6026025 1 Jan van Rijn 9954 Supervised Classification 6969 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.ensemble.forest.RandomForestClassifier)(1) 4060710 bootstrap true 6902 class_weight null 6902 criterion "gini" 6902 max_depth null 6902 max_features 0.4572912158997313 6902 max_leaf_nodes null 6902 min_impurity_split 1e-07 6902 min_samples_leaf 2 6902 min_samples_split 12 6902 min_weight_fraction_leaf 0.0 6902 n_estimators 100 6902 n_jobs 1 6902 oob_score false 6902 random_state 7901 6902 verbose 0 6902 warm_start false 6902 axis 0 6947 categorical_features [] 6947 copy true 6947 fill_empty 0 6947 missing_values "NaN" 6947 strategy "median" 6947 strategy_nominal "most_frequent" 6947 verbose 0 6947 categorical_features [] 6948 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 6948 handle_unknown "ignore" 6948 n_values "auto" 6948 sparse true 6948 threshold 0.0 6949 openml-pimp openml-python Sklearn_0.18.1. study_71 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 12975161 description https://api.openml.org/data/download/12975161/description.xml -1 12975162 predictions https://api.openml.org/data/download/12975162/predictions.arff area_under_roc_curve 0.9959935290404042 [0.99562,0.999724,0.999921,1,0.999684,0.996922,0.999605,0.999803,0.998106,0.999803,0.999014,0.999921,0.999684,0.99345,1,0.999842,1,0.982797,0.990964,0.975852,0.999724,0.999882,0.996449,1,0.998777,0.968711,0.984336,0.998856,0.998264,0.999961,0.999842,0.999921,0.998974,0.9884,0.999803,0.997475,0.994437,0.989465,0.999487,0.999842,0.999645,0.999487,0.999053,0.998303,0.998737,0.999842,0.998698,0.99925,0.996843,0.997475,0.998303,0.993687,0.996015,0.985085,0.997435,0.98035,0.999961,0.999171,0.994003,0.991241,1,0.999211,0.993766,0.998382,0.998264,0.996488,0.980824,0.999763,0.988242,0.99566,0.995699,0.99854,0.995818,1,0.973366,0.999487,0.999724,0.992898,0.999763,0.999527,0.995975,0.997593,0.99858,0.99708,0.996804,0.997277,0.999961,0.993766,0.998816,1,0.999645,0.999053,0.997869,0.992977,0.998422,0.999092,0.998224,0.996449,0.970683,0.999763] average_cost 0 f_measure 0.8000551166432888 [0.642857,0.833333,0.967742,1,0.967742,0.785714,0.909091,0.969697,0.875,0.9375,0.909091,0.914286,0.909091,0.758621,1,0.933333,0.914286,0.514286,0.588235,0.424242,0.866667,0.9375,0.774194,1,0.823529,0.363636,0.275862,0.83871,0.848485,0.969697,0.9375,0.866667,0.875,0.5,0.941176,0.756757,0.5,0.444444,0.848485,0.9375,0.833333,0.969697,0.777778,0.83871,0.9375,0.909091,0.83871,0.896552,0.6875,0.8,0.848485,0.611111,0.787879,0.666667,0.787879,0.516129,0.969697,0.866667,0.545455,0.709677,1,0.8,0.727273,0.914286,0.882353,0.810811,0.30303,0.914286,0.56,0.666667,0.733333,0.866667,0.647059,0.967742,0.64,0.875,0.9375,0.608696,0.969697,0.903226,0.740741,0.774194,0.787879,0.666667,0.666667,0.875,1,0.742857,0.848485,1,0.864865,0.882353,0.875,0.769231,0.9375,0.857143,0.909091,0.774194,0.538462,0.909091] kappa 0.8017676767676767 kb_relative_information_score 1164.8371715524504 mean_absolute_error 0.013117729901332819 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 0.8107639014752014 [0.75,0.75,1,1,1,0.916667,0.882353,0.941176,0.875,0.9375,0.882353,0.842105,0.882353,0.846154,1,1,0.842105,0.473684,0.555556,0.411765,0.928571,0.9375,0.8,1,0.777778,0.666667,0.307692,0.866667,0.823529,0.941176,0.9375,0.928571,0.875,0.5,0.888889,0.666667,0.5,0.4,0.823529,0.9375,0.75,0.941176,0.7,0.866667,0.9375,0.882353,0.866667,1,0.6875,0.857143,0.823529,0.55,0.764706,0.818182,0.764706,0.533333,0.941176,0.928571,0.529412,0.733333,1,0.666667,0.705882,0.842105,0.833333,0.714286,0.294118,0.842105,0.777778,0.714286,0.785714,0.928571,0.611111,1,0.888889,0.875,0.9375,1,0.941176,0.933333,0.909091,0.8,0.764706,0.647059,0.647059,0.875,1,0.684211,0.823529,1,0.761905,0.833333,0.875,1,0.9375,0.789474,0.882353,0.8,0.7,0.882353] predictive_accuracy 