8703031 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) 6680450 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 0.07301159244258999 7708 cache_size 200 7708 class_weight null 7708 coef0 0.0946348038069853 7708 decision_function_shape null 7708 degree 5 7708 gamma 0.033844071710374055 7708 kernel "poly" 7708 max_iter -1 7708 probability true 7708 random_state 59835 7708 shrinking false 7708 tol 0.00032112911253064124 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 18322953 description https://api.openml.org/data/download/18322953/description.xml -1 18322954 predictions https://api.openml.org/data/download/18322954/predictions.arff area_under_roc_curve 0.9954269255050505 [0.983862,0.999369,0.999961,0.99929,0.998382,0.991832,0.999684,0.997672,0.995857,0.999842,0.998461,0.999645,0.998974,0.945115,0.998224,0.99858,1,0.991398,0.983783,0.973445,0.997435,0.999369,0.993884,0.999014,0.99854,0.978299,0.989268,0.99858,0.997948,1,0.999092,0.999921,1,0.989978,0.996567,0.998343,0.993056,0.983389,0.998974,0.999171,0.998343,0.996804,1,0.99854,0.995739,1,0.998856,0.998185,0.99712,0.996686,0.994949,0.998461,0.997869,0.993884,0.99491,0.993056,1,0.999014,0.992109,0.996054,0.997041,0.999448,0.997041,0.998974,0.998698,0.992424,0.98477,0.999684,0.996962,0.992266,0.997396,0.998422,0.994239,0.997041,0.989623,0.99929,0.999053,0.99057,0.99929,1,0.996054,0.995975,0.997475,0.995068,0.999092,0.999842,0.999803,0.998737,0.998895,0.999408,0.999487,0.999605,0.997633,0.99566,0.993174,0.99562,0.996054,0.997672,0.966067,0.994358] average_cost 0 f_measure 0.7964938371301803 [0.666667,0.903226,0.967742,0.814815,0.896552,0.758621,0.9375,0.736842,0.787879,0.857143,0.848485,0.896552,0.882353,0.689655,0.857143,0.823529,0.933333,0.625,0.411765,0.242424,0.774194,0.9375,0.742857,0.857143,0.857143,0.444444,0.545455,0.848485,0.903226,1,0.903226,0.967742,0.9375,0.516129,0.8,0.848485,0.5625,0.482759,0.909091,0.896552,0.727273,0.875,0.967742,0.848485,0.857143,0.941176,0.896552,0.848485,0.666667,0.774194,0.848485,0.774194,0.787879,0.647059,0.774194,0.625,0.896552,0.9375,0.65,0.702703,0.785714,0.882353,0.689655,0.9375,0.909091,0.684211,0.428571,0.9375,0.733333,0.6875,0.823529,0.785714,0.709677,0.896552,0.7,0.9375,0.896552,0.615385,0.967742,1,0.848485,0.8125,0.83871,0.5625,0.909091,0.866667,0.967742,0.8,0.875,0.933333,0.8125,0.785714,0.866667,0.8,0.882353,0.777778,0.8,0.774194,0.571429,0.83871] kappa 0.7916666666666666 kb_relative_information_score 954.0995340801903 mean_absolute_error 0.015994062642802544 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 0.8091000587744765 [0.6,0.933333,1,1,1,0.846154,0.9375,0.636364,0.764706,1,0.823529,1,0.833333,0.769231,1,0.777778,1,0.625,0.388889,0.235294,0.8,0.9375,0.684211,0.789474,0.789474,0.4,0.529412,0.823529,0.933333,1,0.933333,1,0.9375,0.533333,0.857143,0.823529,0.5625,0.538462,0.882353,1,0.705882,0.875,1,0.823529,1,0.888889,1,0.823529,0.647059,0.8,0.823529,0.8,0.764706,0.611111,0.8,0.625,1,0.9375,0.541667,0.619048,0.916667,0.833333,0.769231,0.9375,0.882353,0.590909,0.5,0.9375,0.785714,0.6875,0.777778,0.916667,0.733333,1,0.583333,0.9375,1,0.521739,1,1,0.823529,0.8125,0.866667,0.5625,0.882353,0.928571,1,0.857143,0.875,1,0.8125,0.916667,0.928571,0.857143,0.833333,0.7,0.736842,0.8,0.526316,0.866667] predictive_accuracy 0.79375 prior_entropy 6.6438561897747395 recall 0.79375 [0.75,0.875,0.9375,0.6875,0.8125,0.6875,0.9375,0.875,0.8125,0.75,0.875,0.8125,0.9375,0.625,0.75,0.875,0.875,0.625,0.4375,0.25,0.75,0.9375,0.8125,0.9375,0.9375,0.5,0.5625,0.875,0.875,1,0.875,0.9375,0.9375,0.5,0.75,0.875,0.5625,0.4375,0.9375,0.8125,0.75,0.875,0.9375,0.875,0.75,1,0.8125,0.875,0.6875,0.75,0.875,0.75,0.8125,0.6875,0.75,0.625,0.8125,0.9375,0.8125,0.8125,0.6875,0.9375,0.625,0.9375,0.9375,0.8125,0.375,0.9375,0.6875,0.6875,0.875,0.6875,0.6875,0.8125,0.875,0.9375,0.8125,0.75,0.9375,1,0.875,0.8125,0.8125,0.5625,0.9375,0.8125,0.9375,0.75,0.875,0.875,0.8125,0.6875,0.8125,0.75,0.9375,0.875,0.875,0.75,0.625,0.8125] relative_absolute_error 0.8077809415556826 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.0828956593477526 root_relative_squared_error 0.8331327236533707 total_cost 0 area_under_roc_curve 0.9960107176976354 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.974684,1,0.974843,1,1,0.984177,0.993711,0.987421,1,1,1,1,0.990506,0.993671,1,1,1,1,1,1,0.993671,0.993671,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,0.993711,1,1,0.993671,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.962025,1,1,0.943396,0.996835,1,1,1,1,1,1,0.955696,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.946203,1] area_under_roc_curve 0.994901132473529 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.867089,1,1,1,0.977848,0.993671,0.987421,1,0.993711,0.987421,1,1,0.987342,0.968354,1,1,1,1,1,1,0.996835,0.981013,1,0.993711,1,1,1,1,1,1,1,0.996835,1,1,1,0.993671,1,1,0.993671,1,1,1,0.993711,1,1,1,0.987342,1,1,1,1,1,0.996835,1,1,0.981132,1,1,1,1,1,1,1,0.996835,0.993671,1,1,0.971519,0.990506,1,0.981132,0.993671,1,1,1,1,1,1,1,1,0.958861,1,1,1,0.996835,1,0.981132] area_under_roc_curve 0.9963259294642146 [1,1,1,1,1,1,0.993711,1,0.993671,1,1,1,1,1,0.993671,1,1,0.993671,1,0.936709,1,1,0.990506,1,1,0.952532,0.993671,0.993711,1,1,1,1,1,0.984177,1,1,0.993711,1,0.993711,0.996835,0.996835,0.968553,1,1,1,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.984177,1,1,1,0.987421,0.996835,0.990506,1,1,0.993711,0.990506,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,0.996835,0.996835,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1,1,1,1,1] area_under_roc_curve 0.9967618024042671 [0.996835,1,1,0.993711,1,0.984177,1,1,0.996835,1,1,1,1,1,0.993671,1,1,0.987342,0.943396,0.981013,1,1,0.993671,1,0.993711,1,1,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.990506,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,0.996835,1,1,0.974684,0.987342,0.996835,0.993711,1,0.993711,1,1,0.993671,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.984177,1,1,1] area_under_roc_curve 0.9952245541756228 [1,1,1,1,1,1,1,0.996835,1,1,0.987342,1,1,0.886792,1,0.996835,1,0.993671,0.968553,0.984177,1,1,1,1,1,0.990506,0.990506,1,1,1,1,1,1,0.984177,1,1,1,0.984177,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981132,1,1,0.993671,0.984177,1,0.990506,1,1,0.993671,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.943038,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.962025,0.993711,1,1,0.90566,1] area_under_roc_curve 0.995452193296712 [0.905063,1,1,1,1,0.981013,1,0.993671,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.955696,1,1,1,1,0.993671,0.981013,0.977848,1,1,1,1,1,1,0.993671,1,0.993671,1,0.987342,0.996835,1,0.990506,1,1,0.993711,1,1,1,1,0.993671,0.993671,1,1,0.993671,1,0.996835,0.987342,1,1,0.993671,0.993711,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,0.996835,1,1,0.996835,1,1,1,1,1,1,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.990506] area_under_roc_curve 0.994944918796274 [0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,0.996835,1,1,1,1,1,1,0.974684,0.993671,1,0.996835,1,1,0.893082,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.974843,1,0.996835,0.996835,0.993711,1,1,1,1,1,0.996835,1,1,0.996835,0.990506,1,1,1,1,1,1,0.981132,1,0.996835,1,1,1,1,0.996835,1,1,1,1,0.993711,0.993711,1,0.990506,1,0.996835,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.996835,1,0.838608,1] area_under_roc_curve 