8827251 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) 6802301 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "median" 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 0.1280615471130018 7708 cache_size 200 7708 class_weight null 7708 coef0 0.4736950641496114 7708 decision_function_shape null 7708 degree 5 7708 gamma 0.014834820393693651 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 63582 7708 shrinking false 7708 tol 0.04818591995900326 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 18570312 description https://api.openml.org/data/download/18570312/description.xml -1 18570313 predictions https://api.openml.org/data/download/18570313/predictions.arff area_under_roc_curve 0.9959663036616155 [0.985243,0.999487,0.999961,0.999487,0.998382,0.9927,0.999724,0.99854,0.996528,0.999921,0.998658,0.999566,0.999092,0.95565,0.998461,0.999132,1,0.991201,0.985559,0.971551,0.997751,0.999448,0.994831,0.999132,0.998895,0.979561,0.988557,0.999211,0.998382,1,0.999527,0.999921,1,0.991517,0.997475,0.999171,0.994081,0.988123,0.999132,0.999645,0.998658,0.997001,1,0.998895,0.996133,1,0.998974,0.998698,0.997435,0.996843,0.99566,0.998698,0.998027,0.993095,0.995581,0.991122,1,0.999132,0.993963,0.996133,0.997475,0.999487,0.99779,0.998935,0.999092,0.992148,0.987176,0.999724,0.997633,0.993016,0.998106,0.998737,0.994476,0.997633,0.988873,0.999408,0.999448,0.991043,0.999566,0.999961,0.996922,0.996054,0.997633,0.996252,0.999211,0.999921,0.999842,0.999092,0.99929,0.999684,0.998816,0.999605,0.997554,0.996528,0.99491,0.996646,0.997001,0.998224,0.970407,0.996054] average_cost 0 f_measure 0.8053625465363342 [0.594595,0.903226,0.967742,0.857143,0.896552,0.733333,0.9375,0.756757,0.774194,0.896552,0.848485,0.896552,0.882353,0.666667,0.896552,0.903226,0.933333,0.516129,0.4375,0.285714,0.774194,0.9375,0.764706,0.857143,0.882353,0.486486,0.484848,0.875,0.903226,1,0.903226,0.967742,0.9375,0.545455,0.83871,0.882353,0.5625,0.555556,0.909091,0.896552,0.787879,0.875,0.967742,0.823529,0.857143,0.888889,0.896552,0.8125,0.722222,0.8125,0.848485,0.758621,0.8125,0.75,0.774194,0.516129,0.896552,0.969697,0.666667,0.742857,0.8,0.882353,0.689655,0.903226,0.909091,0.666667,0.545455,0.9375,0.8,0.6875,0.823529,0.827586,0.709677,0.896552,0.717949,0.9375,0.896552,0.666667,1,1,0.875,0.8125,0.83871,0.5625,0.875,0.903226,0.967742,0.83871,0.875,0.967742,0.758621,0.827586,0.866667,0.8125,0.882353,0.8,0.8,0.83871,0.533333,0.875] kappa 0.8011363636363636 kb_relative_information_score 971.5791763511145 mean_absolute_error 0.01590920175513641 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 0.815690277648347 [0.52381,0.933333,1,1,1,0.785714,0.9375,0.666667,0.8,1,0.823529,1,0.833333,0.714286,1,0.933333,1,0.533333,0.4375,0.333333,0.8,0.9375,0.722222,0.789474,0.833333,0.428571,0.470588,0.875,0.933333,1,0.933333,1,0.9375,0.529412,0.866667,0.833333,0.5625,0.5,0.882353,1,0.764706,0.875,1,0.777778,1,0.8,1,0.8125,0.65,0.8125,0.823529,0.846154,0.8125,0.75,0.8,0.533333,1,0.941176,0.565217,0.684211,0.857143,0.833333,0.769231,0.933333,0.882353,0.6,0.529412,0.9375,0.857143,0.6875,0.777778,0.923077,0.733333,1,0.608696,0.9375,1,0.565217,1,1,0.875,0.8125,0.866667,0.5625,0.875,0.933333,1,0.866667,0.875,1,0.846154,0.923077,0.928571,0.8125,0.833333,0.736842,0.736842,0.866667,0.571429,0.875] predictive_accuracy 0.803125 prior_entropy 6.6438561897747395 recall 0.803125 [0.6875,0.875,0.9375,0.75,0.8125,0.6875,0.9375,0.875,0.75,0.8125,0.875,0.8125,0.9375,0.625,0.8125,0.875,0.875,0.5,0.4375,0.25,0.75,0.9375,0.8125,0.9375,0.9375,0.5625,0.5,0.875,0.875,1,0.875,0.9375,0.9375,0.5625,0.8125,0.9375,0.5625,0.625,0.9375,0.8125,0.8125,0.875,0.9375,0.875,0.75,1,0.8125,0.8125,0.8125,0.8125,0.875,0.6875,0.8125,0.75,0.75,0.5,0.8125,1,0.8125,0.8125,0.75,0.9375,0.625,0.875,0.9375,0.75,0.5625,0.9375,0.75,0.6875,0.875,0.75,0.6875,0.8125,0.875,0.9375,0.8125,0.8125,1,1,0.875,0.8125,0.8125,0.5625,0.875,0.875,0.9375,0.8125,0.875,0.9375,0.6875,0.75,0.8125,0.8125,0.9375,0.875,0.875,0.8125,0.5,0.875] relative_absolute_error 0.8034950381382011 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08237399202209267 root_relative_squared_error 0.8278897697606369 total_cost 0 area_under_roc_curve 0.996327919751612 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987342,1,0.974843,1,0.996835,0.990506,0.974843,0.987421,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,0.990506,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.984177,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,1,0.949686,0.996835,1,1,1,1,1,1,0.958861,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,0.987421,0.993671,0.943038,1] area_under_roc_curve 