8830050 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) 6805100 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 5040.179471452119 7708 cache_size 200 7708 class_weight null 7708 coef0 0.3058714945438783 7708 decision_function_shape null 7708 degree 4 7708 gamma 0.10836485505589782 7708 kernel "rbf" 7708 max_iter -1 7708 probability true 7708 random_state 42611 7708 shrinking true 7708 tol 1.1628969117765793e-05 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 18575886 description https://api.openml.org/data/download/18575886/description.xml -1 18575887 predictions https://api.openml.org/data/download/18575887/predictions.arff area_under_roc_curve 0.9944298453282825 [0.992424,0.999684,1,0.998224,0.997435,0.976997,0.999882,0.999921,0.997238,0.966501,0.997711,0.99929,0.99925,0.940972,0.99779,0.997001,1,0.985953,0.987492,0.971315,0.996765,0.999724,0.993529,1,0.997751,0.980784,0.988005,0.998619,0.999369,0.999803,0.999566,0.988834,0.999645,0.991083,0.997869,0.998856,0.993371,0.984493,0.999053,0.998619,0.996567,0.994003,1,0.998146,0.998856,0.998935,0.980548,0.999171,0.997869,0.99854,0.996094,0.99712,0.996054,0.994358,0.994989,0.991162,1,0.999921,0.995541,0.988321,0.998816,0.999408,0.993884,0.999803,0.999487,0.99349,0.986466,1,0.994989,0.996291,0.995936,0.998027,0.994397,0.995147,0.974392,0.999961,0.999408,0.973958,1,0.998856,0.990846,0.995344,0.995502,0.996488,0.999211,0.999763,0.999882,0.99783,0.998343,0.99929,0.999132,0.998974,0.997869,0.998816,0.999763,0.99858,0.994121,0.997554,0.967487,0.998461] average_cost 0 f_measure 0.7979634973051618 [0.769231,0.967742,0.933333,0.233577,0.903226,0.592593,0.967742,0.967742,0.8,0.857143,0.827586,0.827586,0.933333,0.8125,0.787879,0.933333,1,0.4,0.424242,0.2,0.823529,0.933333,0.6875,0.896552,0.9375,0.533333,0.4,0.787879,0.903226,0.967742,0.967742,0.896552,0.933333,0.606061,0.777778,0.83871,0.4,0.4375,0.903226,0.866667,0.75,0.83871,0.967742,0.903226,0.933333,0.967742,0.875,0.909091,0.823529,0.857143,0.785714,0.774194,0.827586,0.733333,0.764706,0.533333,0.933333,0.9375,0.666667,0.6875,0.866667,0.903226,0.75,0.903226,0.875,0.625,0.533333,0.933333,0.787879,0.756757,0.6875,0.866667,0.705882,0.866667,0.787879,0.933333,0.896552,0.6,0.933333,0.866667,0.909091,0.866667,0.785714,0.62069,0.785714,0.896552,0.866667,0.787879,0.896552,0.967742,0.83871,0.785714,0.896552,0.903226,0.903226,0.866667,0.727273,0.823529,0.545455,0.9375] kappa 0.7758838383838383 kb_relative_information_score 943.0141776326207 mean_absolute_error 0.01545987340992855 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 0.8328227410391779 [1,1,1,0.132231,0.933333,0.727273,1,1,0.857143,1,0.923077,0.923077,1,0.8125,0.764706,1,1,0.428571,0.411765,0.214286,0.777778,1,0.6875,1,0.9375,0.571429,0.368421,0.764706,0.933333,1,1,1,1,0.588235,0.7,0.866667,0.428571,0.4375,0.933333,0.928571,0.75,0.866667,1,0.933333,1,1,0.875,0.882353,0.777778,1,0.916667,0.8,0.923077,0.785714,0.722222,0.571429,1,0.9375,0.647059,0.6875,0.928571,0.933333,0.75,0.933333,0.875,0.625,0.571429,1,0.764706,0.666667,0.6875,0.928571,0.666667,0.928571,0.764706,1,1,0.642857,1,0.928571,0.882353,0.928571,0.916667,0.692308,0.916667,1,0.928571,0.764706,1,1,0.866667,0.916667,1,0.933333,0.933333,0.928571,0.705882,0.777778,0.529412,0.9375] predictive_accuracy 0.778125 prior_entropy 6.6438561897747395 recall 0.778125 [0.625,0.9375,0.875,1,0.875,0.5,0.9375,0.9375,0.75,0.75,0.75,0.75,0.875,0.8125,0.8125,0.875,1,0.375,0.4375,0.1875,0.875,0.875,0.6875,0.8125,0.9375,0.5,0.4375,0.8125,0.875,0.9375,0.9375,0.8125,0.875,0.625,0.875,0.8125,0.375,0.4375,0.875,0.8125,0.75,0.8125,0.9375,0.875,0.875,0.9375,0.875,0.9375,0.875,0.75,0.6875,0.75,0.75,0.6875,0.8125,0.5,0.875,0.9375,0.6875,0.6875,0.8125,0.875,0.75,0.875,0.875,0.625,0.5,0.875,0.8125,0.875,0.6875,0.8125,0.75,0.8125,0.8125,0.875,0.8125,0.5625,0.875,0.8125,0.9375,0.8125,0.6875,0.5625,0.6875,0.8125,0.8125,0.8125,0.8125,0.9375,0.8125,0.6875,0.8125,0.875,0.875,0.8125,0.75,0.875,0.5625,0.9375] relative_absolute_error 