1645381 1 Jan van Rijn 9956 Supervised Classification 1728 weka.HoeffdingTree(4) 2371 weka.classifiers.trees.HoeffdingTree -- -L 2 -S 1 -E 1.0E-7 -H 0.05 -M 0.01 -G 200.0 -N 0.0 L 2 1728 S 1 1728 E 1.0E-7 1728 H 0.05 1728 M 0.01 1728 G 200.0 1728 N 0.0 1728 weka weka_3.7.13 1493 one-hundred-plants-texture https://www.openml.org/data/download/1592285/phpoOxxNn -1 4184604 description https://api.openml.org/data/download/4184604/weka_generated_run4889418874362352620.xml -1 4184607 model_readable https://api.openml.org/data/download/4184607/WekaModel_weka.classifiers.trees.HoeffdingTree527245656563530559.model -1 4184606 model_serialized https://api.openml.org/data/download/4184606/WekaSerialized_weka.classifiers.trees.HoeffdingTree7427801242454564251.model -1 4184605 predictions https://api.openml.org/data/download/4184605/weka_generated_predictions8291989699127287214.arff area_under_roc_curve 0.926838 [0.898611,0.968691,0.87346,0.936592,0.902183,0.936592,0.873243,0.936039,0.873342,0.968454,0.967427,0.805946,0.905954,0.968316,0.873835,0.96873,0.872374,0.936829,1,0.905618,0.968395,0.77288,0.935763,0.937342,0.743387,0.905776,0.87421,0.681834,1,0.937066,0.935526,0.968434,0.870973,0.968079,0.967684,0.806913,0.936079,0.999645,0.873283,0.875,0.999961,0.903072,0.904533,0.902697,0.807604,0.968493,0.807328,0.874052,0.872868,0.96573,0.966776,0.968256,0.998934,0.935625,0.869058,0.96571,0.96873,0.901966,0.999961,0.9375,1,0.968651,0.937184,0.967783,0.999684,0.936671,0.968691,0.936552,0.968612,0.901394,0.968474,0.932802,0.96875,0.936552,0.906191,0.968651,0.967131,0.999645,0.968178,0.936079,0.936789,0.997394,0.777835,0.967743,0.873697,0.968474,0.869907,0.968158,1,0.967842,0.905816,0.84073,0.936888,0.937342,0.968632,0.905441,0.968079,0.96875,0.936908,0.998618] average_cost 0 f_measure 0.777584 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[15,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16,16] precision 0.788405 [0.6,0.882353,0.6875,0.823529,0.578947,0.857143,0.6875,0.722222,0.818182,0.875,0.777778,0.428571,0.928571,0.875,0.923077,1,0.5,0.823529,1,0.866667,0.9375,0.357143,0.666667,0.928571,0.4375,0.764706,0.923077,0.666667,1,0.875,0.666667,0.9375,0.625,0.833333,0.875,0.434783,0.777778,0.888889,0.846154,1,1,0.8,0.9,0.6,0.5,0.866667,0.5,0.8,0.705882,0.611111,0.6,0.833333,0.882353,0.625,0.4375,0.576923,1,0.5625,0.941176,1,1,0.9375,0.933333,0.8125,0.882353,0.823529,0.882353,0.923077,0.933333,0.5,0.882353,0.458333,1,0.7,1,0.833333,0.764706,0.941176,0.866667,0.8125,0.8125,0.666667,0.555556,0.866667,0.785714,0.823529,0.5,0.75,0.888889,0.777778,0.928571,0.7,0.875,1,0.9375,0.75,0.866667,1,0.866667,0.75] predictive_accuracy 0.779862 prior_entropy 6.643831 recall 0.779862 [0.8,0.9375,0.6875,0.875,0.6875,0.75,0.6875,0.8125,0.5625,0.875,0.875,0.375,0.8125,0.875,0.75,0.9375,0.625,0.875,0.9375,0.8125,0.9375,0.3125,0.875,0.8125,0.4375,0.8125,0.75,0.25,1,0.875,0.75,0.9375,0.625,0.9375,0.875,0.625,0.875,1,0.6875,0.75,0.9375,0.5,0.5625,0.5625,0.4375,0.8125,0.5625,0.75,0.75,0.6875,0.9375,0.9375,0.9375,0.625,0.4375,0.9375,0.9375,0.5625,1,0.875,1,0.9375,0.875,0.8125,0.9375,0.875,0.9375,0.75,0.875,0.25,0.9375,0.6875,0.9375,0.875,0.8125,0.9375,0.8125,1,0.8125,0.8125,0.8125,0.75,0.3125,0.8125,0.6875,0.875,0.5625,0.9375,1,0.875,0.8125,0.4375,0.875,0.8125,0.9375,0.75,0.8125,0.875,0.8125,0.9375] relative_absolute_error 0.224615 root_mean_prior_squared_error 0.099499 root_mean_squared_error 0.064848 root_relative_squared_error 0.65175 total_cost 0 area_under_roc_curve 0.936197 [1,1,0.496855,1,0.993671,0.993671,0.496835,1,1,1,0.996855,0.993671,1,1,1,1,1,1,1,1,0.5,0.745253,0.996835,0.75,0.487342,1,1,1,1,0.5,1,1,1,1,1,1,0.996855,1,1,0.5,1,1,0.993711,0.996835,1,1,0.748418,0.996855,0.746835,0.992089,1,1,1,1,0.974843,1,1,0.993711,1,1,1,1,1,0.993711,1,0.995253,1,1,1,1,1,0.993671,1,1,1,1,1,1,1,1,1,1,0.75,1,0.496855,1,0.748418,1,1,1,0.75,0.748418,1,1,0.75,0.748418,1,1,1,1] area_under_roc_curve 0.914025 [1,1,1,1,1,0.745253,1,1,0.496835,1,1,0.990506,1,1,1,1,0.75,0.996855,1,0.746835,1,0.746835,0.996835,1,0.740506,0.5,0.5,0.746835,1,1,1,1,1,0.996855,0.493711,0.490506,1,1,1,1,1,1,0.493711,1,1,1,0.490506,1,1,1,1,1,1,0.990506,1,0.996835,1,0.987421,1,1,1,0.75,1,1,1,0.748418,1,0.496855,1,0.746835,0.75,0.996835,1,1,1,1,0.996835,0.993671,1,1,0.996855,0.995253,0.493711,1,1,1,1,0.746835,1,1,0.75,0.743671,1,1,1,0.75,0.993711,1,1,1] area_under_roc_curve 0.932517 [1,1,1,1,0.990506,1,1,1,1,1,1,1,0.748418,0.75,1,1,0.996835,0.75,1,1,1,0.481013,0.746835,1,1,1,1,0.745253,1,1,1,1,1,0.75,1,0.993711,1,1,1,1,1,1,1,0.748418,0.481013,1,0.740506,0.75,0.743671,0.993711,1,0.748418,0.996835,1,0.981132,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.990506,1,0.737342,1,1,1,1,1,1,1,0.748418,1,1,1,0.995253,0.993711,1,0.738924,1,1,1,1,0.493671,1,1,1,1,1,1,1,0.996855] area_under_roc_curve 0.923914 [1,1,0.996835,1,1,0.5,0.75,1,1,1,1,0.493711,1,1,0.5,1,0.75,1,1,1,1,0.742089,1,1,0.490566,1,1,0.490506,1,1,1,1,0.738924,1,1,0.490566,0.996855,1,0.493711,1,1,1,0.490566,0.988924,0.75,1,1,0.75,1,0.993711,1,1,1,0.746835,0.75,0.996835,1,0.993711,1,1,1,1,1,1,1,1,1,1,1,1,1,0.748418,1,1,0.75,0.75,0.745253,1,1,1,1,1,1,1,1,0.75,1,1,1,1,1,1,0.75,1,1,1,0.996835,1,1,1] area_under_roc_curve 0.904749 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average_cost 0 f_measure 0.764583 [1,1,0,1,0.5,0.5,0,1,1,1,0.666667,0.8,0.8,1,1,1,0.666667,1,1,1,0,0,0.8,0.666667,0,1,1,1,1,0,0.8,1,0.666667,1,1,1,0.666667,1,1,0,1,1,0.666667,0.5,1,1,0.666667,0.666667,0.666667,0.4,0.666667,1,1,0.666667,0.333333,1,1,0.666667,1,1,1,1,1,0.666667,0.666667,0.8,1,1,0.666667,1,1,0.5,1,1,1,1,0.666667,1,1,0.666667,0.666667,0.8,0,0.666667,0,1,0,1,1,0.8,0.666667,0.666667,1,0.666667,0.666667,0.5,1,1,1,0.666667] f_measure 