0.9954549299418834 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.870253,1,1,1,0.977848,0.996835,0.974843,1,0.993711,0.993711,1,1,0.984177,0.971519,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,0.993671,1,1,1,1,0.993671,1,1,1,1,1,1,0.987421,1,1,1,0.987342,1,1,1,1,1,0.996835,1,1,0.987421,1,1,1,1,1,1,1,1,0.990506,1,1,0.977848,0.990506,1,0.987421,0.993671,1,1,1,1,1,1,1,1,0.974684,1,1,1,0.996835,1,0.981132] area_under_roc_curve 0.9966418875885678 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.993671,1,1,0.996835,1,0.936709,1,1,0.990506,1,1,0.962025,0.990506,1,1,1,1,1,1,0.981013,1,1,0.987421,1,0.993711,0.996835,0.996835,0.974843,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.990506,1,1,1,0.987421,1,0.990506,1,1,1,0.993671,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,0.996835,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,0.987421,1] area_under_roc_curve 0.9968013593662922 [0.996835,1,1,0.993711,1,0.987342,1,1,0.996835,1,1,1,1,1,0.996835,1,1,0.990506,0.937107,0.971519,1,1,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.981132,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.984177,1,1,0.977848,0.993671,0.996835,0.981132,1,0.993711,1,1,0.993671,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.990506,1,1,1] area_under_roc_curve 0.9959736485948569 [1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.899371,1,1,1,0.996835,0.987421,0.987342,1,1,1,1,1,0.993671,0.990506,1,1,1,1,1,1,0.984177,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.993671,1,0.993671,0.996835,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.943038,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.96519,0.993711,1,1,0.930818,1] area_under_roc_curve 0.9962435813231426 [0.911392,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.96519,1,1,1,1,0.993671,0.984177,0.984177,1,1,1,1,1,1,1,1,0.993671,1,0.987342,0.996835,1,0.993671,1,1,0.993711,1,1,1,1,0.993671,0.996835,1,1,0.993671,1,0.996835,0.990506,1,1,0.996835,1,0.996835,1,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,0.993671] area_under_roc_curve 0.9960913243372342 [0.996835,1,1,1,1,1,1,0.996835,1,1,1,1,0.993671,1,1,1,1,1,0.993671,0.977848,0.996835,1,0.993671,1,1,0.90566,0.962264,1,1,1,0.996835,1,1,1,1,1,0.977848,0.993671,1,1,1,1,1,1,0.974843,1,0.996835,0.996835,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,0.996835,1,1,1,1,1,1,1,1,1,0.993711,1,1,0.987342,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.898734,1] area_under_roc_curve 0.99712179563729 [0.996835,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.987421,0.993671,0.987342,0.996835,1,1,1,0.993671,0.987421,1,1,0.974843,1,1,1,1,1,1,1,0.984177,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.987421,0.962264,1,0.996835,1,1,0.990506,1,1,0.996835,1,1,1,1,1,1,0.974843,0.924528,1,1,0.996835,1,1,1,1,1,1,1,0.974843,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.993671] area_under_roc_curve 0.9971175662765701 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,1,0.968553,0.996835,1,0.968553,0.990506,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.96519,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.977848,0.981013,0.987421,1,1,1,1,0.993671,1,1,1,1,1,0.993671,1,1,1,1,0.993711,0.974684,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,0.996835,1,1,0.990506,1,1,1,1] area_under_roc_curve 0.9974723350051751 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.987421,0.962025,0.987421,0.996835,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.984177,1,1,1,0.993671,0.996835,0.993711,0.993711,0.984177,1,1,1,1,1,0.987342,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,1,1] 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 0.7726285872182529 kappa 0.7915185974887466 kappa 0.8168252339031227 kappa 0.797860160290576 kappa 0.8167384177890121 kappa 0.8041615667074664 kappa 0.8104639684106614 kappa 0.8230787457546797 kappa 0.810493900272415 kappa 0.7663588286368301 kb_relative_information_score 98.47379568187208 kb_relative_information_score 95.60935863546318 kb_relative_information_score 96.04982292066622 kb_relative_information_score 95.32966650428399 kb_relative_information_score 96.93525354534955 kb_relative_information_score 96.74219470991902 kb_relative_information_score 101.01573332650815 kb_relative_information_score 97.13863112604338 kb_relative_information_score 