8874380 710 William Raynaut 9956 Supervised Classification 7850 weka.kf.RandomSubspace(1) 6833437 mDataExp Modelage study_107 1493 one-hundred-plants-texture https://www.openml.org/data/download/1592285/phpoOxxNn -1 18664577 description https://api.openml.org/data/download/18664577/description1307968313743120906.xml -1 18664578 predictions https://api.openml.org/data/download/18664578/predictions3839405528245382734.arff area_under_roc_curve 0.980495 [0.986364,0.998658,0.879422,0.99542,0.932111,0.984661,0.994275,0.989656,0.992893,0.997789,0.987464,0.95732,0.999487,0.998658,0.971968,0.998934,0.978364,0.996841,0.99769,0.994196,0.998895,0.953056,0.998381,0.999605,0.872809,0.974021,0.995815,0.927175,1,0.999289,0.954536,0.999605,0.890813,0.999052,0.99013,0.953253,0.99692,0.997631,0.96873,0.999013,0.99617,0.960676,0.979154,0.934914,0.952503,0.996131,0.926228,0.995578,0.992183,0.992104,0.985925,0.955859,0.994275,0.982865,0.981522,0.998263,1,0.925537,0.998263,1,0.999921,0.998539,0.999368,0.998421,0.997434,0.995993,0.999408,0.957833,0.995815,0.91085,0.996407,0.979667,1,0.952207,0.994157,0.999131,0.975087,0.998816,0.994611,0.995144,0.991669,0.977554,0.966302,0.993051,0.988965,0.999368,0.916298,0.993367,0.999605,0.998184,0.958899,0.964901,0.993328,0.999842,0.961209,0.971277,0.996151,0.997256,0.995223,0.999566] average_cost 0 f_measure 0.656738 [0.518519,0.731707,0.454545,0.774194,0.296296,0.521739,0.645161,0.625,0.545455,0.764706,0.75,0.827586,0.842105,0.8125,0.518519,0.857143,0.37037,0.580645,0.909091,0.742857,0.909091,0.210526,0.810811,0.842105,0.222222,0.774194,0.814815,0.222222,0.914286,0.909091,0.866667,0.882353,0.166667,0.75,0.564103,0.166667,0.666667,0.810811,0.24,0.909091,0.8,0.6,0.26087,0.37037,0.32,0.685714,0.48,0.742857,0.564103,0.647059,0.666667,0.6,0.578947,0.727273,0.518519,0.756757,0.969697,0.6,0.702703,1,0.969697,0.75,0.882353,0.75,0.848485,0.722222,0.888889,0.482759,0.8,0.3,0.647059,0.4,0.941176,0.705882,0.833333,0.769231,0.538462,0.882353,0.875,0.538462,0.642857,0.606061,0.444444,0.594595,0.533333,0.848485,0.413793,0.585366,0.864865,0.685714,0.736842,0.117647,0.756757,0.933333,0.636364,0.307692,0.666667,0.848485,0.727273,0.857143] kappa 0.67846 kb_relative_information_score 1057.747801 mean_absolute_error 0.014055 mean_prior_absolute_error 0.0198 number_of_instances 1599 [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 0.687348 [0.583333,0.6,0.833333,0.8,0.363636,0.857143,0.666667,0.625,0.529412,0.722222,0.75,0.923077,0.727273,0.8125,0.636364,0.789474,0.454545,0.6,0.882353,0.684211,0.882353,0.666667,0.714286,0.727273,1,0.8,1,1,0.842105,0.882353,0.928571,0.833333,0.25,0.625,0.478261,0.25,0.565217,0.714286,0.333333,0.882353,0.857143,0.642857,0.428571,0.454545,0.444444,0.631579,0.666667,0.684211,0.478261,0.611111,0.647059,0.5,0.5,0.705882,0.636364,0.666667,0.941176,0.642857,0.619048,1,0.941176,0.625,0.833333,0.625,0.823529,0.65,0.8,0.538462,0.736842,0.75,0.611111,0.555556,0.888889,0.666667,0.75,0.652174,0.7,0.833333,0.875,0.7,0.75,0.588235,0.545455,0.52381,0.571429,0.823529,0.461538,0.48,0.761905,0.631579,0.636364,1,0.666667,1,0.5,0.4,0.538462,0.823529,0.705882,0.789474] predictive_accuracy 0.681676 prior_entropy 6.643831 recall 0.681676 [0.466667,0.9375,0.3125,0.75,0.25,0.375,0.625,0.625,0.5625,0.8125,0.75,0.75,1,0.8125,0.4375,0.9375,0.3125,0.5625,0.9375,0.8125,0.9375,0.125,0.9375,1,0.125,0.75,0.6875,0.125,1,0.9375,0.8125,0.9375,0.125,0.9375,0.6875,0.125,0.8125,0.9375,0.1875,0.9375,0.75,0.5625,0.1875,0.3125,0.25,0.75,0.375,0.8125,0.6875,0.6875,0.6875,0.75,0.6875,0.75,0.4375,0.875,1,0.5625,0.8125,1,1,0.9375,0.9375,0.9375,0.875,0.8125,1,0.4375,0.875,0.1875,0.6875,0.3125,1,0.75,0.9375,0.9375,0.4375,0.9375,0.875,0.4375,0.5625,0.625,0.375,0.6875,0.5,0.875,0.375,0.75,1,0.75,0.875,0.0625,0.875,0.875,0.875,0.25,0.875,0.875,0.75,0.9375] relative_absolute_error 0.709848 root_mean_prior_squared_error 0.099499 root_mean_squared_error 0.076848 root_relative_squared_error 0.772352 total_cost 0 area_under_roc_curve 0.970243 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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 f_measure 0.565625 [0,0.666667,0,1,0,0,0,0,0.8,1,1,0.666667,0.8,1,0,0.8,0.666667,0.666667,1,1,1,0,0.666667,1,0,1,1,0,1,0,1,1,0,0,0,0,0.5,0.5,0,1,0.8,0.666667,0.666667,0,0,0.333333,0,0,0.8,1,0.5,1,0,1,0,0.666667,1,0,0.666667,1,1,0.666667,1,0.666667,0.5,0.8,1,0.5,0.5,0,0.666667,0.5,1,1,0.8,0.8,0,1,1,0.666667,0,0.8,0,0.666667,0,1,0.5,0.5,1,0.8,0.5,0,0.5,1,0.333333,0,0.5,0.666667,0.666667,1] f_measure 0.629851 [1,0.666667,0,0.5,0,0,0.666667,0.666667,0.5,0.666667,1,0.8,1,0.5,0,1,0,0.5,1,0.666667,1,0,1,1,0,0,0,0,1,0.8,1,0.8,0,1,0,0,1,1,0,1,0.666667,1,0,0,1,1,0,0.333333,0.8,0.5,1,1,0.4,1,0.666667,1,1,0.666667,1,1,1,0.571429,1,1,1,0.5,1,0,0.666667,0,1,0.5,1,1,1,1,0.666667,0.5,1,0.666667,0.666667,0.8,0,0.666667,0.5,1,0.666667,0.333333,1,1,1,0,0.5,1,0.666667,0,0.4,1,1,1] f_measure 