8844411 1 Jan van Rijn 9954 Supervised Classification 7707 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,scaling=sklearn.preprocessing.data.StandardScaler,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1) 6819461 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "most_frequent" 7660 strategy_nominal "most_frequent" 7660 verbose 0 7660 categorical_features [] 7661 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7661 handle_unknown "ignore" 7661 n_values "auto" 7661 sparse true 7661 threshold 0.0 7662 copy true 7663 with_mean false 7663 with_std true 7663 C 0.11411667275718168 7708 cache_size 200 7708 class_weight null 7708 coef0 -0.16358281402067099 7708 decision_function_shape null 7708 degree 3 7708 gamma 0.7151874086591404 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 50133 7708 shrinking true 7708 tol 0.0012164592182003363 7708 verbose false 7708 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18604462 description https://api.openml.org/data/download/18604462/description.xml -1 18604463 predictions https://api.openml.org/data/download/18604463/predictions.arff area_under_roc_curve 0.996266966540404 [0.98694,0.999803,1,0.999763,0.99854,0.993647,0.999763,0.999132,0.997396,0.999921,0.998777,0.999645,0.999329,0.967685,0.999053,0.999369,1,0.991319,0.984691,0.974313,0.997988,0.999921,0.995857,0.999171,0.998698,0.977233,0.991359,0.999132,0.998658,1,0.999605,0.999961,1,0.990846,0.998737,0.998737,0.993726,0.983152,0.999408,0.999763,0.998816,0.997909,1,0.998895,0.99633,1,0.999171,0.999211,0.997593,0.996962,0.994673,0.998895,0.998106,0.992582,0.996488,0.992779,1,0.999448,0.99349,0.996528,0.997988,0.999408,0.997238,0.99929,0.999171,0.993647,0.985598,0.999882,0.997988,0.993963,0.996646,0.999132,0.995541,0.998264,0.989662,0.999408,0.999369,0.99128,0.999684,1,0.996449,0.997199,0.997948,0.995581,0.999369,0.999961,0.999961,0.999132,0.999171,0.999882,0.999408,0.999605,0.998382,0.997356,0.995896,0.997435,0.99783,0.998264,0.969026,0.996765] average_cost 0 f_measure 0.8200026710717434 [0.648649,0.9375,0.967742,0.933333,0.933333,0.774194,0.969697,0.823529,0.8,0.896552,0.848485,0.903226,0.909091,0.714286,0.933333,0.903226,0.967742,0.5625,0.424242,0.2,0.774194,0.9375,0.764706,0.9375,0.882353,0.411765,0.529412,0.909091,0.903226,0.967742,0.9375,0.967742,0.9375,0.588235,0.875,0.875,0.5625,0.533333,0.909091,0.896552,0.8,0.875,0.933333,0.848485,0.903226,0.941176,0.857143,0.875,0.727273,0.8125,0.848485,0.774194,0.8125,0.666667,0.733333,0.580645,0.967742,0.9375,0.666667,0.742857,0.903226,0.833333,0.709677,0.9375,0.909091,0.722222,0.484848,0.9375,0.866667,0.75,0.787879,0.896552,0.6875,0.967742,0.789474,0.9375,0.903226,0.685714,1,1,0.8125,0.8125,0.83871,0.533333,0.875,0.933333,0.967742,0.866667,0.903226,0.967742,0.8125,0.903226,0.866667,0.83871,0.9375,0.8,0.8,0.83871,0.625,0.903226] kappa 0.8162878787878788 kb_relative_information_score 983.4862125512041 mean_absolute_error 0.01589277916949994 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 0.8270500402366062 [0.571429,0.9375,1,1,1,0.8,0.941176,0.777778,0.857143,1,0.823529,0.933333,0.882353,0.833333,1,0.933333,1,0.5625,0.411765,0.214286,0.8,0.9375,0.722222,0.9375,0.833333,0.388889,0.5,0.882353,0.933333,1,0.9375,1,0.9375,0.555556,0.875,0.875,0.5625,0.571429,0.882353,1,0.736842,0.875,1,0.823529,0.933333,0.888889,1,0.875,0.705882,0.8125,0.823529,0.8,0.8125,0.647059,0.785714,0.6,1,0.9375,0.565217,0.684211,0.933333,0.75,0.733333,0.9375,0.882353,0.65,0.470588,0.9375,0.928571,0.75,0.764706,1,0.6875,1,0.681818,0.9375,0.933333,0.631579,1,1,0.8125,0.8125,0.866667,0.571429,0.875,1,1,0.928571,0.933333,1,0.8125,0.933333,0.928571,0.866667,0.9375,0.736842,0.736842,0.866667,0.625,0.933333] predictive_accuracy 0.818125 prior_entropy 6.6438561897747395 recall 0.818125 [0.75,0.9375,0.9375,0.875,0.875,0.75,1,0.875,0.75,0.8125,0.875,0.875,0.9375,0.625,0.875,0.875,0.9375,0.5625,0.4375,0.1875,0.75,0.9375,0.8125,0.9375,0.9375,0.4375,0.5625,0.9375,0.875,0.9375,0.9375,0.9375,0.9375,0.625,0.875,0.875,0.5625,0.5,0.9375,0.8125,0.875,0.875,0.875,0.875,0.875,1,0.75,0.875,0.75,0.8125,0.875,0.75,0.8125,0.6875,0.6875,0.5625,0.9375,0.9375,0.8125,0.8125,0.875,0.9375,0.6875,0.9375,0.9375,0.8125,0.5,0.9375,0.8125,0.75,0.8125,0.8125,0.6875,0.9375,0.9375,0.9375,0.875,0.75,1,1,0.8125,0.8125,0.8125,0.5,0.875,0.875,0.9375,0.8125,0.875,0.9375,0.8125,0.875,0.8125,0.8125,0.9375,0.875,0.875,0.8125,0.625,0.875] relative_absolute_error 0.8026656146212078 