97.1970360181967 kb_relative_information_score 97.08768388281058 mean_absolute_error 0.015688213969528565 mean_absolute_error 0.016140386309378772 mean_absolute_error 0.016111366384373015 mean_absolute_error 0.016220627332464738 mean_absolute_error 0.016067136010997598 mean_absolute_error 0.015913472427614647 mean_absolute_error 0.015349029387099436 mean_absolute_error 0.015973911455311882 mean_absolute_error 0.015864229902508103 mean_absolute_error 0.01576364437208755 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 0.81875 predictive_accuracy 0.8 predictive_accuracy 0.81875 predictive_accuracy 0.80625 predictive_accuracy 0.8125 predictive_accuracy 0.825 predictive_accuracy 0.8125 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 0.775 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,1,0,1,0,0.5,0.5,0,0,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.79375 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0] recall 0.81875 [0.5,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,1] recall 0.8 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0,1,1,0.5,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1] recall 0.81875 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0,1,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.80625 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1] recall 0.8125 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.825 [0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,0,1,1,1,1,1,1,1,1,0.5] recall 0.8125 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,0,1,0.5,1] recall 0.76875 [1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,0,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0.5,1,0,1,1,1,0,1,0,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.7923340388650777 relative_absolute_error 0.8151710257261991 relative_absolute_error 0.8137053729481306 relative_absolute_error 0.8192236026497329 relative_absolute_error 0.811471515706948 relative_absolute_error 0.803710728667405 relative_absolute_error 0.77520350439896 relative_absolute_error 0.8067632048137301 relative_absolute_error 0.801223732449903 relative_absolute_error 0.7961436551559354 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 0.08150935094977722 root_mean_squared_error 0.08315224590921115 root_mean_squared_error 0.08306730870965097 root_mean_squared_error 0.08365622793491201 root_mean_squared_error 0.08279227745431349 root_mean_squared_error 0.0824865183849516 root_mean_squared_error 0.08013992752199975 root_mean_squared_error 0.08232787831243635 root_mean_squared_error 0.08229903929595948 root_mean_squared_error 0.08225646938056626 root_relative_squared_error 0.8191998000176044 root_relative_squared_error 0.8357115156249029 root_relative_squared_error 0.8348578646501008 root_relative_squared_error 0.8407767255653059 root_relative_squared_error 0.8320936965301771 root_relative_squared_error 0.8290207022595054 root_relative_squared_error 0.8054365767174219 root_relative_squared_error 0.8274263095405723 root_relative_squared_error 0.8271364665194312 root_relative_squared_error 0.8267086227718053 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 1852.33530800906 usercpu_time_millis 1995.9158680139808 usercpu_time_millis 1881.6695519926725 usercpu_time_millis 1670.0644489901606 usercpu_time_millis 1787.7572769939434 usercpu_time_millis 1735.4552470060298 usercpu_time_millis 1799.5696760044666 usercpu_time_millis 1511.8891620077193 usercpu_time_millis 1792.3971339914715 usercpu_time_millis 1713.1720379984472 usercpu_time_millis_testing 197.0299960084958 usercpu_time_millis_testing 234.8838180041639 usercpu_time_millis_testing 141.96232399262954 usercpu_time_millis_testing 193.70093799079768 usercpu_time_millis_testing 187.67742499767337 usercpu_time_millis_testing 195.85248400107957 usercpu_time_millis_testing 196.14906700735446 usercpu_time_millis_testing 195.21003200497944 usercpu_time_millis_testing 194.72622498869896 usercpu_time_millis_testing 195.55332099844236 usercpu_time_millis_training 1655.3053120005643 usercpu_time_millis_training 1761.0320500098169 usercpu_time_millis_training 1739.707228000043 usercpu_time_millis_training 1476.363510999363 usercpu_time_millis_training 1600.07985199627 usercpu_time_millis_training 1539.6027630049502 usercpu_time_millis_training 1603.4206089971121 usercpu_time_millis_training 1316.6791300027398 usercpu_time_millis_training 1597.6709090027725 usercpu_time_millis_training 1517.6187170000048