8968833 1 Jan van Rijn 9954 Supervised Classification 8330 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)(2) 6904415 axis 0 7104 categorical_features [] 7104 copy true 7104 fill_empty 0 7104 missing_values "NaN" 7104 strategy "most_frequent" 7104 strategy_nominal "most_frequent" 7104 verbose 0 7104 categorical_features [] 7105 dtype {"oml-python:serialized_object": "type", "value": "np.float64"} 7105 handle_unknown "ignore" 7105 n_values "auto" 7105 sparse true 7105 threshold 0.0 7106 C 2064.6597352124663 7107 cache_size 200 7107 class_weight null 7107 coef0 0.13712099561045776 7107 decision_function_shape null 7107 degree 3 7107 gamma 1.026296001662096 7107 kernel "poly" 7107 max_iter -1 7107 probability true 7107 random_state 57348 7107 shrinking true 7107 tol 1.804601743526651e-05 7107 verbose false 7107 copy true 7713 with_mean false 7713 with_std true 7713 openml-pimp openml-python Sklearn_0.18.1. 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18852826 description https://api.openml.org/data/download/18852826/description.xml -1 18852827 predictions https://api.openml.org/data/download/18852827/predictions.arff area_under_roc_curve 0.9962389520202025 [0.987413,0.999763,1,0.999724,0.998422,0.994081,0.999724,0.999014,0.99712,0.999961,0.998658,0.999645,0.99929,0.96733,0.998974,0.999487,1,0.992622,0.983586,0.974432,0.997711,0.999684,0.995541,0.999171,0.998816,0.977667,0.990767,0.999053,0.99858,1,0.999605,0.999961,1,0.989978,0.998422,0.998698,0.993332,0.982521,0.999408,0.999842,0.998698,0.997948,1,0.998777,0.996528,0.999961,0.999092,0.999053,0.997475,0.996883,0.994831,0.998974,0.99783,0.99274,0.996173,0.993332,1,0.999329,0.992819,0.996646,0.998067,0.999408,0.997593,0.999171,0.999171,0.993647,0.986387,0.999882,0.998027,0.994081,0.996725,0.998974,0.99566,0.997948,0.989544,0.999448,0.999369,0.991122,0.999724,1,0.995936,0.997001,0.99783,0.995818,0.999369,0.999961,0.999961,0.999171,0.99925,0.999921,0.999408,0.999605,0.998224,0.997672,0.995936,0.997277,0.997869,0.998422,0.969736,0.996488] average_cost 0 f_measure 0.8193982344247973 [0.666667,0.9375,0.967742,0.896552,0.896552,0.8,0.969697,0.8,0.83871,0.896552,0.848485,0.866667,0.909091,0.714286,0.896552,0.903226,0.967742,0.625,0.424242,0.266667,0.774194,0.9375,0.787879,0.909091,0.848485,0.411765,0.588235,0.848485,0.903226,0.967742,0.9375,0.967742,0.9375,0.5625,0.875,0.875,0.5625,0.533333,0.903226,0.896552,0.742857,0.875,0.967742,0.848485,0.866667,0.941176,0.866667,0.875,0.742857,0.8125,0.848485,0.8,0.83871,0.727273,0.733333,0.588235,0.967742,0.9375,0.666667,0.764706,0.896552,0.857143,0.709677,0.903226,0.909091,0.666667,0.484848,0.9375,0.83871,0.727273,0.787879,0.896552,0.75,0.933333,0.731707,0.9375,0.903226,0.685714,1,1,0.8125,0.8125,0.83871,0.533333,0.875,0.933333,0.967742,0.827586,0.903226,0.967742,0.848485,0.903226,0.866667,0.875,0.909091,0.8,0.8,0.83871,0.606061,0.933333] kappa 0.8150252525252526 kb_relative_information_score 979.6439792290299 mean_absolute_error 0.015943433906295233 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 0.8281434270475191 [0.6,0.9375,1,1,1,0.857143,0.941176,0.736842,0.866667,1,0.823529,0.928571,0.882353,0.833333,1,0.933333,1,0.625,0.411765,0.285714,0.8,0.9375,0.764706,0.882353,0.823529,0.388889,0.555556,0.823529,0.933333,1,0.9375,1,0.9375,0.5625,0.875,0.875,0.5625,0.571429,0.933333,1,0.684211,0.875,1,0.823529,0.928571,0.888889,0.928571,0.875,0.684211,0.8125,0.823529,0.857143,0.866667,0.705882,0.785714,0.555556,1,0.9375,0.565217,0.722222,1,0.789474,0.733333,0.933333,0.882353,0.565217,0.470588,0.9375,0.866667,0.705882,0.764706,1,0.75,1,0.6,0.9375,0.933333,0.631579,1,1,0.8125,0.8125,0.866667,0.571429,0.875,1,1,0.923077,0.933333,1,0.823529,0.933333,0.928571,0.875,0.882353,0.736842,0.736842,0.866667,0.588235,1] predictive_accuracy 0.816875 prior_entropy 6.6438561897747395 recall 