8699601 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) 6677020 axis 0 7660 categorical_features [] 7660 copy true 7660 fill_empty 0 7660 missing_values "NaN" 7660 strategy "mean" 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 17.084223979669474 7708 cache_size 200 7708 class_weight null 7708 coef0 -0.9637266757206799 7708 decision_function_shape null 7708 degree 4 7708 gamma 0.0002731136995389932 7708 kernel "rbf" 7708 max_iter -1 7708 probability true 7708 random_state 551 7708 shrinking true 7708 tol 0.01176303774148489 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 18316125 description https://api.openml.org/data/download/18316125/description.xml -1 18316126 predictions https://api.openml.org/data/download/18316126/predictions.arff area_under_roc_curve 0.9942475536616157 [0.986348,0.999961,1,1,0.998106,0.993766,0.999921,0.999921,0.995226,1,0.999053,0.999684,0.997751,0.978812,0.999842,0.999842,1,0.974945,0.979324,0.960464,0.997041,0.999329,0.98982,0.999842,0.997554,0.968119,0.976089,0.999132,0.998856,0.999842,0.999803,0.999961,1,0.986466,0.998737,0.998264,0.982126,0.96441,0.999684,0.999842,0.99779,0.998856,1,0.999132,0.997869,0.999684,0.998816,0.998343,0.998856,0.994752,0.991438,0.997396,0.997435,0.989031,0.996291,0.969973,1,0.999684,0.991004,0.986545,0.997554,0.999369,0.996962,0.999369,0.999369,0.992148,0.974747,1,0.99854,0.992819,0.991201,0.998461,0.992109,0.998422,0.981376,0.999684,0.99925,0.985243,0.999803,0.999921,0.994949,0.997238,0.998343,0.995778,0.998303,0.999684,1,0.999487,0.99854,0.999763,0.992266,0.999566,0.997988,0.998224,0.99779,0.998224,0.991635,0.998224,0.955374,0.996252] average_cost 0 f_measure 0.759712180880492 [0.578947,0.967742,1,1,0.933333,0.733333,1,0.967742,0.774194,0.896552,0.8,0.9375,0.903226,0.645161,0.967742,0.933333,1,0.137931,0.30303,0.060606,0.666667,0.967742,0.611111,1,0.789474,0.266667,0.222222,0.857143,0.875,1,0.9375,0.967742,0.9375,0.387097,0.8,0.736842,0.296296,0.090909,0.882353,0.933333,0.648649,0.875,0.967742,0.888889,0.9375,0.909091,0.827586,0.827586,0.8,0.758621,0.666667,0.8,0.83871,0.62069,0.647059,0.129032,1,0.9375,0.583333,0.625,0.8125,0.9375,0.727273,0.875,0.875,0.666667,0.222222,0.969697,0.823529,0.6,0.709677,0.827586,0.4,0.967742,0.777778,0.909091,0.866667,0.4,0.9375,0.9375,0.909091,0.83871,0.882353,0.210526,0.823529,0.903226,0.933333,0.866667,0.909091,0.967742,0.533333,0.827586,0.848485,0.9375,0.967742,0.8,0.777778,0.769231,0.193548,0.774194] kappa 0.7607323232323233 kb_relative_information_score 944.0582662268806 mean_absolute_error 0.016493592517163393 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 0.7687889740884595 [0.5,1,1,1,1,0.785714,1,1,0.8,1,0.857143,0.9375,0.933333,0.666667,1,1,1,0.153846,0.294118,0.058824,0.6,1,0.55,1,0.681818,0.285714,0.2,0.789474,0.875,1,0.9375,1,0.9375,0.4,0.857143,0.636364,0.363636,0.166667,0.833333,1,0.571429,0.875,1,0.8,0.9375,0.882353,0.923077,0.923077,0.736842,0.846154,0.714286,0.857143,0.866667,0.692308,0.611111,0.133333,1,0.9375,0.4375,0.625,0.8125,0.9375,0.705882,0.875,0.875,0.647059,0.272727,0.941176,0.777778,0.642857,0.733333,0.923077,0.428571,1,0.7,0.882353,0.928571,0.333333,0.9375,0.9375,0.882353,0.866667,0.833333,0.666667,0.777778,0.933333,1,0.928571,0.882353,1,0.571429,0.923077,0.823529,0.9375,1,0.736842,0.7,0.652174,0.2,0.8] predictive_accuracy 0.763125 prior_entropy 6.6438561897747395 recall 0.763125 [0.6875,0.9375,1,1,0.875,0.6875,1,0.9375,0.75,0.8125,0.75,0.9375,0.875,0.625,0.9375,0.875,1,0.125,0.3125,0.0625,0.75,0.9375,0.6875,1,0.9375,0.25,0.25,0.9375,0.875,1,0.9375,0.9375,0.9375,0.375,0.75,0.875,0.25,0.0625,0.9375,0.875,0.75,0.875,0.9375,1,0.9375,0.9375,0.75,0.75,0.875,0.6875,0.625,0.75,0.8125,0.5625,0.6875,0.125,1,0.9375,0.875,0.625,0.8125,0.9375,0.75,0.875,0.875,0.6875,0.1875,1,0.875,0.5625,0.6875,0.75,0.375,0.9375,0.875,0.9375,0.8125,0.5,0.9375,0.9375,0.9375,0.8125,0.9375,0.125,0.875,0.875,0.875,0.8125,0.9375,0.9375,0.5,0.75,0.875,0.9375,0.9375,0.875,0.875,0.9375,0.1875,0.75] relative_absolute_error 