0.734643 [1,1,1,1,0.666667,0,1,0.666667,0,1,1,0.666667,1,1,1,1,0.666667,0.666667,1,0.5,1,0.5,0.571429,1,0,0,0,0.666667,1,1,1,1,1,0.5,0,0,1,0.666667,0.666667,1,1,1,0,1,0.5,1,0,1,1,0.8,0.666667,0.8,0.666667,0.4,1,0.8,1,0,1,1,1,0.666667,1,1,1,0.666667,1,0,1,0,0.666667,0.666667,1,0.666667,1,1,0.666667,0.8,1,0.666667,0.666667,0.8,0,0.666667,1,1,0.666667,0.5,1,1,0.666667,0.5,1,1,1,0.666667,0,1,0.8,1] f_measure 0.776667 [0.8,1,0.8,1,0.666667,1,1,1,1,1,1,1,0.666667,0.666667,1,1,0.666667,0.666667,1,1,1,0,0.5,1,1,1,1,0,1,1,0.666667,1,0.666667,0.666667,1,0.5,0.666667,1,1,1,1,1,1,0.666667,0,1,0.5,0.666667,0.5,0,1,0.666667,0.8,0.8,0,1,1,0,1,1,1,0.666667,0,1,1,1,1,0,1,0,1,0.25,1,1,1,0.8,1,1,1,0.666667,1,0.666667,1,0.8,0,0.666667,0,1,1,1,1,0,1,1,1,1,1,1,1,0.666667] f_measure 0.7575 [1,1,0.5,0.8,1,0,0,0.666667,1,0.8,1,0,1,0.666667,0,1,0.666667,0.8,0.666667,1,1,0.4,1,0,0,1,1,0,1,1,1,1,0.4,1,1,0,0.666667,1,0,1,1,1,0,0.333333,0.666667,0.666667,1,0.666667,0.666667,0.4,1,1,1,0.4,0.666667,0.8,1,0.666667,1,1,1,1,1,1,1,1,0.666667,1,1,0,1,0.5,1,0.666667,0.666667,0.666667,0.666667,1,1,1,1,0.666667,1,1,1,0.666667,0.8,0.4,1,1,1,1,0.666667,1,1,0.666667,0.5,1,0.666667,0.666667] f_measure 0.717113 [0.5,1,0.4,1,0.666667,1,1,0,0,0,0.666667,0,0,1,0,1,0.666667,1,1,1,1,0,1,0,0.666667,0.4,0.8,1,1,1,1,1,0.666667,1,1,1,1,1,0,0,1,0.8,0.666667,0,0.285714,0,0.666667,0.8,0.666667,0.666667,0.4,1,1,0,0,0.666667,1,0.666667,1,1,1,1,1,0.8,0.5,1,1,1,1,0.5,1,1,0.666667,0.666667,0.666667,1,0.666667,1,0.8,0.8,0.5,1,0,1,1,0.8,0.4,1,1,1,0,0.666667,1,0,1,0.666667,1,1,0,0.8] f_measure 0.802768 [0.666667,0.8,0.666667,1,0.666667,1,1,1,1,1,0.8,0,1,1,1,1,0.8,0.8,1,1,1,0,0.8,1,0,1,0.666667,0,1,1,0,1,0.8,1,0.666667,0.333333,1,1,1,1,1,0.666667,0.666667,1,0.666667,1,0.8,1,1,1,0.571429,1,0.666667,1,0.666667,0.666667,1,0.666667,1,1,1,1,0.666667,1,1,1,1,0.666667,0.666667,0.4,1,0.5,1,0.8,1,1,1,1,0.666667,1,0,0,0,1,0,0.8,1,1,0.8,0.666667,1,0.666667,0.666667,1,1,1,0.666667,1,1,1] f_measure 0.77625 [0.666667,1,1,0,1,0.8,0.666667,1,0.666667,1,0.666667,0.5,1,1,1,1,1,1,1,0.8,1,0,0,1,1,0.8,0.666667,0,1,0.5,0,1,0.666667,1,1,0.8,0.666667,1,1,1,1,0,1,1,0,1,0.5,0.5,1,1,1,0.666667,1,0,0.666667,0.5,1,0.5,0.8,1,1,1,1,0.5,1,0.666667,1,1,0.666667,0,1,0.666667,1,0,1,1,1,1,1,0.666667,1,0,0,0.666667,1,1,1,0.8,0.666667,0.5,0.8,0,1,1,1,0.5,1,1,0.666667,1] f_measure 0.769167 [0,1,0.5,1,0,1,0.5,1,0.5,0,0.666667,0,1,1,1,1,0,0.666667,1,0.666667,1,0,1,1,0,1,0.666667,0,1,1,0.666667,1,0,1,1,0.5,1,0.8,0.666667,1,1,0,0.666667,0.666667,1,1,0,1,1,1,0.8,0.666667,1,1,1,0.666667,1,0.4,1,0.666667,1,1,1,1,1,0.8,1,0.666667,1,0,0.5,0,1,0.8,1,1,1,1,0.5,0.666667,1,0.666667,0.5,1,0.5,1,0,1,1,1,1,1,0.8,1,1,1,0.8,0.666667,1,1] f_measure 0.70375 [0,0.8,1,1,0,1,0.666667,0.666667,0,1,1,0,1,0.666667,0.666667,0.666667,0,1,1,1,1,0.8,0.666667,0.8,0.666667,0.666667,1,0,1,0.666667,0.5,0.5,0.666667,0.666667,1,0.666667,0.5,1,1,0.666667,1,0.666667,0,0,0,0.666667,0.5,0,0.4,0.666667,0.8,1,1,0.666667,0,0.4,1,0.666667,1,1,1,1,1,0,1,1,1,0.8,1,0,1,0.666667,1,1,0,0.8,0.8,1,0,1,1,0.5,0,1,1,1,1,1,1,0.8,1,0,0.666667,1,0.8,0.666667,1,0,1,0.666667] f_measure 0.77065 [0.666667,0.666667,1,0.4,0.666667,1,0.666667,0.666667,1,0.8,1,0.333333,0.666667,0.666667,0.8,1,0,1,1,0.666667,0.666667,0.5,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0.5,1,1,0.666667,1,0,0,1,0,0,0.666667,0,1,0.666667,0,0.8,1,1,0.8,0,0.666667,0.666667,0.8,1,0.666667,1,1,0.8,1,1,0.666667,0,1,1,0,1,1,1,1,1,0.8,0.666667,1,1,1,1,0.8,1,0.666667,1,0.666667,0.333333,1,1,0.666667,1,0,1,0.666667,1,1,1,1,1,0.8] kappa 0.791576 kappa 0.759949 kappa 0.785176 kappa 0.766285 kappa 0.747215 kappa 0.816789 kappa 0.797836 kappa 0.778883 kappa 0.741008 kappa 0.790196 kb_relative_information_score 129.044176 kb_relative_information_score 122.006246 kb_relative_information_score 127.909551 kb_relative_information_score 125.109256 kb_relative_information_score 122.815898 kb_relative_information_score 132.129074 kb_relative_information_score 130.698855 kb_relative_information_score 126.209647 kb_relative_information_score 121.243253 kb_relative_information_score 128.136545 mean_absolute_error 0.004197 mean_absolute_error 0.004983 mean_absolute_error 0.004353 mean_absolute_error 0.004639 mean_absolute_error 0.004966 mean_absolute_error 0.003733 mean_absolute_error 0.003929 mean_absolute_error 0.004444 mean_absolute_error 0.005069 mean_absolute_error 0.004157 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 mean_prior_absolute_error 0.0198 number_of_instances 160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,1,2,2,2,2,1,1,2,2,1,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,2,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1] number_of_instances 160 [2,1,1,2,2,2,2,1,2,2,1,2,2,2,2,2,2,1,2,2,2,2,2,2,2,2,1,2,2,2,2,2,1,1,1,2,1,1,2,2,2,1,1,2,1,1,2,1,2,2,1,2,1,2,1,2,1,1,1,1,1,2,1,1,2,2,2,1,1,2,2,2,1,1,2,2,2,2,1,1,1,2,1,2,1,2,2,2,2,2,2,2,1,1,2,2,1,1,2,1] number_of_instances 160 [2,1,2,2,2,1,2,1,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,2,1,1,2,2,2,1,2,1,1,1,1,2,1,1,1,2,2,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,2,1,1,2,2,1] number_of_instances 160 [2,1,2,2,2,1,2,2,1,2,2,1,2,2,2,2,2,2,2,1,2,2,2,1,1,2,1,2,2,2,2,1,2,2,2,1,1,2,1,1,2,1,1,2,2,2,2,2,1,1,1,2,2,2,2,2,1,1,1,1,2,1,1,1,2,1,2,1,1,2,2,2,2,1,2,2,2,1,2,2,1,2,1,2,1,2,2,1,2,1,1,2,2,1,1,1,2,2,2,1] number_of_instances 160 [2,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,2,2,1,2,2,1,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,2,1,1,2,2,2,1,2,1,1,2,2,1,1,1,2,2,2,2] number_of_instances 160 [1,2,2,1,1,1,1,2,1,1,2,1,1,2,1,1,2,2,2,1,2,1,2,1,1,2,2,1,2,2,1,1,2,2,2,1,2,2,1,1,2,2,2,2,2,2,2,2,1,1,2,1,2,1,2,2,2,2,2,2,2,1,2,2,1,1,2,2,2,2,1,1,2,2,2,1,1,1,2,2,2,1,2,1,2,2,2,1,2,1,1,2,2,2,1,1,2,2,2,2] number_of_instances 