0.725833 [1,1,1,1,0.666667,1,1,1,1,1,0.8,1,0.666667,0.5,1,1,0.666667,0.666667,0.666667,1,1,0,0.8,1,1,1,1,0.666667,0.8,1,1,1,0,1,0.8,0,1,1,0,0.666667,0.666667,0.666667,0.666667,0,0,0.666667,0.666667,0.666667,0.5,0,0,0.4,0.666667,1,0.666667,1,1,0,1,1,1,1,0.666667,1,1,0.666667,1,0,0.666667,0.666667,0.8,0.666667,1,0.4,1,1,0.666667,1,1,0.666667,1,0.666667,0.666667,0.8,0,0.666667,0,1,0.666667,0,1,0,1,1,0.666667,0,1,1,0.666667,0.666667] f_measure 0.605238 [0.666667,0.285714,0,1,0,0,0,0.666667,0.666667,0.8,0,1,1,0.666667,0,1,0,0,0.666667,0.666667,1,0,1,1,0,0.8,1,0,1,1,1,1,0,1,1,0,0.5,0.8,0,1,0.8,0,0.666667,0.8,0,0.666667,0,0.666667,1,0.333333,1,0.571429,0.666667,0.5,0.666667,0.8,1,1,0.666667,1,1,0.666667,0.666667,1,1,1,0.571429,0,1,0.666667,0.5,0,1,1,0.5,0.4,0.666667,1,0.666667,0.666667,0.666667,0,1,0.8,0,1,0.5,0.666667,1,0.666667,0.666667,0,0.666667,0,0.666667,0.666667,0.8,1,1,0.5] f_measure 0.625268 [0.4,1,0,0.5,1,0.666667,0.666667,0,0,0,1,0,1,1,0,0.666667,0,0.666667,1,1,1,0,0.666667,0.666667,0,0.666667,1,1,0.8,1,1,1,1,1,0.666667,0,0.571429,1,1,0,1,0.8,0,0.666667,0.4,0,0,1,0,1,0,0.666667,0.8,0,0,1,1,0.5,0.666667,1,1,1,1,0.666667,1,0.666667,1,1,1,0.5,0.666667,1,1,0.5,0.8,0.5,0,0.666667,0.8,0,0,0.5,0,0.5,1,1,0.666667,0.5,0.8,0,0,0,1,0,0.5,0,0.8,1,0.666667,0.8] f_measure 0.650208 [0,0.666667,0.666667,1,0,1,1,0.666667,0.666667,1,1,1,1,1,0.5,1,0.333333,0.5,1,1,1,0,0.8,1,0,0.8,0.666667,0,1,1,0,1,0,0.8,0.5,0,0.666667,1,0,1,1,0.666667,0,0.4,0.5,0.8,1,1,0.5,1,0.8,0,0.4,1,0.666667,0.8,1,0.666667,0.666667,1,0.8,0.5,1,0.8,1,1,0.8,0,0.5,0,0,0,0.8,0.8,1,1,0.666667,0.666667,1,0.666667,0,0,0,0.5,0,1,0.666667,0,1,0.666667,1,0,0.666667,1,0.5,0.666667,0,1,0.666667,1] f_measure 0.651458 [0,1,0.666667,0,1,0,0.666667,0.8,0.5,0.4,0.666667,0.666667,0.666667,1,0,1,0.666667,1,1,0.666667,1,0,1,0.666667,0,1,1,0,1,1,1,0.8,0,0.8,0.5,0.666667,0.5,0.5,0,1,1,0.5,0,0,0,0.8,0.666667,0.8,0,0.8,1,0.666667,0.4,1,0.666667,0,1,0.666667,1,1,1,1,1,0.666667,0.666667,0.5,1,0.666667,0.666667,0,0.666667,0,1,0,1,1,0,1,0.666667,0.666667,1,1,0,0.666667,0.666667,1,0,0.666667,0.666667,0.666667,1,0,1,1,0,0,0.8,1,1,1] f_measure 0.642976 [0,0.8,0,1,0.4,0,0,0.5,0,0,0.666667,1,1,1,1,1,0,0.4,1,0.666667,0.5,0,0.666667,0.8,0,1,0.666667,0,0.666667,1,0,1,0,0.571429,1,0,1,0.666667,0.666667,1,1,0.666667,0,0,0.666667,1,0.666667,1,0.666667,0.8,0.666667,0,1,1,1,0.666667,1,0.666667,0.5,1,1,0.8,1,0.8,1,0.666667,1,0.666667,1,0,0.5,0,1,0.666667,1,1,1,1,0.8,0,1,1,0.5,0,0.333333,0,0,0.666667,1,0.666667,0.8,1,0.8,1,1,0,0.8,0.4,0,1] f_measure 0.565119 [0,1,1,0.666667,0,0.666667,0.8,0.666667,0,1,0.666667,1,0.8,0.666667,0.666667,0.4,1,1,1,0.8,0,0,1,0.8,0,0,0,0,1,0.666667,0.666667,0.5,0,0.4,0.333333,0,0.666667,1,0.666667,1,0,0.666667,0,1,0,1,0.666667,0.666667,0.5,0,0,0.4,0,0.4,0,0.666667,1,0.8,0.571429,1,1,1,0.666667,0.4,0.666667,1,1,0.5,1,0,0,0,0.666667,0.8,0.666667,0.571429,0.5,1,1,1,0,0.666667,0.5,0.666667,0.666667,0.4,0,0.666667,0.666667,1,0.666667,0,0.5,1,0.8,0.666667,1,0,0.5,0.666667] f_measure 0.689428 [0.666667,0.666667,0,0.666667,0,1,1,1,1,0.8,0,0.666667,0.8,1,1,1,0,0,1,0.4,1,1,0.666667,0.8,0.666667,0.666667,1,0,1,1,1,1,0,0.666667,1,0.666667,0.666667,1,0,0.8,0,0,0,0,0,0.666667,0,1,0.4,0.666667,0.8,0.666667,0.666667,0.5,0,0.5,0.8,0.666667,0.666667,1,1,0.666667,0.8,1,0.8,0.666667,1,0.666667,1,0,0.8,0.666667,1,1,0.666667,1,0.666667,1,1,0,1,0.4,1,0.333333,1,1,0.285714,0.8,1,0.5,0.8,0,1,1,1,0.666667,0.5,1,0.666667,1] kappa 0.627452 kappa 0.671678 kappa 0.753593 kappa 0.646395 kappa 0.671536 kappa 0.696754 kappa 0.696754 kappa 0.684044 kappa 0.621093 kappa 0.713914 kb_relative_information_score 103.138796 kb_relative_information_score 106.164219 kb_relative_information_score 106.045818 kb_relative_information_score 103.293018 kb_relative_information_score 106.831756 kb_relative_information_score 107.659661 kb_relative_information_score 105.578602 kb_relative_information_score 107.159128 kb_relative_information_score 105.492699 kb_relative_information_score 106.384104 mean_absolute_error 0.014135 mean_absolute_error 0.013967 mean_absolute_error 0.014211 mean_absolute_error 0.014303 mean_absolute_error 0.01398 mean_absolute_error 0.013757 mean_absolute_error 0.014102 mean_absolute_error 0.013855 mean_absolute_error 0.014295 mean_absolute_error 0.013944 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 0.552292 [0,0.5,0,1,0,0,0,0,0.666667,1,1,1,0.666667,1,0,0.666667,1,0.5,1,1,1,0,0.5,1,0,1,1,0,1,0,1,1,0,0,0,0,0.333333,0.333333,0,1,0.666667,0.5,0.5,0,0,0.2,0,0,0.666667,1,0.333333,1,0,1,0,1,1,0,0.5,1,1,0.5,1,0.5,0.5,0.666667,1,0.333333,0.333333,0,1,0.5,1,1,0.666667,0.666667,0,1,1,0.5,0,0.666667,0,1,0,1,0.5,0.5,1,0.666667,0.5,0,0.333333,1,0.25,0,0.333333,0.5,1,1] precision 