0.8330097230890587 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08461811912143777 root_relative_squared_error 0.8504440957315349 total_cost 0 area_under_roc_curve 0.9944689913223469 [0.974843,1,1,1,1,1,1,1,0.993671,1,1,0.996835,1,0.971519,1,0.993711,1,0.993671,0.974684,0.968553,0.993711,1,1,1,1,0.984177,0.981013,1,1,0.993711,1,1,1,0.987342,1,0.993711,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,0.977848,0.996835,1,1,0.993671,0.993711,1,1,1,0.987421,0.990506,1,1,1,1,1,1,0.984177,1,1,0.955975,0.981013,1,0.996835,1,1,1,1,0.962025,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,1,1,0.996835,1,0.987342,0.981132,0.996835,0.908228,1] area_under_roc_curve 0.9944276928588488 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.914557,1,1,1,0.977848,0.987342,0.993711,1,1,0.987421,1,1,0.971519,0.96519,1,1,1,1,1,1,0.993671,0.990506,0.993711,0.987421,0.981132,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993671,0.996835,1,1,1,0.962264,1,1,1,0.971519,1,1,1,1,1,0.996835,0.984177,1,1,1,0.990506,1,1,1,1,1,1,0.974684,1,1,0.981013,0.996835,1,1,0.993671,1,1,1,1,1,0.993671,1,1,0.987342,1,1,0.987421,0.996835,0.968354,0.974843] area_under_roc_curve 0.9949426797229517 [0.993671,1,1,1,1,0.993671,1,1,0.990506,1,1,1,0.993711,0.996835,1,1,1,0.946203,1,0.927215,1,1,0.987342,1,1,0.977848,0.981013,1,1,1,1,1,1,0.981013,1,1,0.993711,1,1,1,0.996835,0.993711,1,1,1,1,1,1,0.996835,1,0.993671,1,1,1,1,0.996835,1,1,0.981132,0.968354,0.993711,1,1,0.974843,1,0.984177,0.993671,1,1,0.993671,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.984177,1,1,1,1,1,1,1,0.924528,1] area_under_roc_curve 0.9950628433245761 [1,1,1,1,0.996835,0.996835,1,1,0.993671,1,1,1,1,1,1,1,1,0.987342,0.930818,0.993671,1,1,0.993671,1,0.987421,0.962025,0.952532,1,1,1,1,1,1,0.990506,1,1,0.987421,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,0.971519,1,1,0.987421,0.987342,1,1,1,1,1,1,0.993671,1,1,0.984177,0.968354,0.996835,0.974843,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,0.977848,0.993671,0.962264,0.993671] area_under_roc_curve 0.9932815361038134 [0.993671,1,1,1,1,1,1,1,1,1,0.993671,1,0.993711,0.962264,1,1,1,0.990506,0.974843,0.936709,1,1,0.990506,1,0.996835,0.971519,0.974684,1,1,1,1,1,1,0.974684,1,1,0.977848,0.93038,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.987342,1,0.996835,0.996835,1,0.993671,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,1,0.901899,1,0.993671,0.990506,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.974684,0.993711,1,1,0.949686,1] area_under_roc_curve 0.9930422040442639 [0.927215,1,1,1,1,0.981013,1,1,1,1,1,1,0.993711,1,1,1,1,0.955696,0.987421,0.905063,0.996835,1,0.990506,1,0.993671,0.968354,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,0.993671,0.962025,1,1,0.996835,1,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,1,1,0.987342,0.943038,1,1,0.993671,1,0.990506,1,0.993711,1,1,0.984177,1,1,0.996835,1,1,1,1,1,0.993671,1,1,0.990506,1,1,1,0.996835,1,0.993671,1,1,1,1,1,1,0.987421,1,1,1,1,1,1,1,0.930818,0.996835] area_under_roc_curve 0.9938778759652893 [0.990506,1,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,0.993711,0.993671,0.943038,0.996835,1,0.977848,1,1,0.893082,0.949686,0.993671,1,1,0.996835,1,1,0.981132,1,1,0.96519,0.949367,1,1,1,0.996835,1,1,0.981132,0.996835,0.996835,0.996835,1,1,1,1,1,1,0.993671,0.984177,1,1,0.987342,1,1,1,1,0.996835,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.987342,1,0.927215,1] area_under_roc_curve 0.994515016718414 [1,1,1,1,1,1,1,1,0.993711,1,1,1,0.990506,1,1,1,1,0.930818,0.974684,0.993671,0.993671,1,0.993671,1,0.996835,0.987421,0.993711,1,0.981132,0.996835,1,1,1,0.987421,0.996835,1,0.977848,0.943038,1,1,0.987342,1,1,1,1,1,1,0.993671,1,0.981132,0.943396,0.996835,0.996835,1,1,0.96519,1,1,0.987342,1,1,1,1,1,1,0.968553,0.924528,1,1,0.990506,1,1,1,1,0.996835,1,1,0.974843,1,1,0.990506,1,0.987342,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,0.996835,1,0.993671,0.993671] area_under_roc_curve 0.9958141768171321 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,0.990506,0.974843,0.993671,1,0.974843,1,1,1,0.993711,1,1,1,1,1,1,0.987421,1,1,0.987342,0.949367,1,1,0.993711,1,1,0.990506,1,1,1,0.993671,1,0.993711,0.984177,1,1,0.962025,0.996835,0.930818,1,1,0.996835,0.993671,1,1,1,1,1,0.993711,0.971519,1,0.996835,1,1,0.993711,0.974684,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993671,1,1,1,1,0.993671,0.993711,1,0.990506,1] area_under_roc_curve 0.9964856500278637 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.990506,1,1,1,0.962264,0.984177,0.974843,0.996835,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,1,0.993671,0.996835,0.974684,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.990506,1,0.993711,1,1,0.981013,0.993671,1,1,0.990506,1,0.993711,0.993711,0.971519,1,1,1,1,1,0.981013,0.990506,1,0.993671,1,0.993711,1,1,1,0.968553,1,0.987421,1,0.996835,1,1,1,1,0.987342,1,1,1,1,1,0.981132,1,0.996835,1] 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 0.7158642462509865 kappa 0.8105387803433984 kappa 0.7410492243318991 kappa 0.7284817869687044 kappa 0.7789270064348032 kappa 0.7600252604988949 kappa 0.7789182787208844 kappa 0.7599778927006434 kappa 0.7789444597955236 kappa 0.753739295157662 kb_relative_information_score 93.8271302999774 kb_relative_information_score 93.55152272005634 kb_relative_information_score 93.8054211198396 kb_relative_information_score 93.74917099827104 kb_relative_information_score 93.44849176078085 kb_relative_information_score 94.09245818544994 kb_relative_information_score 96.7040735912743 kb_relative_information_score 94.3832491857109 kb_relative_information_score 95.2890386818828 kb_relative_information_score 95.20770968363784 mean_absolute_error 0.016423193599650613 mean_absolute_error 0.01658796451477926 mean_absolute_error 0.01660866762696649 mean_absolute_error 0.016590712692472278 mean_absolute_error 0.01662665026976807 mean_absolute_error 0.016484338651890744 mean_absolute_error 0.016245052649957355 mean_absolute_error 0.016503408321968113 mean_absolute_error 0.016468764101950768 mean_absolute_error 0.016397172742230365 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 0.71875 predictive_accuracy 0.8125 predictive_accuracy 0.74375 predictive_accuracy 0.73125 predictive_accuracy 0.78125 predictive_accuracy 0.7625 predictive_accuracy 0.78125 predictive_accuracy 0.7625 predictive_accuracy 0.78125 predictive_accuracy 0.75625 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 0.71875 [1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0,1,0,1,0,0,0,0,1,1,1,1,0,0,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,0.5,0,1,1,0,0.5,1,0,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,1,0.5,0,1,0,1] recall 0.8125 [1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,0.5,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0] recall 0.74375 [0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,0,1,1,0,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,0,1,1,1,1,0.5,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,1,0.5,0,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,1,1,1,1,0,0,0,1,1,1,1,1,1,0,1] recall 0.73125 [0.5,1,1,1,0.5,1,1,1,0,0.5,1,0.5,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0,0,0,1,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,0,0.5] recall 0.78125 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,0,1,1,1,0,0,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0.5,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1] recall 0.7625 