0.7808016873701275 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08103078546476461 root_relative_squared_error 0.8143900359224486 total_cost 0 area_under_roc_curve 0.9956539586816338 [0.987421,1,1,1,0.996835,0.981132,1,1,1,1,1,1,1,1,1,0.974843,1,0.981013,0.974684,1,1,1,1,1,0.962264,0.981013,0.990506,1,1,1,1,1,1,0.993671,0.996835,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,0.993711,1,1,1,1,1,0.993711,0.990506,1,1,1,0.981013,1,1,1,0.996835,1,1,1,0.993711,1,0.996835,0.920886,1,1,1,1,0.993711,1,1,1,1,1,0.993671,1,0.996835,1,1,0.996835,1,0.990506,1,0.993671,0.949367,1] area_under_roc_curve 0.9936776032959159 [1,1,1,1,1,1,0.996835,1,1,1,1,0.996835,1,0.787975,0.996835,1,1,0.984177,0.993671,0.962264,1,1,1,1,1,0.996835,0.974684,1,1,1,1,1,1,0.996835,1,0.993711,0.974843,0.993711,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.974843,1,1,1,0.984177,1,1,1,1,1,0.993671,0.984177,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,0.993671,0.946203,0.993671,0.974843,0.993711,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.974843,0.993671,0.984177,0.987421] area_under_roc_curve 0.996244327680917 [0.996835,1,1,1,1,0.974684,1,1,0.996835,1,1,0.996835,1,1,1,1,1,0.968354,1,0.93038,1,0.993711,0.977848,1,1,0.971519,1,1,1,1,1,1,1,0.993671,0.987421,1,0.993711,0.987421,1,1,0.993671,1,1,1,1,1,0.984177,1,0.993671,1,1,1,1,1,1,1,1,1,0.987421,0.968354,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,0.993711,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1] area_under_roc_curve 0.997237232306345 [1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,0.990506,0.974843,0.984177,1,1,1,1,1,0.996835,0.984177,1,1,1,1,1,1,0.990506,1,0.993711,1,0.955975,1,1,0.996835,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.984177,1,0.993671,1,1,1,1,1,1,1,1,1,0.996835,0.993671,1,1,0.990506,0.987342,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1,0.996835,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,0.937107,0.996835] area_under_roc_curve 0.9952641111376481 [1,1,1,1,1,0.958861,1,0.996835,1,1,0.993671,1,1,0.830189,1,1,1,0.990506,0.993711,0.981013,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,0.984177,1,1,1,0.981013,1,1,1,1,1,1,0.987421,1,1,1,1,1,0.981132,0.981132,0.996835,1,0.977848,0.984177,1,0.996835,1,1,0.996835,1,0.993711,1,1,1,1,1,0.996835,0.993671,1,1,1,0.993711,0.962025,1,1,1,1,1,1,1,0.987342,0.993671,1,1,1,1,1,1,1,1,1,0.993711,0.996835,1,0.996835,1,0.974843,0.996835] area_under_roc_curve 0.9968371845394473 [0.977848,1,1,0.996835,1,0.987342,1,1,1,1,0.996835,1,0.987421,1,1,1,1,0.996835,1,0.977848,1,1,0.990506,1,1,0.993671,0.984177,1,1,1,1,1,1,0.977848,0.987421,1,1,0.996835,1,1,0.993671,1,1,1,1,1,0.996835,1,1,1,1,1,0.987342,1,0.996835,0.993671,1,1,0.996835,0.993711,0.996835,0.996835,0.993711,1,1,0.977848,1,1,0.993671,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,0.990506,0.993671,0.993671,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,0.993711,1,1] area_under_roc_curve 0.9953797965926279 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.971519,0.981013,1,1,1,1,0.886792,0.962264,1,1,1,0.993671,1,1,1,1,1,0.968354,0.981013,1,0.993711,0.996835,0.990506,1,1,1,0.993671,1,0.993671,1,1,0.981132,1,1,1,1,1,1,1,0.993671,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.993711,0.993711,1,1,1,1,1,1,1,1,1,0.89557,1] area_under_roc_curve 0.9971969289865455 [0.977848,1,1,1,1,0.993671,1,1,0.993711,1,1,1,1,1,1,1,1,0.987421,0.971519,0.977848,0.987342,1,1,1,1,1,1,1,0.993711,0.996835,1,1,1,1,1,1,0.987342,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671,1,1,1,0.990506,1,1,0.990506,0.981132,1,1,1,1,1,0.968553,0.981132,1,1,0.990506,1,1,1,1,0.996835,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1] area_under_roc_curve 