0.80375 prior_entropy 6.6438561897747395 recall 0.80375 [0.5625,0.9375,0.9375,1,0.9375,0.6875,0.9375,1,0.875,0.9375,0.9375,1,0.9375,0.6875,1,0.875,1,0.5625,0.625,0.4375,0.8125,0.9375,0.75,1,0.875,0.25,0.25,0.8125,0.875,1,0.9375,0.8125,0.875,0.5,1,0.875,0.5,0.5,0.875,0.9375,0.9375,1,0.875,0.8125,0.9375,0.9375,0.8125,0.8125,0.6875,0.75,0.875,0.6875,0.8125,0.5625,0.8125,0.5,1,0.8125,0.5625,0.6875,1,1,0.75,1,0.9375,0.9375,0.3125,1,0.4375,0.625,0.6875,0.8125,0.6875,0.9375,0.5,0.875,0.9375,0.4375,1,0.875,0.625,0.75,0.8125,0.6875,0.6875,0.875,1,0.8125,0.875,1,1,0.9375,0.875,0.625,0.9375,0.9375,0.9375,0.75,0.4375,0.9375] relative_absolute_error 0.662511611178424 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.07183071307667165 root_relative_squared_error 0.7219258293908932 total_cost 0 area_under_roc_curve 0.9956106699307379 [0.993711,0.993671,1,1,1,1,1,1,1,1,0.996835,0.996835,1,0.993671,1,1,1,0.993671,0.974684,1,1,1,0.993711,1,1,0.993671,0.96519,1,1,1,0.996835,1,0.996835,0.987342,1,0.987421,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.971519,0.996835,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.977848,1,1,0.993711,0.984177,1,0.996835,1,1,1,1,0.993671,1,1,1,1,1,1,0.990506,0.987342,1,0.996835,0.996835,1,1,1,1,0.993671,1,0.990506,1,0.990506,0.917722,1] area_under_roc_curve 0.9954942381179842 [0.993711,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.977848,1,1,1,0.914557,0.996835,0.987421,1,1,1,1,1,0.96519,0.990506,1,1,1,1,1,1,1,1,0.993711,0.981132,1,1,1,1,1,1,0.987342,1,1,1,1,0.993671,1,1,0.974684,1,1,1,0.955975,1,1,1,0.962025,1,1,1,0.993671,1,1,0.996835,1,0.993711,1,1,1,1,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.977848,1,1,1,0.990506,0.990506,1] area_under_roc_curve 0.9971969289865455 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,0.958861,1,1,0.984177,1,1,0.990506,1,1,1,1,1,1,1,0.981013,1,1,0.993711,0.949686,1,1,0.996835,1,1,1,1,1,0.996835,1,0.990506,1,1,1,1,1,1,1,1,1,0.981132,0.987342,1,1,1,1,0.996835,0.996835,1,1,0.981132,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.996835,1,1,1,1,0.987342,1,1,1,0.993671,0.955975,1] area_under_roc_curve 0.9967615536183423 [0.996835,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.943396,0.990506,1,1,1,1,0.993711,0.987342,0.981013,1,1,1,1,1,1,0.987342,1,1,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974684,1,0.993671,1,1,1,1,1,1,0.996835,1,0.996835,1,0.943038,0.993671,1,1,0.990506,0.996835,0.987421,1,0.993711,1,1,1,1,1,0.993711,1,1,1,0.987421,1,1,1,1,1,1,1,0.987421,1,1,0.993711,0.987342,1,1,0.996835] area_under_roc_curve 0.9948605803678049 [0.996835,1,1,1,1,0.996835,1,0.996835,1,1,0.996835,1,1,0.993711,1,1,1,0.996835,1,0.987342,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.971519,1,1,1,0.971519,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.981013,1,0.984177,0.974684,1,0.996835,1,1,1,1,1,1,0.996835,1,0.968553,1,0.993671,0.993671,1,1,1,1,0.867089,1,1,0.984177,1,1,1,1,1,0.993671,0.987421,1,1,1,1,1,1,0.993671,1,1,0.984177,1,0.996835,1,1,1] area_under_roc_curve 0.99608684619059 [0.996835,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.905063,1,1,1,1,1,1,0.96519,1,1,1,1,1,0.993671,0.996835,1,0.993671,0.977848,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,0.993671,1,1,1,0.987421,1,1,0.996835,1,1,0.971519,1,1,0.993671,1,1,0.981013,1,1,1,1,1,0.993711,0.993671,1,0.993671,1,1,1,0.993711,1,1,1,1,0.993711,1,1,1,1,0.993711,0.962264,1] area_under_roc_curve 0.9962535327601302 [1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,0.974684,1,1,0.996835,1,0.996835,0.861635,0.981132,0.993671,1,1,1,1,1,0.974843,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.993671,0.996835,0.993711,1,0.993711,1,1,1,0.996835,0.984177,1,1,1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,0.937107,1,1,1,1,0.990506,1,1,1,1,1,0.936709,1] area_under_roc_curve 