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,1,1,1,0,1,0,0.5,0.5,1,1,1,1,0,0,1,1,1,1,1,0.5,1,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,0,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0,0.5] recall 0.78125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,0.5,1,1,0,0,1,1,1,0.5,0,1,1,1,1,0,0,1,1,1,0.5,1,1,0,0.5,0,0.5,1,1,1,1,1,1,0.5,0,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.7625 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,0,0.5,0,0.5,1,0.5,1,0.5,1,1,1,0,1,1,1,1,1,1,1,0,0,1,1,0,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,0.5,0,0.5,0.5,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5] recall 0.78125 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,0,1,1,0,1,1,1,1,1,1,1,1,1,1,0,0,0.5,0,0,1,0.5,1,1,1,1,0.5,1,1,0,1,0.5,0.5,0,0,1,0,1,0.5,0.5,1,1,1,1,1,0,1,1,1,1,0,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1] recall 0.75625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,0,0.5,0.5,0,0,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0.5,0.5,0.5,0.5,1,1,1,1,1,0,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0,1] relative_absolute_error 0.829454222204575 relative_absolute_error 0.8377759855949108 relative_absolute_error 0.8388215973215385 relative_absolute_error 0.8379147824480934 relative_absolute_error 0.8397298116044465 relative_absolute_error 0.8325423561560968 relative_absolute_error 0.8204572045432994 relative_absolute_error 0.8335054708064689 relative_absolute_error 0.8317557627247849 relative_absolute_error 0.8281400374863808 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 0.08446739887829732 root_mean_squared_error 0.08489584120459122 root_mean_squared_error 0.08509269214449766 root_mean_squared_error 0.08499801614591526 root_mean_squared_error 0.08513188324206467 root_mean_squared_error 0.08466705000132628 root_mean_squared_error 0.08356309193797369 root_mean_squared_error 0.08456844704426146 root_mean_squared_error 0.08444180167062938 root_mean_squared_error 0.08434340024504873 root_relative_squared_error 0.8489293002927231 root_relative_squared_error 0.853235307688553 root_relative_squared_error 0.8552137340743059 root_relative_squared_error 0.8542622044865786 root_relative_squared_error 0.8556076194250696 root_relative_squared_error 0.8509358695777537 root_relative_squared_error 0.839840673576457 root_relative_squared_error 0.8499448725722886 root_relative_squared_error 0.8486720386760099 root_relative_squared_error 0.8476830671381702 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 1896.71004200045 usercpu_time_millis 1699.5795729999372 usercpu_time_millis 1685.8059849996607 usercpu_time_millis 1692.6598989998638 usercpu_time_millis 1699.0022440004395 usercpu_time_millis 1690.763020000304 usercpu_time_millis 1692.9229709994615 usercpu_time_millis 1696.1980820001372 usercpu_time_millis 1702.1498749995772 usercpu_time_millis 1692.5145309996878 usercpu_time_millis_testing 163.15538600019863 usercpu_time_millis_testing 172.75641100013672 usercpu_time_millis_testing 157.3207569999795 usercpu_time_millis_testing 161.8441939999684 usercpu_time_millis_testing 164.48104000028252 usercpu_time_millis_testing 155.68840500009173 usercpu_time_millis_testing 161.9215279997661 usercpu_time_millis_testing 161.41941600017162 usercpu_time_millis_testing 170.3565439997874 usercpu_time_millis_testing 161.39881899971442 usercpu_time_millis_training 1733.5546560002513 usercpu_time_millis_training 1526.8231619998005 usercpu_time_millis_training 1528.4852279996812 usercpu_time_millis_training 1530.8157049998954 usercpu_time_millis_training 1534.521204000157 usercpu_time_millis_training 1535.0746150002124 usercpu_time_millis_training 1531.0014429996954 usercpu_time_millis_training 1534.7786659999656 usercpu_time_millis_training 1531.7933309997898 usercpu_time_millis_training 1531.1157119999734