160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,1,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,1,2,2,2,1,2] number_of_instances 160 [1,2,2,1,1,2,1,2,2,1,2,2,1,1,1,1,1,2,1,2,1,1,1,2,2,1,2,1,1,1,1,2,2,2,2,2,2,2,2,2,1,2,2,1,2,2,1,2,2,2,2,1,2,1,2,1,2,2,2,2,2,2,2,2,1,2,1,2,2,1,1,1,2,2,1,1,1,2,2,2,2,1,2,1,2,1,1,2,1,2,2,1,2,2,2,2,2,2,1,2] number_of_instances 160 [1,2,1,2,2,2,2,2,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,1,1,2,2,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,1,2,2,2,1,1,2,1,2,2,1,1,2,2,2,2,1,1,2] number_of_instances 159 [1,2,1,2,2,2,2,1,2,2,1,2,2,1,2,2,1,1,1,2,1,2,1,2,2,1,2,2,1,1,2,2,1,1,1,2,2,1,2,2,1,2,2,1,1,1,1,1,2,2,2,2,1,2,1,1,2,2,2,2,1,2,2,2,2,2,1,2,2,1,2,2,1,2,1,2,2,2,1,1,2,2,2,2,2,1,1,2,1,2,2,1,1,2,2,2,1,1,1,2] precision 0.7825 [1,1,0,1,0.5,0.5,0,1,1,1,0.5,0.666667,0.666667,1,1,1,0.5,1,1,1,0,0,0.666667,1,0,1,1,1,1,0,0.666667,1,0.5,1,1,1,0.5,1,1,0,1,1,0.5,0.5,1,1,1,0.5,1,0.333333,0.5,1,1,1,0.2,1,1,0.5,1,1,1,1,1,0.5,1,0.666667,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,0.5,0.5,0.666667,0,1,0,1,0,1,1,0.666667,1,1,1,1,1,0.5,1,1,1,0.5] precision 0.757083 [1,1,1,1,0.5,0,1,0.5,0,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,0.4,1,0,0,0,1,1,1,1,1,1,0.333333,0,0,1,0.5,1,1,1,1,0,1,0.333333,1,0,1,1,0.666667,0.5,0.666667,0.5,0.333333,1,0.666667,1,0,1,1,1,1,1,1,1,1,1,0,1,0,1,0.5,1,0.5,1,1,1,0.666667,1,0.5,0.5,0.666667,0,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,0,1,0.666667,1] precision 0.807292 [0.666667,1,0.666667,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,0,1,1,1,1,1,1,1,0.333333,0.5,1,1,1,1,1,1,1,0,1,0.5,1,0.5,0,1,1,0.666667,0.666667,0,1,1,0,1,1,1,0.5,0,1,1,1,1,0,1,0,1,0.166667,1,1,1,0.666667,1,1,1,1,1,1,1,0.666667,0,1,0,1,1,1,1,0,1,1,1,1,1,1,1,0.5] precision 0.797917 [1,1,0.5,0.666667,1,0,0,1,1,0.666667,1,0,1,1,0,1,1,0.666667,1,1,1,0.333333,1,0,0,1,1,0,1,1,1,1,0.333333,1,1,0,0.5,1,0,1,1,1,0,0.25,1,1,1,1,0.5,0.25,1,1,1,0.333333,1,0.666667,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,0.25,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5] precision 0.725417 [0.5,1,0.333333,1,0.5,1,1,0,0,0,0.5,0,0,1,0,1,1,1,1,1,1,0,1,0,0.5,0.333333,0.666667,1,1,1,1,1,1,1,1,1,1,1,0,0,1,0.666667,1,0,0.2,0,1,0.666667,0.5,0.5,0.333333,1,1,0,0,1,1,1,1,1,1,1,1,0.666667,0.333333,1,1,1,1,0.5,1,1,1,0.5,1,1,0.5,1,0.666667,0.666667,0.5,1,0,1,1,0.666667,0.333333,1,1,1,0,1,1,0,1,0.5,1,1,0,0.666667] precision 0.842708 [0.5,0.666667,1,1,0.5,1,1,1,1,1,0.666667,0,1,1,1,1,0.666667,0.666667,1,1,1,0,0.666667,1,0,1,1,0,1,1,0,1,0.666667,1,1,0.2,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,0.4,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.333333,1,0.333333,1,0.666667,1,1,1,1,1,1,0,0,0,1,0,0.666667,1,1,0.666667,0.5,1,1,1,1,1,1,1,1,1,1] precision 0.7875 [0.5,1,1,0,1,0.666667,0.5,1,1,1,1,0.5,1,1,1,1,1,1,1,0.666667,1,0,0,1,1,0.666667,1,0,1,0.5,0,1,1,1,1,0.666667,1,1,1,1,1,0,1,1,0,1,0.333333,0.5,1,1,1,0.5,1,0,1,0.333333,1,0.5,0.666667,1,1,1,1,0.5,1,0.5,1,1,1,0,1,0.5,1,0,1,1,1,1,1,1,1,0,0,0.5,1,1,1,0.666667,0.5,0.5,0.666667,0,1,1,1,0.5,1,1,0.5,1] precision 0.792708 [0,1,0.5,1,0,1,0.333333,1,0.5,0,1,0,1,1,1,1,0,1,1,1,1,0,1,1,0,1,1,0,1,1,0.5,1,0,1,1,0.5,1,0.666667,1,1,1,0,1,0.5,1,1,0,1,1,1,0.666667,0.5,1,1,1,0.5,1,0.333333,1,1,1,1,1,1,1,0.666667,1,1,1,0,0.333333,0,1,0.666667,1,1,1,1,0.5,1,1,0.5,0.5,1,0.5,1,0,1,1,1,1,1,0.666667,1,1,1,0.666667,1,1,1] precision 0.716146 [0,0.666667,1,1,0,1,0.5,0.5,0,1,1,0,1,0.5,1,1,0,1,1,1,1,0.666667,0.5,0.666667,0.5,0.5,1,0,1,0.5,0.5,0.5,0.5,0.5,1,1,0.5,1,1,1,1,1,0,0,0,0.5,0.333333,0,0.333333,1,0.666667,1,1,1,0,0.25,1,1,1,1,1,1,1,0,1,1,1,0.666667,1,0,1,1,1,1,0,0.666667,0.666667,1,0,1,1,0.333333,0,1,1,1,1,1,1,0.666667,1,0,0.5,1,0.666667,1,1,0,1,1] precision 0.797904 [0.5,1,1,0.333333,1,1,1,0.5,1,0.666667,1,0.25,1,0.5,0.666667,1,0,1,1,1,0.5,0.5,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0.5,1,1,1,1,0,0,1,0,0,0.5,0,1,1,0,0.666667,1,1,0.666667,0,0.5,1,0.666667,1,1,1,1,0.666667,1,1,1,0,1,1,0,1,1,1,1,1,0.666667,0.5,1,1,1,1,0.666667,1,1,1,0.5,0.2,1,1,1,1,0,1,1,1,1,1,1,1,0.666667] predictive_accuracy 0.79375 predictive_accuracy 0.7625 predictive_accuracy 0.7875 predictive_accuracy 0.76875 predictive_accuracy 0.75 predictive_accuracy 0.81875 predictive_accuracy 0.8 predictive_accuracy 0.78125 predictive_accuracy 0.74375 predictive_accuracy 0.792453 prior_entropy 6.643831 prior_entropy 6.643831 prior_entropy 6.643831 prior_entropy 6.643831 prior_entropy 6.643831 prior_entropy 6.643831 prior_entropy 6.643831 prior_entropy 6.643831 prior_entropy 6.643831 prior_entropy 6.643831 recall 0.79375 [1,1,0,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,1,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,1,0.5,1,0.5,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,0.5,1,1,1,1,1,0,0.5,0,1,0,1,1,1,0.5,0.5,1,0.5,0.5,0.5,1,1,1,1] recall 0.7625 [1,1,1,1,1,0,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0,0,0,0.5,1,1,1,1,1,1,0,0,1,1,0.5,1,1,1,0,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0,1,1,1,0.5,1,1,1,0.5,1,0,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0.5,0.5,1,1,0.5,0.5,1,1,1,0.5,0,1,1,1] recall 0.7875 [1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,1,0,0.5,1,1,1,1,0,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0.5,0,1,0.5,0.5,0.5,0,1,0.5,1,1,0,1,1,0,1,1,1,1,0,1,1,1,1,0,1,0,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0,0.5,0,1,1,1,1,0,1,1,1,1,1,1,1,1] recall 0.76875 [1,1,0.5,1,1,0,0,0.5,1,1,1,0,1,0.5,0,1,0.5,1,0.5,1,1,0.5,1,0,0,1,1,0,1,1,1,1,0.5,1,1,0,1,1,0,1,1,1,0,0.5,0.5,0.5,1,0.5,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,0.5,0.5,0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,0.5,1] recall 