0.9963284173234616 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.993671,0.981013,1,1,1,1,1,1,0.993711,1,0.968553,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,0.993711,0.996835,0.990506,1,0.996835,0.939873,1,1,0.987342,0.987421,1,0.996835,1,1,1,0.962264,0.949686,1,0.977848,0.987342,1,1,1,1,0.993671,1,1,0.987421,1,1,0.987342,1,0.990506,1,1,0.993671,1,0.993711,1,1,1,1,1,0.993711,1,1,1,1,0.993671,1] area_under_roc_curve 0.9965279436350608 [0.993711,1,1,1,1,1,0.996835,1,1,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.962264,1,1,0.930818,0.981132,1,1,1,1,1,1,1,1,1,1,0.977848,1,1,1,1,0.996835,0.996835,1,1,1,1,0.993711,1,0.996835,0.993671,1,0.949367,1,0.968553,1,0.993711,1,0.990506,1,1,0.996835,1,1,1,0.993671,1,0.993671,0.993711,1,0.987421,0.987342,1,1,1,1,0.987421,1,1,0.996835,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.984177,1,1,0.993711,1,0.987342,1] area_under_roc_curve 0.996449078496935 [0.981132,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993711,0.993671,0.993711,1,1,1,1,1,0.880503,0.993711,1,0.993671,1,1,1,1,1,1,0.993671,0.993671,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.974684,1,1,1,1,1,0.996835,1,0.996835,0.974684,1,0.993711,1,0.993671,1,0.990506,0.993711,1,1,0.984177,1,0.977848,1,1,1,1,0.993671,1,0.981132,1,0.968553,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1] 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 0.7789008212255212 kappa 0.8420657796027955 kappa 0.8294243070362474 kappa 0.753632343651295 kappa 0.7978681405448086 kappa 0.7978202495656295 kappa 0.8293838862559242 kappa 0.7662204320183233 kappa 0.8041151613285416 kappa 0.8167528928557324 kb_relative_information_score 115.50698279444363 kb_relative_information_score 115.62219974189674 kb_relative_information_score 117.04461908874256 kb_relative_information_score 115.97884886263233 kb_relative_information_score 116.50106637826117 kb_relative_information_score 115.7293491157829 kb_relative_information_score 118.22828055921646 kb_relative_information_score 117.0137775538688 kb_relative_information_score 116.3788639694972 kb_relative_information_score 116.83318348811007 mean_absolute_error 0.013168044533344397 mean_absolute_error 0.013259377205221076 mean_absolute_error 0.013094189363406502 mean_absolute_error 0.013304238569448342 mean_absolute_error 0.013022758311286301 mean_absolute_error 0.013311342442842596 mean_absolute_error 0.012761927860398278 mean_absolute_error 0.012955203323754882 mean_absolute_error 0.01318596575640168 mean_absolute_error 0.013114251647224092 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 0.84375 predictive_accuracy 0.83125 predictive_accuracy 0.75625 predictive_accuracy 0.8 predictive_accuracy 0.8 predictive_accuracy 0.83125 predictive_accuracy 0.76875 predictive_accuracy 0.80625 predictive_accuracy 0.81875 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 [1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,0.5,0.5,1,1,1,0,0.5,1,1,1,0,1,0,0.5,0,1,1,1,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,0,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,1] recall 0.84375 [1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] recall 0.83125 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,0,1,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1] recall 0.75625 [0,1,1,1,0.5,0,1,1,0.5,1,1,1,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0,0,0,1,1,1,0.5,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,0,1,0,0.5,0,0.5,0,0.5,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,0.5] recall 0.8 [0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0,1,1,0.5,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,0.5,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,0,1,0.5,0.5,1,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1] recall 