0.971519,1,1,1,1,1,1,0.987421,1,1,0.990506,1,1,0.993711,1,1,1,0.996835,0.993671,1,1,0.987342,1,1,0.996835,1,0.996835,1,0.968553,1,0.996835,0.993671,1,1,1,0.996835,1,1,1,1,0.981132,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.990506,1,0.984177,1] area_under_roc_curve 0.9961326228007327 [1,1,1,1,1,1,1,1,0.987421,1,0.987421,1,1,1,1,1,1,0.949686,0.968354,1,0.993671,1,0.993671,1,0.993671,0.981132,0.987421,1,0.993711,1,1,1,1,1,1,1,0.984177,0.96519,1,1,1,1,1,1,1,1,1,0.990506,1,0.993711,0.962264,0.996835,1,1,1,0.962025,1,1,0.987342,1,1,1,1,1,1,0.974843,0.962264,1,0.996835,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.996835,1,0.996835,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.993671,0.996835,1,0.993671,1] area_under_roc_curve 0.995616143221081 [1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,1,1,1,0.993711,0.990506,0.987421,0.996835,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.974684,1,1,0.993711,1,1,0.990506,1,0.987342,1,0.993671,0.993711,0.993711,0.990506,0.996835,1,0.936709,0.996835,0.918239,1,1,0.993671,1,0.996835,1,1,0.996835,1,0.993711,0.993671,1,0.990506,1,1,0.987421,0.955696,1,1,1,1,0.993711,1,0.996835,1,1,1,0.981132,1,1,1,1,1,1,0.996835,1,1,0.996835,1,0.993671,1,1,0.987342,1] area_under_roc_curve 0.9965264509195129 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,0.937107,0.974684,0.981132,0.977848,1,1,1,1,0.987421,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.996835,1,1,0.987342,1,0.987421,1,1,0.990506,0.990506,1,1,0.984177,0.996835,0.993711,0.993711,0.987342,1,1,0.993711,1,1,0.984177,1,0.996835,0.996835,1,1,1,1,1,0.974843,1,0.981132,1,1,1,0.996835,1,0.996835,0.993671,1,1,0.996835,1,0.996835,0.987421,0.996835,1,1] 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 0.760006315623273 kappa 0.785226420308737 kappa 0.78520097923083 kappa 0.760006315623273 kappa 0.7599210266535044 kappa 0.7473451502112036 kappa 0.7789095503178175 kappa 0.772592680326898 kappa 0.7726285872182529 kappa 0.7979000552617037 kb_relative_information_score 95.77090411000704 kb_relative_information_score 94.22540851317959 kb_relative_information_score 95.56639612495896 kb_relative_information_score 95.10103585537937 kb_relative_information_score 95.25284528432542 kb_relative_information_score 93.93383155857202 kb_relative_information_score 98.08021907668018 kb_relative_information_score 96.26468502379721 kb_relative_information_score 95.75334565483342 kb_relative_information_score 96.37751232092563 mean_absolute_error 0.01635164940875026 mean_absolute_error 0.01654779890799628 mean_absolute_error 0.016447361139123776 mean_absolute_error 0.01646641535507917 mean_absolute_error 0.016506038738180316 mean_absolute_error 0.016619910124378506 mean_absolute_error 0.016139413083270285 mean_absolute_error 0.016383379242531304 mean_absolute_error 0.016441257412041933 mean_absolute_error 0.01635875648402927 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 0.7625 predictive_accuracy 0.7875 predictive_accuracy 0.7875 predictive_accuracy 0.7625 predictive_accuracy 0.7625 predictive_accuracy 0.75 predictive_accuracy 0.78125 predictive_accuracy 0.775 predictive_accuracy 0.775 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 0.7625 [1,0.5,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,0,0.5,0,1,1,0,1,1,0.5,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0,1,0.5,1,0.5,0.5,1,0.5,1,1,0.5,1,0.5,1,1,0,1] recall 0.7875 [1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,0] recall 0.7875 [0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,1] recall 0.7625 [1,1,1,1,0.5,0.5,1,1,0,0.5,1,1,1,0.5,1,1,1,0.5,0,0.5,0,1,1,1,0,0.5,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,0,0.5,0,0.5,1,1,0.5,0,0.5,0,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0,0] recall 0.7625 