0.75 [0.5,1,0.5,1,1,1,1,0,0,0,1,0,0,1,0,1,0.5,1,1,1,1,0,1,0,1,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,0,0,1,1,0.5,0,0.5,0,0.5,1,1,1,0.5,1,1,0,0,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,0,0.5,1,0,1,1,1,1,0,1] recall 0.81875 [1,1,0.5,1,1,1,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,0,1,1,0,1,0.5,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.5,1,1,1,1,0.5,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,0,0,0,1,0,1,1,1,1,1,1,0.5,0.5,1,1,1,0.5,1,1,1] recall 0.8 [1,1,1,0,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,1,1,1,0,0,1,1,1,0.5,0,1,0.5,0,1,0.5,1,1,1,0.5,1,1,1,1,0,1,1,0,1,1,0.5,1,1,1,1,1,0,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,0.5,0,1,1,1,0,1,1,1,1,1,0.5,1,0,0,1,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1] recall 0.78125 [0,1,0.5,1,0,1,1,1,0.5,0,0.5,0,1,1,1,1,0,0.5,1,0.5,1,0,1,1,0,1,0.5,0,1,1,1,1,0,1,1,0.5,1,1,0.5,1,1,0,0.5,1,1,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,1,1,1,1,0.5,1,0,1,0,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,0.5,1,0,1,1,1,1,1,1,1,1,1,1,0.5,1,1] recall 0.74375 [0,1,1,1,0,1,1,1,0,1,1,0,1,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,0.5,1,1,1,0.5,0.5,1,1,0.5,1,0.5,0,0,0,1,1,0,0.5,0.5,1,1,1,0.5,0,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,0,1,0.5,1,1,0,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,0,1,1,1,0.5,1,0,1,0.5] recall 0.792453 [1,0.5,1,0.5,0.5,1,0.5,1,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,1,0.5,1,1,0.5,1,1,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0,0,1,0,0,1,0,1,0.5,0,1,1,1,1,0,1,0.5,1,1,0.5,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,0,1,0.5,1,1,1,1,1,1] relative_absolute_error 0.211986 relative_absolute_error 0.251661 relative_absolute_error 0.219869 relative_absolute_error 0.234297 relative_absolute_error 0.250814 relative_absolute_error 0.188559 relative_absolute_error 0.19844 relative_absolute_error 0.224445 relative_absolute_error 0.256012 relative_absolute_error 0.209971 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_prior_squared_error 0.099499 root_mean_squared_error 0.063239 root_mean_squared_error 0.068927 root_mean_squared_error 0.063633 root_mean_squared_error 0.065972 root_mean_squared_error 0.068957 root_mean_squared_error 0.059004 root_mean_squared_error 0.061023 root_mean_squared_error 0.064627 root_mean_squared_error 0.069231 root_mean_squared_error 0.063008 root_relative_squared_error 0.635576 root_relative_squared_error 0.692738 root_relative_squared_error 0.639535 root_relative_squared_error 0.663042 root_relative_squared_error 0.693045 root_relative_squared_error 0.593018 root_relative_squared_error 0.613307 root_relative_squared_error 0.649529 root_relative_squared_error 0.695797 root_relative_squared_error 0.633253 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