5876838 1 Jan van Rijn 9954 Supervised Classification 6952 sklearn.pipeline.Pipeline(imputation=openmlstudy14.preprocessing.ConditionalImputer,hotencoding=sklearn.preprocessing.data.OneHotEncoder,variencethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,classifier=sklearn.svm.classes.SVC)(1) 3911588 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 false 6948 threshold 0.0 6949 C 9294.65234652209 6953 cache_size 200 6953 class_weight null 6953 coef0 -0.8016618214933062 6953 decision_function_shape null 6953 degree 3 6953 gamma 3.6781084402190243 6953 kernel "sigmoid" 6953 max_iter -1 6953 probability true 6953 random_state 6117 6953 shrinking true 6953 tol 0.09442707828419063 6953 verbose false 6953 openml-pimp openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 12677490 description https://api.openml.org/data/download/12677490/description.xml -1 12677491 predictions https://api.openml.org/data/download/12677491/predictions.arff area_under_roc_curve 0.9964082228535354 [0.993924,1,1,1,0.999605,0.996607,0.999605,0.999961,0.997435,0.999803,0.998895,1,0.999882,0.985085,0.999724,1,1,0.993016,0.980035,0.976484,0.997396,0.999842,0.996252,1,0.995186,0.973603,0.984059,0.998698,0.999014,1,0.999842,1,0.999842,0.988005,0.999763,0.999171,0.992661,0.982994,0.999961,1,0.999527,0.999921,1,0.998777,0.999487,0.999645,0.999763,0.997475,0.998264,0.997948,0.996686,0.998816,0.999092,0.988557,0.997711,0.989741,1,0.999763,0.992188,0.994081,0.999014,0.999487,0.995699,0.998777,0.999408,0.993095,0.989031,0.999921,0.9942,0.995028,0.994989,0.998224,0.993884,0.999961,0.988242,0.999763,0.999882,0.991635,1,0.999645,0.992819,0.997277,0.999171,0.994397,0.998422,1,1,0.999605,0.999448,1,0.996765,0.999448,0.999092,0.99858,0.999487,0.997593,0.99929,0.998422,0.970249,0.99708] average_cost 0 f_measure 0.8239727198796882 [0.722222,0.9375,1,1,0.967742,0.8,0.969697,0.967742,0.875,0.857143,0.9375,0.941176,0.967742,0.758621,1,0.967742,1,0.533333,0.352941,0.277778,0.8125,0.967742,0.727273,1,0.8,0.322581,0.461538,0.8125,0.866667,0.896552,0.969697,1,0.875,0.451613,0.941176,0.827586,0.5,0.545455,0.933333,0.967742,0.810811,0.969697,0.933333,0.8125,0.967742,0.903226,0.9375,0.903226,0.722222,0.875,0.8,0.785714,0.909091,0.758621,0.787879,0.516129,1,0.969697,0.684211,0.742857,0.8,0.9375,0.777778,0.909091,0.903226,0.571429,0.451613,1,0.75,0.787879,0.709677,0.857143,0.5,0.967742,0.8,0.969697,0.933333,0.516129,1,0.9375,0.774194,0.903226,0.903226,0.592593,0.83871,0.933333,0.967742,0.875,0.9375,1,0.896552,0.866667,0.909091,0.909091,0.9375,0.857143,0.848485,0.823529,0.375,0.866667] kappa 0.8200757575757576 kb_relative_information_score 982.3701707081484 mean_absolute_error 0.016159950900628422 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 0.832251963502165 [0.65,0.9375,1,1,1,0.857143,0.941176,1,0.875,1,0.9375,0.888889,1,0.846154,1,1,1,0.571429,0.333333,0.25,0.8125,1,0.705882,1,0.736842,0.333333,0.391304,0.8125,0.928571,1,0.941176,1,0.875,0.466667,0.888889,0.923077,0.5,0.529412,1,1,0.714286,0.941176,1,0.8125,1,0.933333,0.9375,0.933333,0.65,0.875,0.736842,0.916667,0.882353,0.846154,0.764706,0.533333,1,0.941176,0.590909,0.684211,0.857143,0.9375,0.7,0.882353,0.933333,0.526316,0.466667,1,0.75,0.764706,0.733333,1,0.5,1,0.736842,0.941176,1,0.533333,1,0.9375,0.8,0.933333,0.933333,0.727273,0.866667,1,1,0.875,0.9375,1,1,0.928571,0.882353,0.882353,0.9375,0.789474,0.823529,0.777778,0.375,0.928571] predictive_accuracy 0.821875 prior_entropy 6.6438561897747395 recall 0.821875 [0.8125,0.9375,1,1,0.9375,0.75,1,0.9375,0.875,0.75,0.9375,1,0.9375,0.6875,1,0.9375,1,0.5,0.375,0.3125,0.8125,0.9375,0.75,1,0.875,0.3125,0.5625,0.8125,0.8125,0.8125,1,1,0.875,0.4375,1,0.75,0.5,0.5625,0.875,0.9375,0.9375,1,0.875,0.8125,0.9375,0.875,0.9375,0.875,0.8125,0.875,0.875,0.6875,0.9375,0.6875,0.8125,0.5,1,1,0.8125,0.8125,0.75,0.9375,0.875,0.9375,0.875,0.625,0.4375,1,0.75,0.8125,0.6875,0.75,0.5,0.9375,0.875,1,0.875,0.5,1,0.9375,0.75,0.875,0.875,0.5,0.8125,0.875,0.9375,0.875,0.9375,1,0.8125,0.8125,0.9375,0.9375,0.9375,0.9375,0.875,0.875,0.375,0.8125] relative_absolute_error 0.8161591363953733 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08293001858403524 root_relative_squared_error 0.8334780469710457 total_cost 0 area_under_roc_curve 0.9964831621686171 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[1,1,1,1,1,0.993671,0.993711,1,0.993671,1,1,1,1,1,1,1,1,0.993671,0.993711,0.962025,0.993711,1,0.987342,1,1,0.96519,0.996835,0.993711,1,1,1,1,1,0.987342,1,1,0.993711,0.968553,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.974843,0.984177,1,1,1,0.974843,0.996835,0.996835,0.996835,1,0.987421,0.996835,0.993671,0.996835,1,1,0.993711,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,1] area_under_roc_curve 0.9958169134623039 [1,1,1,1,1,0.993671,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.90566,0.974684,1,1,1,1,0.974843,0.987342,0.977848,1,1,1,1,1,1,0.987342,1,1,1,0.937107,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.984177,1,1,0.993711,0.993671,1,1,1,1,1,0.996835,0.974684,1,1,0.990506,0.981013,0.993671,0.987421,1,1,1,1,1,1,1,1,1,1,0.987342,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,0.955975,0.993671] area_under_roc_curve 0.9963264270360638 [1,1,1,1,1,1,1,1,1,1,0.993671,1,1,0.949686,1,1,1,0.993671,1,0.984177,1,1,1,1,0.993671,1,0.981013,1,1,1,1,1,1,0.974684,1,1,0.993671,0.977848,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.981132,1,1,0.990506,0.993671,1,0.996835,1,0.993711,1,1,1,1,1,1,0.987421,1,0.993671,1,1,1,1,1,0.939873,1,0.996835,0.984177,1,1,1,1,1,0.993671,0.987421,1,1,1,1,1,1,0.996835,1,1,0.993671,0.993711,1,1,0.987421,1] area_under_roc_curve 0.996718264867447 [0.958861,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.955696,1,1,1,1,0.993671,0.990506,0.993671,1,1,1,1,1,1,0.993671,1,1,0.993671,0.987342,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,0.981013,1,1,0.993671,1,0.993671,1,0.987421,1,1,0.990506,1,1,1,1,1,0.987342,1,1,0.984177,1,1,0.990506,1,1,0.993711,0.984177,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,0.993671] area_under_roc_curve 0.9958161671045298 [1,1,1,1,1,0.996835,1,1,0.993711,1,1,1,1,1,1,1,1,1,0.987342,0.968354,0.996835,1,1,1,1,0.886792,0.937107,0.993671,1,1,0.996835,1,1,0.987421,1,1,0.974684,0.990506,1,1,1,1,1,1,0.993711,1,1,0.996835,1,1,0.993711,1,1,1,0.993671,1,1,1,0.993671,1,1,1,1,0.996835,1,0.981132,1,1,0.996835,1,1,1,0.993671,1,1,1,1,0.993711,1,1,0.974684,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,0.996835,1,1,0.936709,1] area_under_roc_curve 0.9973959577262956 [1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.993711,0.974684,1,1,1,1,1,0.993671,0.974843,0.987421,1,0.993711,1,1,1,1,0.993711,1,1,0.987342,0.993671,1,1,1,1,1,1,1,1,1,0.984177,1,1,0.974843,1,1,1,1,0.981013,1,1,0.993671,1,1,1,0.993711,1,1,0.974843,0.949686,1,0.993671,1,1,1,0.996835,1,1,1,1,1,1,1,0.993671,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1] area_under_roc_curve 0.997078009314545 