0.9969245083990128 [1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.993711,1,1,0.987421,0.993671,0.984177,0.996835,1,0.996835,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,0.981132,0.962264,1,1,1,1,0.990506,1,1,0.993671,1,1,1,1,1,1,0.981132,0.91195,1,1,0.996835,1,1,1,1,1,1,1,0.968553,1,1,0.996835,1,0.993671,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.993671,1,1,1,0.990506] area_under_roc_curve 0.9968409163283175 [1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,0.993711,0.996835,0.981132,0.996835,1,0.955975,0.990506,1,0.987421,1,1,1,1,1,1,1,0.993711,1,1,1,0.955696,1,1,1,1,1,1,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.987342,0.971519,1,1,1,1,1,0.990506,1,1,1,1,1,0.990506,1,1,1,1,0.993711,0.974684,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,0.987421,0.987342,1,1,1,1] area_under_roc_curve 0.997155381737123 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,1,1,0.962025,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993671,0.996835,0.993711,0.987421,0.981013,1,1,1,1,1,0.990506,0.990506,1,0.987342,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.993671,1,1,1,1,1,1,1,0.996835,0.993711,1,1,1] 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 0.7789444597955236 kappa 0.7662665824384082 kappa 0.7978521794061908 kappa 0.78520097923083 kappa 0.8041306322315681 kappa 0.7978521794061908 kappa 0.8104489989337756 kappa 0.8104489989337756 kappa 0.8041615667074664 kappa 0.7600442023837714 kb_relative_information_score 97.10345056119097 kb_relative_information_score 94.05918189383223 kb_relative_information_score 94.19722411405357 kb_relative_information_score 93.27954503640434 kb_relative_information_score 95.31905548256475 kb_relative_information_score 95.13981461361364 kb_relative_information_score 99.39528139893788 kb_relative_information_score 95.46145576127253 kb_relative_information_score 95.15285493803944 kb_relative_information_score 94.99167028028171 mean_absolute_error 0.01573316511249928 mean_absolute_error 0.01622960726804363 mean_absolute_error 0.0162240163258322 mean_absolute_error 0.016353346505631593 mean_absolute_error 0.016118987796287763 mean_absolute_error 0.01597002280642612 mean_absolute_error 0.01541433858340906 mean_absolute_error 0.016047412001490267 mean_absolute_error 0.01596769788825898 mean_absolute_error 0.015882032140146468 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 0.78125 predictive_accuracy 0.76875 predictive_accuracy 0.8 predictive_accuracy 0.7875 predictive_accuracy 0.80625 predictive_accuracy 0.8 predictive_accuracy 0.8125 predictive_accuracy 0.8125 predictive_accuracy 0.80625 predictive_accuracy 0.7625 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 0.78125 [1,0.5,1,0,0.5,1,1,1,1,1,0.5,0.5,1,0,1,0,0.5,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.76875 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,0.5,1,0,1,1,1,1,0.5,1,0.5,1,0.5,0,1,1,1,1,0.5,0] recall 0.8 [1,1,1,1,0.5,1,0,1,1,1,1,0.5,1,1,0,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,0,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,1,0,1,0,1,1,1,1,1,0.5,1,1] recall 0.7875 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0,1,1,0.5,0.5,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0,1,1,1,0.5,0.5,1,1] recall 0.80625 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1,0,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,0.5,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.8 [0.5,1,0.5,1,1,0,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,0.5,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5] recall 0.8125 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,1,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,0,0.5,0.5,0.5,0.5,0,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.8125 [0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,0.5,1,0,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5] recall 0.80625 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0,0,1,1,1,0.5,1,1,1,0,1,0.5,1,1,0.5,0,0,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0,1,1,1,0.5,1,1,0.5,1] recall 0.7625 [1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,1,0.5,1,1,1,0.5,1,0,1,1,1,0,1,0,1,1,0,0,0.5,0.5,0.5,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.7946042986110734 relative_absolute_error 0.8196771347496769 relative_absolute_error 0.8193947639309177 relative_absolute_error 0.8259265911935135 relative_absolute_error 0.8140902927418048 relative_absolute_error 0.8065668084053583 relative_absolute_error 0.7785019486570219 relative_absolute_error 0.8104753536106182 relative_absolute_error 0.8064493882959067 relative_absolute_error 0.8021228353609314 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 0.08186316261482544 root_mean_squared_error 0.08363166409135933 root_mean_squared_error 0.08363767754827409 root_mean_squared_error 0.08430511797962877 root_mean_squared_error 0.08317923000114526 root_mean_squared_error 0.08289344463363037 root_mean_squared_error 0.08060493575632305 root_mean_squared_error 0.08281882908378624 root_mean_squared_error 0.08297662358573542 root_mean_squared_error 0.08298864430857747 root_relative_squared_error 0.8227557410461371 root_relative_squared_error 0.8405298496487204 root_relative_squared_error 0.8405902871647178 root_relative_squared_error 0.8472983158941616 root_relative_squared_error 0.8359827159529449 root_relative_squared_error 0.8331104649389427 root_relative_squared_error 0.8101100853164402 root_relative_squared_error 0.8323605504469456 root_relative_squared_error 0.8339464448619347 root_relative_squared_error 0.834067257672165 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 1752.599283001473 usercpu_time_millis 1545.3463879985065 usercpu_time_millis 1530.6577400006063 usercpu_time_millis 1539.107406999392 usercpu_time_millis 1579.6237639988249 usercpu_time_millis 1550.167830000646 usercpu_time_millis 1568.0911989984452 usercpu_time_millis 1535.2056009996886 usercpu_time_millis 1536.965715000406 usercpu_time_millis 1554.187412999454 usercpu_time_millis_testing 140.66076900053304 usercpu_time_millis_testing 137.72229699861782 usercpu_time_millis_testing 138.6461860001873 usercpu_time_millis_testing 137.01754800058552 usercpu_time_millis_testing 138.6136159999296 usercpu_time_millis_testing 138.22136599992518 usercpu_time_millis_testing 146.44137999857776 usercpu_time_millis_testing 137.14493899897207 usercpu_time_millis_testing 136.53819099999964 usercpu_time_millis_testing 138.1547769997269 usercpu_time_millis_training 1611.93851400094 usercpu_time_millis_training 1407.6240909998887 usercpu_time_millis_training 1392.011554000419 usercpu_time_millis_training 1402.0898589988064 usercpu_time_millis_training 1441.0101479988953 usercpu_time_millis_training 1411.9464640007209 usercpu_time_millis_training 1421.6498189998674 usercpu_time_millis_training 1398.0606620007165 usercpu_time_millis_training 1400.4275240004063 usercpu_time_millis_training 1416.032635999727