0.816875 [0.75,0.9375,0.9375,0.8125,0.8125,0.75,1,0.875,0.8125,0.8125,0.875,0.8125,0.9375,0.625,0.8125,0.875,0.9375,0.625,0.4375,0.25,0.75,0.9375,0.8125,0.9375,0.875,0.4375,0.625,0.875,0.875,0.9375,0.9375,0.9375,0.9375,0.5625,0.875,0.875,0.5625,0.5,0.875,0.8125,0.8125,0.875,0.9375,0.875,0.8125,1,0.8125,0.875,0.8125,0.8125,0.875,0.75,0.8125,0.75,0.6875,0.625,0.9375,0.9375,0.8125,0.8125,0.8125,0.9375,0.6875,0.875,0.9375,0.8125,0.5,0.9375,0.8125,0.75,0.8125,0.8125,0.75,0.875,0.9375,0.9375,0.875,0.75,1,1,0.8125,0.8125,0.8125,0.5,0.875,0.875,0.9375,0.75,0.875,0.9375,0.875,0.875,0.8125,0.875,0.9375,0.875,0.875,0.8125,0.625,0.875] relative_absolute_error 0.8052239346613739 root_mean_prior_squared_error 0.09949874371066209 root_mean_squared_error 0.08228214781023498 root_relative_squared_error 0.8269667007003405 total_cost 0 area_under_roc_curve 0.9966023306265426 [1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.987342,1,0.981132,1,0.996835,0.981013,0.987421,0.993711,1,1,1,1,0.996835,0.993671,1,1,1,1,1,1,0.993671,1,0.993711,1,0.993711,1,1,0.993711,0.993711,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.996835,0.971519,1,1,0.962264,0.996835,1,1,1,1,1,1,0.949367,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987342,0.993711,0.993671,0.949367,1] area_under_roc_curve 0.9961271495103894 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.889241,1,1,1,0.984177,0.993671,0.987421,1,0.993711,0.993711,1,1,0.977848,0.971519,1,1,1,1,1,1,0.996835,0.993671,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1,1,0.993711,1,1,1,0.993671,1,1,1,1,1,0.996835,1,1,0.987421,1,0.996835,1,1,1,1,1,1,0.993671,1,1,0.974684,0.996835,1,0.981132,0.996835,1,1,1,1,1,1,1,1,0.990506,1,1,1,0.996835,1,0.974843] area_under_roc_curve 0.9968777366451713 [1,1,1,1,1,1,0.993711,1,0.996835,1,1,1,1,1,0.996835,1,1,0.996835,1,0.943038,1,1,0.993671,1,1,0.943038,0.996835,0.993711,1,1,1,1,1,0.984177,1,1,1,1,0.993711,1,0.996835,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,0.981132,0.981013,1,1,1,0.987421,1,0.993671,1,1,1,0.987342,1,0.996835,1,1,1,1,1,0.996835,0.993671,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,1] area_under_roc_curve 0.9971969289865455 [1,1,1,1,1,0.993671,1,1,0.996835,1,1,1,1,1,1,1,1,0.990506,0.943396,0.977848,1,1,0.996835,1,0.993711,0.996835,1,1,1,1,1,1,1,0.987342,1,1,1,0.962264,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.981013,1,0.987342,1,1,0.993711,1,1,1,1,1,0.996835,0.996835,1,1,1,0.977848,0.981013,1,0.987421,1,0.993711,1,1,0.996835,1,1,1,1,0.993671,0.996835,1,1,1,1,1,1,1,1,0.987421,1,1,1,0.996835,1,1,1] area_under_roc_curve 0.9962099952233101 [1,1,1,1,1,1,1,1,1,1,0.987342,1,1,0.918239,1,1,1,0.993671,0.987421,0.990506,1,1,1,1,1,0.990506,0.987342,1,1,1,1,1,1,0.981013,1,1,0.996835,0.987342,1,1,1,1,1,1,1,1,1,1,1,1,1,0.987421,1,1,0.993671,0.987342,1,0.996835,1,1,1,1,0.993711,1,1,1,1,1,0.996835,1,1,1,1,1,0.949367,1,0.993671,1,1,1,1,1,1,0.990506,0.993711,1,1,1,1,1,1,1,1,1,0.968354,0.993711,1,1,0.937107,1] area_under_roc_curve 0.9960848559031925 [0.920886,1,1,1,1,0.981013,1,1,1,1,1,1,1,1,1,1,1,1,1,0.962025,1,1,1,1,0.993671,0.987342,0.981013,1,1,1,1,1,1,0.993671,1,0.993671,0.996835,0.984177,0.996835,1,0.993671,1,1,0.993711,1,1,1,1,0.990506,0.993671,1,1,0.993671,1,1,0.987342,1,1,0.993671,1,0.996835,0.996835,0.993711,1,1,0.981013,1,1,0.996835,1,1,1,1,1,0.996835,1,1,1,1,1,0.993711,0.993671,1,0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.993711,0.996835] area_under_roc_curve 0.9955783277605287 [1,1,1,1,1,1,1,1,0.993711,1,1,1,0.996835,1,1,1,1,1,0.996835,0.971519,0.996835,1,0.993671,1,1,0.880503,0.962264,0.993671,1,1,0.996835,1,1,1,1,1,0.974684,0.993671,1,1,1,1,1,1,0.968553,1,1,1,1,1,1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,0.981132,1,1,1,1,1,1,0.996835,1,1,1,1,0.993711,1,1,0.977848,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,0.879747,1] area_under_roc_curve 