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0.5,0,0,1,1,1,1,1,1,0,1,1,0.5,1,1,0.5,0,1,1,0.5,0,1,1,1,1,1,0,1,0,1,1,1,1,0,0,0.5,1,0.5,0,1,0.5,1,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1] recall 0.75 [0.5,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,0.5,1,1,1,1,0.5,0,1,0.5,1,1,1,0.5,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,1,1,0.5,1,0,1,1,1,1,0,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0] recall 0.78125 [1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,0,1,0,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,0,1,1,1,0,1,1,1,1,0,1,1,1,1,1,0,1] recall 0.775 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0,0,0,0.5,1,0.5,1,0.5,1,1,1,0,1,0.5,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,0.5,0,1,0.5,1,0,1,1,0,1,1,1,1,1,1,0,0.5,1] recall 0.775 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,1,0,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,0,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,0.5,1,0.5,1,1,0.5,1] recall 0.8 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,0.5,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,0,1,0.5,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] relative_absolute_error 0.8258408792298098 relative_absolute_error 0.8357474195957704 relative_absolute_error 0.8306748050062499 relative_absolute_error 0.8316371391454112 relative_absolute_error 0.8336383201101155 relative_absolute_error 0.8393894002211353 relative_absolute_error 0.8151218728924373 relative_absolute_error 0.8274433960874383 relative_absolute_error 0.8303665359617124 relative_absolute_error 0.8261998224257193 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 0.08413554176617105 root_mean_squared_error 0.08479558325934364 root_mean_squared_error 0.08445632739891845 root_mean_squared_error 0.08453360048042775 root_mean_squared_error 0.08469674572763562 root_mean_squared_error 0.08539766324558604 root_mean_squared_error 0.08303605191163763 root_mean_squared_error 0.08409890884829962 root_mean_squared_error 0.08443405762802794 root_mean_squared_error 0.08411209955936855 root_relative_squared_error 0.8455940108232268 root_relative_squared_error 0.8522276774260129 root_relative_squared_error 0.8488180277382569 root_relative_squared_error 0.8495946514284414 root_relative_squared_error 0.8512343228566785 root_relative_squared_error 0.8582788089658565 root_relative_squared_error 0.8345437220102275 root_relative_squared_error 0.8452258361457862 root_relative_squared_error 0.8485942081194355 root_relative_squared_error 0.8453584077801304 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 1839.7364049997122 usercpu_time_millis 1682.8481040001861 usercpu_time_millis 1684.5686920000844 usercpu_time_millis 1691.8423790002635 usercpu_time_millis 1690.24947600019 usercpu_time_millis 1693.0582149998372 usercpu_time_millis 1704.6339290000105 usercpu_time_millis 1704.2804569996406 usercpu_time_millis 1705.3693589996328 usercpu_time_millis 1699.5222610003111 usercpu_time_millis_testing 157.8812719999405 usercpu_time_millis_testing 159.73887000018294 usercpu_time_millis_testing 155.93503999980385 usercpu_time_millis_testing 156.875594000212 usercpu_time_millis_testing 157.15107799996986 usercpu_time_millis_testing 156.4255260000209 usercpu_time_millis_testing 159.41321200034508 usercpu_time_millis_testing 160.54710499975045 usercpu_time_millis_testing 159.0249229998335 usercpu_time_millis_testing 159.06423900014488 usercpu_time_millis_training 1681.8551329997717 usercpu_time_millis_training 1523.1092340000032 usercpu_time_millis_training 1528.6336520002806 usercpu_time_millis_training 1534.9667850000515 usercpu_time_millis_training 1533.0983980002202 usercpu_time_millis_training 1536.6326889998163 usercpu_time_millis_training 1545.2207169996655 usercpu_time_millis_training 1543.7333519998901 usercpu_time_millis_training 1546.3444359997993 usercpu_time_millis_training 1540.4580220001662