0.8 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,0.5,0,1,1,1,1,0.5,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,1,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1] recall 0.83125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0,0.5,1,1,0.5,0,1,0,1,1,0.5,1,1,1,1,1,1,1,0,0.5,0,1,1,1,1,1,1,0,1,0.5,1,0.5,0,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.76875 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,1,1,1,0,1,1,0,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,0,0.5,0.5,1,0.5,0.5,1,1,0.5,0,1,1,1,1,1,0,0,1,0,0.5,0,1,1,1,1,1,1,0,1,1,0,0,0,1,0,0.5,1,0,1,1,1,1,1,0,1,1,1,0,0.5,1] recall 0.80625 [0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,0.5,0.5,0.5,0,1,0,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,0,0.5,1,0.5,0.5,1,0,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,1] recall 0.81875 [0,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0,1,1,1,1,1,0,0,1,0.5,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0,1,1,0,1,0.5,1,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1] relative_absolute_error 0.6650527542093119 relative_absolute_error 0.6696655154152047 relative_absolute_error 0.6613226951215394 relative_absolute_error 0.6719312408812282 relative_absolute_error 0.6577150662265798 relative_absolute_error 0.6722900223657865 relative_absolute_error 0.6445418111312251 relative_absolute_error 0.6543031981694374 relative_absolute_error 0.6659578664849323 relative_absolute_error 0.6623359417789935 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 0.07232153722007122 root_mean_squared_error 0.07218092342472976 root_mean_squared_error 0.07142631720292085 root_mean_squared_error 0.07280365507191079 root_mean_squared_error 0.07145683353944757 root_mean_squared_error 0.07266145104062507 root_mean_squared_error 0.07022419005401966 root_mean_squared_error 0.07112561486302574 root_mean_squared_error 0.07204885571407584 root_mean_squared_error 0.0720197117656038 root_relative_squared_error 0.7268587976384812 root_relative_squared_error 0.7254455758218283 root_relative_squared_error 0.71786149793635 root_relative_squared_error 0.7317042643635843 root_relative_squared_error 0.7181681986582753 root_relative_squared_error 0.7302750600743398 root_relative_squared_error 0.7057796655023956 root_relative_squared_error 0.7148393257090349 root_relative_squared_error 0.7241182453880093 root_relative_squared_error 0.7238253376850057 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 2231.588297999224 usercpu_time_millis 2091.4554110004246 usercpu_time_millis 2090.8890470000188 usercpu_time_millis 2084.7172670000873 usercpu_time_millis 2343.160542000078 usercpu_time_millis 2098.012577999725 usercpu_time_millis 2384.6524769996904 usercpu_time_millis 2199.8222539996277 usercpu_time_millis 2088.6265330009337 usercpu_time_millis 2099.315477998971 usercpu_time_millis_testing 34.042399999634654 usercpu_time_millis_testing 33.84005800035084 usercpu_time_millis_testing 34.72604699982185 usercpu_time_millis_testing 34.84720100004779 usercpu_time_millis_testing 33.95912399992085 usercpu_time_millis_testing 33.82897200026491 usercpu_time_millis_testing 43.177094000384386 usercpu_time_millis_testing 33.996528999523434 usercpu_time_millis_testing 34.24534900022991 usercpu_time_millis_testing 33.547451999766054 usercpu_time_millis_training 2197.545897999589 usercpu_time_millis_training 2057.615353000074 usercpu_time_millis_training 2056.163000000197 usercpu_time_millis_training 2049.8700660000395 usercpu_time_millis_training 2309.201418000157 usercpu_time_millis_training 2064.1836059994603 usercpu_time_millis_training 2341.475382999306 usercpu_time_millis_training 2165.8257250001043 usercpu_time_millis_training 2054.381184000704 usercpu_time_millis_training 2065.768025999205