0.9972006607754158 [1,1,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.993711,1,1,0.993711,0.993671,0.990506,0.996835,1,1,1,0.993671,0.993711,1,1,0.974843,1,1,1,1,1,1,1,0.981013,0.990506,1,1,1,1,1,1,0.993711,1,1,0.993671,1,0.987421,0.962264,1,0.996835,1,1,0.990506,1,1,0.996835,1,1,1,1,1,1,0.974843,0.924528,1,1,0.996835,1,1,1,1,1,1,1,0.968553,1,1,0.993671,1,0.993671,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,0.993671] area_under_roc_curve 0.9969988953904941 [1,1,1,1,1,1,1,1,0.990506,1,1,1,1,1,1,1,1,0.993711,0.993671,0.974843,0.996835,1,0.974843,0.993671,1,0.981132,0.993711,1,1,1,1,1,1,0.993711,1,1,1,0.952532,1,1,1,1,1,0.996835,1,1,1,0.993671,0.993711,1,0.993671,1,1,0.974684,0.981013,0.993711,1,1,1,1,0.996835,1,1,1,1,1,0.984177,1,1,1,1,0.993711,0.984177,1,1,1,1,0.993711,1,1,1,0.993711,1,0.987421,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1] area_under_roc_curve 0.9975905083194017 [0.993711,1,1,1,1,1,1,1,1,1,1,1,1,0.993671,1,1,1,1,0.96519,0.987421,0.996835,1,1,1,1,1,1,0.993671,1,1,1,1,1,0.993711,1,0.993671,0.996835,1,1,1,1,0.996835,1,1,0.996835,1,1,1,0.993711,1,1,1,1,0.996835,1,1,1,1,0.981013,1,1,1,0.993671,0.996835,0.993711,0.993711,0.987342,1,1,1,1,1,0.990506,0.990506,1,0.990506,1,1,1,1,1,0.968553,1,0.993711,1,0.996835,1,0.996835,1,1,0.996835,1,1,1,1,1,0.993711,1,1,1] 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 0.7852518553608084 kappa 0.7915021323645554 kappa 0.8547062539481995 kappa 0.7978521794061908 kappa 0.8230857323381905 kappa 0.8231066887783305 kappa 0.8041615667074664 kappa 0.8420345944238212 kappa 0.8231206569804169 kappa 0.8042079501046067 kb_relative_information_score 98.9559328297693 kb_relative_information_score 96.30699413018965 kb_relative_information_score 97.35356652191038 kb_relative_information_score 96.35430283964143 kb_relative_information_score 97.43631593917519 kb_relative_information_score 97.42120871601551 kb_relative_information_score 101.29129089365824 kb_relative_information_score 97.98447088150635 kb_relative_information_score 98.32406328718017 kb_relative_information_score 98.21583318998202 mean_absolute_error 0.01576705134589903 mean_absolute_error 0.016183986384904803 mean_absolute_error 0.016099635317663914 mean_absolute_error 0.016211040191425094 mean_absolute_error 0.01610725435574494 mean_absolute_error 0.015953120605310865 mean_absolute_error 0.015456333643631252 mean_absolute_error 0.015996728940292866 mean_absolute_error 0.015842202808220813 mean_absolute_error 0.01581698546985883 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 0.7875 predictive_accuracy 0.79375 predictive_accuracy 0.85625 predictive_accuracy 0.8 predictive_accuracy 0.825 predictive_accuracy 0.825 predictive_accuracy 0.80625 predictive_accuracy 0.84375 predictive_accuracy 0.825 predictive_accuracy 0.80625 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 0.7875 [1,0.5,1,0,0.5,1,1,1,0.5,1,0.5,0.5,1,0,1,0,1,0.5,0,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0,0.5,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,0.5,0,1,0.5,1] recall 0.79375 [1,1,1,0,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,0.5,0,0,0,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,0.5,1,0,1,1,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,0,0.5,1,0,0.5,1,1,1,0.5,1,0.5,1,0.5,1,1,1,1,1,0.5,0] recall 0.85625 [1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,0.5,1,1,0.5,1,0,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,0,0,1,1,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1] recall 0.8 [0,1,1,0,0.5,0.5,1,1,0.5,0.5,1,0.5,1,0.5,1,1,1,0.5,0,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,0,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,1,1,0,1,1,1,0.5,0.5,1,1] recall 0.825 [1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0.5,1,1,0.5,0,1,1,1,1,1,0,1,1,1,1,1,1,1,0,0.5,1,0.5,0.5,1,1,1,1,1,1,0,1,1,1,0,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,0,1] recall 