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08206766451239048 root_relative_squared_error 0.8248110624495882 total_cost 0 area_under_roc_curve 0.9965630224504419 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987342,1,0.981132,1,0.996835,0.977848,0.987421,0.993711,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.968354,1,1,0.962264,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.946203,1] area_under_roc_curve 0.9962458203964653 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.889241,1,1,1,0.981013,0.993671,0.987421,1,0.993711,0.993711,1,1,0.984177,0.971519,1,1,1,1,1,1,0.996835,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.981013,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.974843] area_under_roc_curve 0.9967195087970703 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,1,1,1,0.996835,1,0.939873,1,1,0.993671,1,1,0.936709,0.993671,1,1,1,1,1,1,0.984177,1,1,1,1,0.993711,1,0.996835,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.984177,1,1,1,0.987421,1,0.990506,1,1,1,0.987342,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1] area_under_roc_curve 0.9971178150624948 [1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.984177,0.943396,0.981013,1,1,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.977848,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.993671,1,1,0.977848,0.984177,1,0.987421,1,0.993711,1,1,0.996835,1,1,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,1] area_under_roc_curve 0.9961308812992595 [1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.918239,1,1,1,0.993671,0.987421,0.987342,1,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.981013,1,1,0.996835,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.987342,1,0.996835,1,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.949367,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.968354,0.993711,1,1,0.937107,1] area_under_roc_curve 0.9962032780033436 [0.917722,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.958861,1,1,1,1,0.993671,0.990506,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,1,0.987342,0.996835,1,0.993671,1,1,0.993711,1,1,1,1,0.990506,0.993671,1,1,0.993671,1,1,0.987342,1,1,0.996835,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835] area_under_roc_curve 0.9955773326168298 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.971519,0.996835,1,0.990506,1,1,0.893082,0.968553,0.993671,1,1,0.996835,1,1,1,1,1,0.971519,0.993671,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.867089,1] area_under_roc_curve 0.9972797746994664 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.993711,0.993671,0.990506,0.996835,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.993671,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.987421,0.955975,1,1,1,1,0.990506,1,1,0.993671,1,1,1,1,1,1,0.974843,0.930818,1,1,0.996835,1,1,1,1,1,1,1,0.968553,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993671] area_under_roc_curve 0.996998397818645 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.990506,0.974843,0.996835,1,0.974843,0.993671,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.952532,1,1,1,1,1,1,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.974684,0.984177,0.993711,1,1,1,1,0.993671,1,1,1,1,1,0.987342,1,1,1,1,0.993711,0.981013,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,0.996835,1] area_under_roc_curve 0.9976300652814271 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.968354,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993671,1,0.993711,0.993711,0.984177,1,1,1,1,1,0.990506,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.996835,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1] 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 0.785234899328859 kappa 0.7914938988271532 kappa 0.87366260018161 kappa 0.7915268290756899 kappa 0.8230787457546797 kappa 0.8357419252941641 kappa 0.7978282329713722 kappa 0.8420221169036335 kappa 0.8231136731551308 kappa 0.7978920775273359 kb_relative_information_score 99.22522390561764 kb_relative_information_score 96.77585619455358 kb_relative_information_score 97.84703453211456 kb_relative_information_score 96.64707210846791 kb_relative_information_score 97.83098600226293 kb_relative_information_score 97.8635239715927 kb_relative_information_score 101.79745061029418 kb_relative_information_score 98.32031357800732 kb_relative_information_score 98.62748957176622 kb_relative_information_score 98.55126207652745 mean_absolute_error 