0.632292 [1,0.5,0,0.5,0,0,1,0.5,0.5,1,1,0.666667,1,0.5,0,1,0,0.333333,1,1,1,0,1,1,0,0,0,0,1,0.666667,1,0.666667,0,1,0,0,1,1,0,1,1,1,0,0,1,1,0,0.2,0.666667,0.5,1,1,0.25,1,0.5,1,1,0.5,1,1,1,0.4,1,1,1,0.5,1,0,0.5,0,1,0.5,1,1,1,1,1,0.5,1,0.5,0.5,0.666667,0,1,0.333333,1,1,0.25,1,1,1,0,0.333333,1,0.5,0,0.25,1,1,1] precision 0.755729 [1,1,1,1,1,1,1,1,1,1,0.666667,1,0.5,0.5,1,1,1,1,0.5,1,1,0,0.666667,1,1,1,1,1,0.666667,1,1,1,0,1,0.666667,0,1,1,0,0.5,1,0.5,0.5,0,0,1,1,1,0.5,0,0,0.333333,0.5,1,0.5,1,1,0,1,1,1,1,0.5,1,1,1,1,0,0.5,1,0.666667,1,1,0.25,1,1,1,1,1,1,1,1,0.5,0.666667,0,1,0,1,0.5,0,1,0,1,1,0.5,0,1,1,0.5,0.5] precision 0.624792 [1,0.166667,0,1,0,0,0,1,0.5,0.666667,0,1,1,1,0,1,0,0,1,0.5,1,0,1,1,0,0.666667,1,0,1,1,1,1,0,1,1,0,0.333333,0.666667,0,1,0.666667,0,0.5,0.666667,0,1,0,1,1,0.2,1,0.4,1,0.5,1,0.666667,1,1,0.5,1,1,0.5,0.5,1,1,1,0.4,0,1,1,0.5,0,1,1,0.5,0.333333,1,1,1,1,0.5,0,1,0.666667,0,1,0.5,0.5,1,0.5,0.5,0,1,0,0.5,0.5,0.666667,1,1,0.333333] precision 0.624792 [0.333333,1,0,0.333333,1,0.5,0.5,0,0,0,1,0,1,1,0,0.5,0,1,1,1,1,0,1,0.5,0,1,1,1,0.666667,1,1,1,1,1,1,0,0.4,1,1,0,1,0.666667,0,1,0.333333,0,0,1,0,1,0,0.5,0.666667,0,0,1,1,0.5,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,0.5,1,1,0.5,0.666667,0.333333,0,0.5,0.666667,0,0,0.5,0,0.333333,1,1,1,0.333333,0.666667,0,0,0,1,0,0.333333,0,0.666667,1,1,0.666667] precision 0.654167 [0,0.5,1,1,0,1,1,1,0.5,1,1,1,1,1,0.333333,1,0.25,0.5,1,1,1,0,0.666667,1,0,0.666667,1,0,1,1,0,1,0,0.666667,0.5,0,0.5,1,0,1,1,1,0,0.333333,0.5,0.666667,1,1,0.333333,1,0.666667,0,0.333333,1,1,0.666667,1,1,0.5,1,0.666667,0.333333,1,0.666667,1,1,0.666667,0,0.5,0,0,0,0.666667,0.666667,1,1,0.5,0.5,1,1,0,0,0,0.333333,0,1,1,0,1,0.5,1,0,1,1,0.333333,0.5,0,1,1,1] precision 0.656771 [0,1,1,0,1,0,0.5,0.666667,0.5,0.25,0.5,1,0.5,1,0,1,0.5,1,1,0.5,1,0,1,0.5,0,1,1,0,1,1,1,0.666667,0,0.666667,0.5,1,0.5,0.5,0,1,1,0.5,0,0,0,0.666667,0.5,0.666667,0,0.666667,1,0.5,0.333333,1,1,0,1,1,1,1,1,1,1,0.5,0.5,0.333333,1,1,1,0,0.5,0,1,0,1,1,0,1,1,1,1,1,0,0.5,1,1,0,0.5,0.5,0.5,1,0,1,1,0,0,0.666667,1,1,1] precision 0.653437 [0,0.666667,0,1,0.25,0,0,0.5,0,0,1,1,1,1,1,1,0,0.333333,1,1,0.333333,0,0.5,0.666667,0,1,1,0,0.5,1,0,1,0,0.4,1,0,1,0.5,1,1,1,1,0,0,1,1,0.5,1,0.5,0.666667,0.5,0,1,1,1,0.5,1,1,0.5,1,1,0.666667,1,0.666667,1,0.5,1,1,1,0,0.333333,0,1,1,1,1,1,1,0.666667,0,1,1,0.5,0,0.25,0,0,1,1,1,0.666667,1,0.666667,1,1,0,0.666667,0.333333,0,1] precision 