0.825 [0.5,1,0.5,1,1,0.5,1,0.5,1,0.5,1,1,0,1,1,1,1,1,1,0,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1] recall 0.80625 [1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,0,0,0.5,1,1,0.5,0,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,1,1,0.5,0.5,0.5,1,0,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.84375 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0.5,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,1,0.5,1,0,0,0.5,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0.5,0.5,0.5,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0.5] recall 0.825 [1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,0,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,1,1,0,0,0,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,1,0.5,0,0,1,0,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1] recall 0.80625 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0,1,0,1,1,1,1,1,1,0,0,1,0.5,1,1,1,1,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,1,1] relative_absolute_error 0.7963157245403536 relative_absolute_error 0.8173730497426654 relative_absolute_error 0.8131128948315094 relative_absolute_error 0.8187394036073266 relative_absolute_error 0.8134976947345914 relative_absolute_error 0.8057131618843857 relative_absolute_error 0.7806229112945064 relative_absolute_error 0.8079156030450929 relative_absolute_error 0.8001112529404437 relative_absolute_error 0.798837649992869 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 0.08157966943284205 root_mean_squared_error 0.08318915193956225 root_mean_squared_error 0.08272397613873658 root_mean_squared_error 0.08337319296035962 root_mean_squared_error 0.08279796333940447 root_mean_squared_error 0.08246687978226984 root_mean_squared_error 0.08026243040452657 root_mean_squared_error 0.08224025472941747 root_mean_squared_error 0.082037692637619 root_mean_squared_error 0.08210667600730498 root_relative_squared_error 0.8199065273635224 root_relative_squared_error 0.8360824351860424 root_relative_squared_error 0.8314072424803095 root_relative_squared_error 0.8379321170406451 root_relative_squared_error 0.8321508418254734 root_relative_squared_error 0.8288233268761651 root_relative_squared_error 0.8066677770115989 root_relative_squared_error 0.8265456593961477 root_relative_squared_error 0.8245098337741927 root_relative_squared_error 0.8252031427257771 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 1913.6134200016386 usercpu_time_millis 1742.0758549997117 usercpu_time_millis 1748.2255610011634 usercpu_time_millis 1750.9059780022653 usercpu_time_millis 1836.8458800032386 usercpu_time_millis 1767.7727059999597 usercpu_time_millis 1780.0892509985715 usercpu_time_millis 1755.1949750013591 usercpu_time_millis 1772.1086769997783 usercpu_time_millis 1759.183017999021 usercpu_time_millis_testing 151.42110899978434 usercpu_time_millis_testing 148.54686499893432 usercpu_time_millis_testing 149.8296140016464 usercpu_time_millis_testing 149.98141800242593 usercpu_time_millis_testing 148.42105800198624 usercpu_time_millis_testing 153.04166300120414 usercpu_time_millis_testing 149.19494499918073 usercpu_time_millis_testing 148.75772500090534 usercpu_time_millis_testing 148.21269800086156 usercpu_time_millis_testing 147.62075799808372 usercpu_time_millis_training 1762.1923110018543 usercpu_time_millis_training 1593.5289900007774 usercpu_time_millis_training 1598.395946999517 usercpu_time_millis_training 1600.9245599998394 usercpu_time_millis_training 1688.4248220012523 usercpu_time_millis_training 1614.7310429987556 usercpu_time_millis_training 1630.8943059993908 usercpu_time_millis_training 1606.4372500004538 usercpu_time_millis_training 1623.8959789989167 usercpu_time_millis_training 1611.5622600009374