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,0.993711,1,1,1,1,1,1,0.993711,1,1,1,1,1,1,0.987421,1,1,0.996835,0.962025,1,1,1,1,1,0.990506,1,0.993671,1,1,0.993711,1,0.993671,1,1,0.936709,0.996835,0.949686,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.996835,1,1,0.987421,0.974684,1,1,1,1,0.993711,1,0.996835,1,0.993711,1,0.987421,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1] area_under_roc_curve 0.9979084567311519 [0.987421,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.981132,0.974684,0.968553,0.996835,1,1,1,1,1,0.993711,1,0.996835,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.990506,0.987342,1,1,0.984177,1,0.993711,0.993711,0.993671,1,1,0.993711,1,1,0.993671,1,1,1,1,1,1,1,1,0.981132,1,0.993711,1,1,1,1,1,1,0.993671,1,1,0.996835,1,1,0.993711,1,1,1] 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 0.8231625483539906 kappa 0.8357224657426056 kappa 0.8610123983258312 kappa 0.7789270064348032 kappa 0.7915103652517275 kappa 0.8104564839677776 kappa 0.8294108355709999 kappa 0.8231066887783305 kappa 0.8230997038499506 kappa 0.8231485867677246 kb_relative_information_score 98.97625983468761 kb_relative_information_score 97.08742698536517 kb_relative_information_score 98.7924361533756 kb_relative_information_score 97.247320978975 kb_relative_information_score 97.24197406350622 kb_relative_information_score 96.15907112481385 kb_relative_information_score 100.58219192672087 kb_relative_information_score 98.5980940024345 kb_relative_information_score 98.72182363262958 kb_relative_information_score 98.96357200564017 mean_absolute_error 0.016061107053866944 mean_absolute_error 0.016294138749693026 mean_absolute_error 0.016121521561805095 mean_absolute_error 0.016250965022324655 mean_absolute_error 0.016288667954255286 mean_absolute_error 0.016402953430089857 mean_absolute_error 0.01585066697262113 mean_absolute_error 0.016131349185400743 mean_absolute_error 0.016100095362381328 mean_absolute_error 0.016098043713846138 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 0.825 predictive_accuracy 0.8375 predictive_accuracy 0.8625 predictive_accuracy 0.78125 predictive_accuracy 0.79375 predictive_accuracy 0.8125 predictive_accuracy 0.83125 predictive_accuracy 0.825 predictive_accuracy 0.825 predictive_accuracy 0.825 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 0.825 [1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,0.5,1,1,1,1,1,1,0.5,0,1,1,0,1,1,0.5,0,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,1,1,0,1] recall 0.8375 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,1,1,1,0.5,0.5,1,0,1,0.5,1,0,1,1,1,0.5,1,1,0.5,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0] recall 0.8625 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,0.5,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,0,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1] recall 0.78125 [1,1,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0.5,0,0,1,1,1,1,0,0,0.5,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,0.5,0,0.5,0,0.5,1,1,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,0.5] recall 0.79375 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0,1,1,1,0.5,1,0.5,1,1,0,1,1,1,1,1,0,0.5,1,1,0.5,0,1,1,0,0.5,1,1,1,1,1,0,1,0,1,0.5,1,1,0,0,1,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1] recall 0.8125 [0.5,1,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0,1,0,1,0.5,1,1,1,0.5,1,0,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,1,0,0.5,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5] recall 0.83125 