0.01571886161801741 mean_absolute_error 0.01612989202212981 mean_absolute_error 0.016037369151090605 mean_absolute_error 0.016171902062688117 mean_absolute_error 0.0160743928173143 mean_absolute_error 0.015901812934463255 mean_absolute_error 0.015374871149944017 mean_absolute_error 0.015951834937929073 mean_absolute_error 0.015804283444605487 mean_absolute_error 0.01576257155681733 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 0.7875 predictive_accuracy 0.79375 predictive_accuracy 0.875 predictive_accuracy 0.79375 predictive_accuracy 0.825 predictive_accuracy 0.8375 predictive_accuracy 0.8 predictive_accuracy 0.84375 predictive_accuracy 0.825 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 0.7875 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,1,0,1,0,1,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,0,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.79375 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0] recall 0.875 [1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1] recall 0.79375 [0,1,1,1,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,0,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1] recall 0.825 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.8375 [0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1] recall 0.8 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,1,1,1,0.5,0,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0.5,0.5,0.5,1,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.84375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5] recall 0.825 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0.5,1,0.5,1,1,1,0,1,0.5,1,1,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1] recall 0.8 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.7938818998998677 relative_absolute_error 0.814641011218676 relative_absolute_error 0.8099681389439685 relative_absolute_error 0.8167627304387924 relative_absolute_error 0.8118380210764785 relative_absolute_error 0.8031218653769308 relative_absolute_error 0.7765086439365654 relative_absolute_error 0.8056482291883357 relative_absolute_error 0.7981961335659323 relative_absolute_error 0.7960894725665305 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 0.08138729697904723 root_mean_squared_error 0.08293672093590165 root_mean_squared_error 0.08243362984751246 root_mean_squared_error 0.08324186746065317 root_mean_squared_error 0.08262085597938496 root_mean_squared_error 0.0822434431207363 root_mean_squared_error 0.07993641640147657 root_mean_squared_error 0.08204875880159473 root_mean_squared_error 0.08184675707628651 root_mean_squared_error 0.08193412632619324 root_relative_squared_error 0.8179731114567425 root_relative_squared_error 0.833545408141815 root_relative_squared_error 0.8284891524583045 root_relative_squared_error 0.836612246107517 root_relative_squared_error 0.8303708458836703 root_relative_squared_error 0.8265777039346003 root_relative_squared_error 0.8033912129979061 root_relative_squared_error 0.8246210529068477 root_relative_squared_error 0.822590859180024 root_relative_squared_error 0.8234689531804951 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 1628.497210003843 usercpu_time_millis 1435.5021709998255 usercpu_time_millis 1419.567355995241 usercpu_time_millis 1425.5653560030623 usercpu_time_millis 1429.1938739988836 usercpu_time_millis 1433.387151992065 usercpu_time_millis 1429.0969889989356 usercpu_time_millis 1423.6436190039967 usercpu_time_millis 1430.6713640035014 usercpu_time_millis 1431.1511100022472 usercpu_time_millis_testing 134.54500799707603 usercpu_time_millis_testing 141.07720400352264 usercpu_time_millis_testing 133.50044300023 usercpu_time_millis_testing 132.8125750005711 usercpu_time_millis_testing 133.04613500076812 usercpu_time_millis_testing 131.77636199543485 usercpu_time_millis_testing 133.4091280004941 usercpu_time_millis_testing 132.60474699927727 usercpu_time_millis_testing 132.47578000300564 usercpu_time_millis_testing 132.19331899745157 usercpu_time_millis_training 1493.952202006767 usercpu_time_millis_training 1294.4249669963028 usercpu_time_millis_training 1286.066912995011 usercpu_time_millis_training 1292.7527810024912 usercpu_time_millis_training 1296.1477389981155 usercpu_time_millis_training 1301.61078999663 usercpu_time_millis_training 1295.6878609984415 usercpu_time_millis_training 1291.0388720047195 usercpu_time_millis_training 1298.1955840004957 usercpu_time_millis_training 1298.9577910047956