0.9976320555688242 [0.993711,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,1,1,1,1,0.993711,1,0.955975,1,1,1,1,1,0.981132,0.993711,0.996835,1,1,1,1,0.993711,0.993711,0.996835,1,1,0.977848,1,1,0.993711,1,1,0.990506,1,1,1,1,0.993711,1,0.996835,1,1,0.987342,0.996835,0.987421,1,1,0.996835,0.996835,1,1,1,1,1,1,0.974684,1,1,1,1,0.987421,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1] area_under_roc_curve 0.9970003881060423 [1,1,1,1,1,0.987421,1,1,1,0.987421,0.993711,1,1,0.949367,1,1,1,0.981132,0.993671,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.993671,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,1,0.993711,0.993711,1,1,1,0.993671,1,0.987421,1,1,0.984177,1,1,1,0.984177,1,0.993711,1,0.990506,1,0.977848,1,0.981132,0.993711,0.993671,1,0.993671,1,1,1,1,1,1,0.981132,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1] 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 0.7915515199368337 kappa 0.7852603323727944 kappa 0.8105238226818774 kappa 0.7726106351900833 kappa 0.7471754760211741 kappa 0.7599210266535044 kappa 0.7598261900059253 kappa 0.7851840151634812 kappa 0.7788396982741598 kappa 0.7662388943731491 kb_relative_information_score 92.49252906741602 kb_relative_information_score 93.10363505241256 kb_relative_information_score 96.50274983535266 kb_relative_information_score 95.51270727826783 kb_relative_information_score 94.28007507819542 kb_relative_information_score 92.16524442634207 kb_relative_information_score 97.31071610933576 kb_relative_information_score 96.75185212383167 kb_relative_information_score 93.83440267314782 kb_relative_information_score 91.06026598831842 mean_absolute_error 0.015654354026013124 mean_absolute_error 0.015447712011505766 mean_absolute_error 0.015075879762043323 mean_absolute_error 0.01533975978651883 mean_absolute_error 0.015429291869006111 mean_absolute_error 0.015724464784370736 mean_absolute_error 0.014932028746295938 mean_absolute_error 0.015375955929818445 mean_absolute_error 0.0155478296170556 mean_absolute_error 0.0160714575666576 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 0.79375 predictive_accuracy 0.7875 predictive_accuracy 0.8125 predictive_accuracy 0.775 predictive_accuracy 0.75 predictive_accuracy 0.7625 predictive_accuracy 0.7625 predictive_accuracy 0.7875 predictive_accuracy 0.78125 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 0.79375 [0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,0.5,0,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.7875 [1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,0,0,0,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,0.5,0.5,0,0,0.5,1,0.5,1,0.5,1,0.5,1,0.5,1,1,1,0,1,0.5,0] recall 0.8125 [1,1,0.5,1,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,0,1,0,1,0,0,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,0,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1] recall 0.775 [0,1,0.5,1,0.5,0.5,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0,0,0.5,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,0.5,0,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,0,1,1,1,0.5,1,1,1,1,1,0,1,0,1] recall 0.75 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,0.5,0.5,0,0,1,1,1,0,1,0.5,1,1,0.5,1,1,1,0,1,1,1,1,0,1,0.5,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0,0,0.5,1,0.5,0,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,0.5,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,0.5,0.5,1,0,1,1,1,1,1,1,1,0,0.5,1,1,1,0,1] recall 0.7625 [0.5,1,1,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,0.5,1,1,1,0.5,0.5,0.5,0.5,0.5,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0,1,0.5,0.5,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1] recall 