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0,1,1,1,1,1,0,0,0.5,1,1,1,1,1,0,1,1,0.5,0.5,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0,1,1,1,1,1,1,0,0,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.825 [0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0,0.5,1,0.5,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0,1,1,1,1,0.5,1,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0.5,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1] recall 0.825 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,0,1,1,1,1,1,1,0,1,1,1,0,0,1,1,1,0.5,0,1,1,1,1,0,1,1,0.5,1,0.5,0.5,0,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,1,0,1,0.5,1] recall 0.825 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,0,0,0,0.5,1,1,1,1,0,0,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1] relative_absolute_error 0.8111670229225716 relative_absolute_error 0.8229363004895454 relative_absolute_error 0.8142182606972257 relative_absolute_error 0.8207558092083146 relative_absolute_error 0.8226599976896596 relative_absolute_error 0.8284319914186783 relative_absolute_error 0.8005387359909648 relative_absolute_error 0.8147146053232684 relative_absolute_error 0.813136129413197 relative_absolute_error 0.8130325108003086 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 0.08249703924169421 root_mean_squared_error 0.08342208367011751 root_mean_squared_error 0.08268086532629883 root_mean_squared_error 0.08341715157428038 root_mean_squared_error 0.0835188684065196 root_mean_squared_error 0.08417026388483848 root_mean_squared_error 0.08154872396313936 root_mean_squared_error 0.08272955842919087 root_mean_squared_error 0.08263444632322385 root_mean_squared_error 0.0826526889381268 root_relative_squared_error 0.829126440848258 root_relative_squared_error 0.8384234871618605 root_relative_squared_error 0.8309739625128446 root_relative_squared_error 0.8383739177336124 root_relative_squared_error 0.8393962103621004 root_relative_squared_error 0.8459429812460937 root_relative_squared_error 0.8195955136909008 root_relative_squared_error 0.8314633466103328 root_relative_squared_error 0.8305074339784747 root_relative_squared_error 0.8306907791567414 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 1284.0182060000002 usercpu_time_millis 1283.5551219999993 usercpu_time_millis 1330.255961999999 usercpu_time_millis 1294.640844999999 usercpu_time_millis 1319.973847000001 usercpu_time_millis 1321.8789130000007 usercpu_time_millis 1342.8699599999998 usercpu_time_millis 1322.8587369999989 usercpu_time_millis 1298.2727880000002 usercpu_time_millis 1305.8072650000022 usercpu_time_millis_testing 170.34218200000018 usercpu_time_millis_testing 171.14495299999976 usercpu_time_millis_testing 177.54582699999943 usercpu_time_millis_testing 170.94691199999977 usercpu_time_millis_testing 179.19597500000071 usercpu_time_millis_testing 181.99763199999984 usercpu_time_millis_testing 181.96273700000097 usercpu_time_millis_testing 178.07514499999934 usercpu_time_millis_testing 173.8831130000005 usercpu_time_millis_testing 176.52197100000143 usercpu_time_millis_training 1113.676024 usercpu_time_millis_training 1112.4101689999995 usercpu_time_millis_training 1152.7101349999996 usercpu_time_millis_training 1123.6939329999993 usercpu_time_millis_training 1140.7778720000001 usercpu_time_millis_training 1139.8812810000009 usercpu_time_millis_training 1160.9072229999988 usercpu_time_millis_training 1144.7835919999995 usercpu_time_millis_training 1124.3896749999997 usercpu_time_millis_training 1129.2852940000007