0.578437 [0,1,1,1,0,1,0.666667,0.5,0,1,0.5,1,0.666667,0.5,1,0.333333,1,1,1,0.666667,0,0,1,0.666667,0,0,0,0,1,0.5,1,0.5,0,0.25,0.2,0,1,1,1,1,0,1,0,1,0,1,0.5,0.5,0.5,0,0,0.25,0,0.333333,0,0.5,1,0.666667,0.4,1,1,1,1,0.333333,1,1,1,0.5,1,0,0,0,0.5,0.666667,0.5,0.4,0.5,1,1,1,0,0.5,0.5,1,1,0.25,0,0.5,0.5,1,0.5,0,0.333333,1,0.666667,1,1,0,0.333333,1] precision 0.72327 [0.5,1,0,1,0,1,1,1,1,0.666667,0,1,0.666667,1,1,1,0,0,1,0.333333,1,1,0.5,0.666667,1,0.5,1,0,1,1,1,1,0,0.5,1,1,1,1,0,0.666667,0,0,0,0,0,0.5,0,1,0.333333,1,0.666667,1,0.5,0.5,0,0.333333,0.666667,1,1,1,1,1,0.666667,1,0.666667,1,1,1,1,0,0.666667,1,1,1,0.5,1,1,1,1,0,1,0.333333,1,0.25,1,1,0.166667,0.666667,1,0.5,0.666667,0,1,1,1,1,0.333333,1,0.5,1] predictive_accuracy 0.63125 predictive_accuracy 0.675 predictive_accuracy 0.75625 predictive_accuracy 0.65 predictive_accuracy 0.675 predictive_accuracy 0.7 predictive_accuracy 0.7 predictive_accuracy 0.6875 predictive_accuracy 0.625 predictive_accuracy 0.716981 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 0.63125 [0,1,0,1,0,0,0,0,1,1,1,0.5,1,1,0,1,0.5,1,1,1,1,0,1,1,0,1,1,0,1,0,1,1,0,0,0,0,1,1,0,1,1,1,1,0,0,1,0,0,1,1,1,1,0,1,0,0.5,1,0,1,1,1,1,1,1,0.5,1,1,1,1,0,0.5,0.5,1,1,1,1,0,1,1,1,0,1,0,0.5,0,1,0.5,0.5,1,1,0.5,0,1,1,0.5,0,1,1,0.5,1] recall 0.675 [1,1,0,0.5,0,0,0.5,1,0.5,0.5,1,1,1,0.5,0,1,0,1,1,0.5,1,0,1,1,0,0,0,0,1,1,1,1,0,1,0,0,1,1,0,1,0.5,1,0,0,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,0,1,0.5,1,1,1,1,0.5,0.5,1,1,1,1,0,0.5,1,1,0.5,0.5,1,1,1,0,1,1,1,0,1,1,1,1] recall 0.75625 [1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,0.5,0.5,1,1,1,0,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0,1,1,0,1,0.5,1,1,0,0,0.5,0.5,0.5,0.5,0,0,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,0.5,1,0,1,0.5,1,0.5,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,0,0.5,0,1,1,0,1,0,1,1,1,0,1,1,1,1] recall 0.65 [0.5,1,0,1,0,0,0,0.5,1,1,0,1,1,0.5,0,1,0,0,0.5,1,1,0,1,1,0,1,1,0,1,1,1,1,0,1,1,0,1,1,0,1,1,0,1,1,0,0.5,0,0.5,1,1,1,1,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0.5,0.5,0,1,1,0.5,0.5,0.5,1,0.5,0.5,1,0,1,1,0,1,0.5,1,1,1,1,0,0.5,0,1,1,1,1,1,1] recall 0.675 [0.5,1,0,1,1,1,1,0,0,0,1,0,1,1,0,1,0,0.5,1,1,1,0,0.5,1,0,0.5,1,1,1,1,1,1,1,1,0.5,0,1,1,1,0,1,1,0,0.5,0.5,0,0,1,0,1,0,1,1,0,0,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0,1,1,0,0,0.5,0,1,1,1,0.5,1,1,0,0,0,1,0,1,0,1,1,0.5,1] recall 0.7 [0,1,0.5,1,0,1,1,0.5,1,1,1,1,1,1,1,1,0.5,0.5,1,1,1,0,1,1,0,1,0.5,0,1,1,0,1,0,1,0.5,0,1,1,0,1,1,0.5,0,0.5,0.5,1,1,1,1,1,1,0,0.5,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,0,0,0,1,1,1,1,1,1,1,0.5,0,0,0,1,0,1,0.5,0,1,1,1,0,0.5,1,1,1,0,1,0.5,1] recall 0.7 [0,1,0.5,0,1,0,1,1,0.5,1,1,0.5,1,1,0,1,1,1,1,1,1,0,1,1,0,1,1,0,1,1,1,1,0,1,0.5,0.5,0.5,0.5,0,1,1,0.5,0,0,0,1,1,1,0,1,1,1,0.5,1,0.5,0,1,0.5,1,1,1,1,1,1,1,1,1,0.5,0.5,0,1,0,1,0,1,1,0,1,0.5,0.5,1,1,0,1,0.5,1,0,1,1,1,1,0,1,1,0,0,1,1,1,1] recall 0.6875 [0,1,0,1,1,0,0,0.5,0,0,0.5,1,1,1,1,1,0,0.5,1,0.5,1,0,1,1,0,1,0.5,0,1,1,0,1,0,1,1,0,1,1,0.5,1,1,0.5,0,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,0.5,1,0,1,0,1,0.5,1,1,1,1,1,0,1,1,0.5,0,0.5,0,0,0.5,1,0.5,1,1,1,1,1,0,1,0.5,0,1] recall 0.625 [0,1,1,0.5,0,0.5,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,0,0,1,1,0,0,0,0,1,1,0.5,0.5,0,1,1,0,0.5,1,0.5,1,0,0.5,0,1,0,1,1,1,0.5,0,0,1,0,0.5,0,1,1,1,1,1,1,1,0.5,0.5,0.5,1,1,0.5,1,0,0,0,1,1,1,1,0.5,1,1,1,0,1,0.5,0.5,0.5,1,0,1,1,1,1,0,1,1,1,0.5,1,0,1,0.5] recall 0.716981 [1,0.5,0,0.5,0,1,1,1,1,1,0,0.5,1,1,1,1,0,0,1,0.5,1,1,1,1,0.5,1,1,0,1,1,1,1,0,1,1,0.5,0.5,1,0,1,0,0,0,0,0,1,0,1,0.5,0.5,1,0.5,1,0.5,0,1,1,0.5,0.5,1,1,0.5,1,1,1,0.5,1,0.5,1,0,1,0.5,1,1,1,1,0.5,1,1,0,1,0.5,1,0.5,1,1,1,1,1,0.5,1,0,1,1,1,0.5,1,1,1,1] relative_absolute_error 0.713897 relative_absolute_error 0.705408 relative_absolute_error 0.717749 relative_absolute_error 0.722364 relative_absolute_error 0.706047 relative_absolute_error 0.694784 relative_absolute_error 0.712241 relative_absolute_error 0.699747 relative_absolute_error 0.721958 relative_absolute_error 0.704253 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 0.077669 root_mean_squared_error 0.07662 root_mean_squared_error 0.076867 root_mean_squared_error 0.077849 root_mean_squared_error 0.0767 root_mean_squared_error 0.075601 root_mean_squared_error 0.0769 root_mean_squared_error 0.075761 root_mean_squared_error 0.078033 root_mean_squared_error 0.076439 root_relative_squared_error 0.780602 root_relative_squared_error 0.770054 root_relative_squared_error 0.772538 root_relative_squared_error 0.782414 root_relative_squared_error 0.770858 root_relative_squared_error 0.759824 root_relative_squared_error 0.772872 root_relative_squared_error 0.761426 root_relative_squared_error 0.784267 root_relative_squared_error 0.76824 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