0.7625 [1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,0,0,1,1,1,0.5,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,0.5,1,0,1,0,1,1,0,0.5,0.5,1,1,1,0,1,1,0,1,0.5,1,1,0,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,0,0,1,1,1,0,1,1,0.5,1,1,0,1] recall 0.7875 [0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,1,0.5,0,0.5,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,0.5,1,1,0,1,1,1,1,0.5,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0,0.5,1,0,1,1,1,0,1,1,1,1,1,1,1,1] recall 0.78125 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,0.5,0,1,0.5,1,1,0,0.5,1,0,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0.5,0,1,0,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,0,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,0.5,0.5,1] recall 0.76875 [1,1,1,1,1,0,0.5,1,1,0,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,1,0,0,0,0.5,0.5,0,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,0.5,1,0.5,0,1,1,1,1,1] relative_absolute_error 0.7906239407077322 relative_absolute_error 0.7801874753285728 relative_absolute_error 0.7614080687900655 relative_absolute_error 0.7747353427534749 relative_absolute_error 0.7792571651013175 relative_absolute_error 0.7941648880995308 relative_absolute_error 0.754142865974541 relative_absolute_error 0.7765634307989102 relative_absolute_error 0.7852439200533118 relative_absolute_error 0.8116897760938169 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 0.08171227384988651 root_mean_squared_error 0.08118853941580637 root_mean_squared_error 0.07948478197560015 root_mean_squared_error 0.08078904005014208 root_mean_squared_error 0.08071954698326676 root_mean_squared_error 0.08236032572421356 root_mean_squared_error 0.07879276473201537 root_mean_squared_error 0.0804502918204782 root_mean_squared_error 0.08151295295574322 root_mean_squared_error 0.08320413749316637 root_relative_squared_error 0.8212392518995233 root_relative_squared_error 0.815975522785484 root_relative_squared_error 0.7988521162311192 root_relative_squared_error 0.8119604030888372 root_relative_squared_error 0.811261971487757 root_relative_squared_error 0.8277524182990064 root_relative_squared_error 0.7918970812449777 root_relative_squared_error 0.8085558552821942 root_relative_squared_error 0.8192360015396705 root_relative_squared_error 0.8362330456665897 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 3833.0040719999943 usercpu_time_millis 3604.257589000099 usercpu_time_millis 3603.1133110000155 usercpu_time_millis 3632.247396000025 usercpu_time_millis 3636.6487130001133 usercpu_time_millis 3637.4411349999036 usercpu_time_millis 3716.1359870000297 usercpu_time_millis 3631.0995520000233 usercpu_time_millis 3638.453367000011 usercpu_time_millis 3602.92670299998 usercpu_time_millis_testing 170.7984419999775 usercpu_time_millis_testing 169.51527100002295 usercpu_time_millis_testing 170.92457499995817 usercpu_time_millis_testing 171.47072900002058 usercpu_time_millis_testing 169.2921030000889 usercpu_time_millis_testing 175.76827699997466 usercpu_time_millis_testing 170.94214100006866 usercpu_time_millis_testing 170.88031200000842 usercpu_time_millis_testing 167.91920399998617 usercpu_time_millis_testing 167.1629899999516 usercpu_time_millis_training 3662.2056300000168 usercpu_time_millis_training 3434.742318000076 usercpu_time_millis_training 3432.1887360000574 usercpu_time_millis_training 3460.7766670000046 usercpu_time_millis_training 3467.3566100000244 usercpu_time_millis_training 3461.672857999929 usercpu_time_millis_training 3545.193845999961 usercpu_time_millis_training 3460.219240000015 usercpu_time_millis_training 3470.534163000025 usercpu_time_millis_training 3435.7637130000285