8881248 710 William Raynaut 9954 Supervised Classification 7838 weka.kf.AdaBoostM1-NaiveBayes(1) 6833449 mDataExp Modelage study_107 1491 one-hundred-plants-margin https://www.openml.org/data/download/1592283/phpCsX3fx -1 18678249 description https://api.openml.org/data/download/18678249/description1832523914178486956.xml -1 18678250 predictions https://api.openml.org/data/download/18678250/predictions1682286995213231081.arff area_under_roc_curve 0.939527 [0.900351,0.934659,1,1,0.96733,0.966166,0.96733,0.999684,0.934304,0.965002,0.966619,1,0.96733,0.901712,1,0.999625,0.995857,0.733112,0.834774,0.793403,0.93462,1,0.901298,0.96733,0.966639,0.832505,0.927123,0.999684,0.96733,0.934659,1,0.999684,0.96733,0.8285,1,0.932726,0.930812,0.863518,0.934343,0.934659,0.899582,0.999684,0.96733,0.999369,0.999684,1,0.934028,0.96656,0.998737,0.90191,0.932528,0.835701,0.934028,0.932548,0.868608,0.896149,1,0.934659,0.89826,0.866221,0.999369,0.999369,0.803464,0.967014,0.998836,0.963995,0.896445,0.96733,0.9666,0.835069,0.96729,0.900805,0.934028,1,0.900253,0.966698,0.965002,0.802557,0.999369,0.934265,0.933041,0.93168,0.934699,0.866576,0.933988,0.932884,0.999684,0.901989,0.966383,0.999684,0.932884,0.96733,0.901357,0.901042,0.964134,0.967014,0.933396,0.966698,0.834852,1] average_cost 0 f_measure 0.845245 [0.742857,0.933333,1,1,0.967742,0.903226,0.967742,0.969697,0.875,0.933333,0.909091,1,0.967742,0.758621,1,0.941176,0.933333,0.357143,0.571429,0.285714,0.933333,1,0.8125,0.967742,0.909091,0.466667,0.484848,0.969697,0.967742,0.933333,1,0.969697,0.967742,0.466667,0.969697,0.787879,0.6875,0.551724,0.903226,0.933333,0.685714,0.941176,0.967742,0.941176,0.969697,1,0.875,0.882353,0.888889,0.83871,0.83871,0.733333,0.875,0.685714,0.8,0.5,1,0.933333,0.709677,0.62069,0.914286,0.941176,0.666667,0.9375,0.9375,0.848485,0.666667,0.967742,0.875,0.666667,0.933333,0.8125,0.83871,1,0.764706,0.909091,0.933333,0.6,0.941176,0.848485,0.777778,0.8125,0.896552,0.647059,0.875,0.866667,0.969697,0.896552,0.857143,0.969697,0.896552,0.967742,0.83871,0.8125,0.848485,0.9375,0.823529,0.909091,0.5625,0.969697] kappa 0.842803 kb_relative_information_score 1356.430771 mean_absolute_error 0.003118 mean_prior_absolute_error 0.0198 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 0.851409 [0.684211,1,1,1,1,0.933333,1,0.941176,0.875,1,0.882353,1,1,0.846154,1,0.888889,1,0.416667,0.526316,0.230769,1,1,0.8125,1,0.882353,0.5,0.470588,0.941176,1,1,1,0.941176,1,0.5,0.941176,0.764706,0.6875,0.615385,0.933333,1,0.631579,0.888889,1,0.888889,0.941176,1,0.875,0.833333,0.8,0.866667,0.866667,0.785714,0.875,0.631579,0.857143,0.5,1,1,0.733333,0.692308,0.842105,0.888889,0.818182,0.9375,0.9375,0.823529,0.647059,1,0.875,0.647059,1,0.8125,0.866667,1,0.722222,0.882353,1,0.642857,0.888889,0.823529,0.7,0.8125,1,0.611111,0.875,0.928571,0.941176,1,0.789474,0.941176,1,1,0.866667,0.8125,0.823529,0.9375,0.777778,0.882353,0.5625,0.941176] predictive_accuracy 0.844375 prior_entropy 6.643856 recall 0.844375 [0.8125,0.875,1,1,0.9375,0.875,0.9375,1,0.875,0.875,0.9375,1,0.9375,0.6875,1,1,0.875,0.3125,0.625,0.375,0.875,1,0.8125,0.9375,0.9375,0.4375,0.5,1,0.9375,0.875,1,1,0.9375,0.4375,1,0.8125,0.6875,0.5,0.875,0.875,0.75,1,0.9375,1,1,1,0.875,0.9375,1,0.8125,0.8125,0.6875,0.875,0.75,0.75,0.5,1,0.875,0.6875,0.5625,1,1,0.5625,0.9375,0.9375,0.875,0.6875,0.9375,0.875,0.6875,0.875,0.8125,0.8125,1,0.8125,0.9375,0.875,0.5625,1,0.875,0.875,0.8125,0.8125,0.6875,0.875,0.8125,1,0.8125,0.9375,1,0.8125,0.9375,0.8125,0.8125,0.875,0.9375,0.875,0.9375,0.5625,1] relative_absolute_error 0.157491 root_mean_prior_squared_error 0.099499 root_mean_squared_error 0.055362 root_relative_squared_error 0.556407 total_cost 0 area_under_roc_curve 0.909952 [0.996855,0.745253,1,1,1,0.996855,1,0.996855,0.745253,1,0.745253,1,1,0.490506,1,1,1,0.487342,0.740506,0.996855,1,1,1,1,0.490566,0.993671,0.992089,0.996835,0.745253,1,1,1,1,0.490506,1,0.996855,1,0.993711,1,1,0.996855,1,0.745253,0.996835,1,1,1,0.993711,1,0.745253,1,0.745253,0.996855,0.996835,0.487421,1,1,1,1,1,1,1,0.490506,1,1,1,1,1,1,0.487421,1,0.996855,1,1,0.487421,1,0.745253,0.490506,1,1,1,1,1,0.996855,1,0.745253,1,1,0.745253,1,0.745253,1,1,0.996835,1,1,0.490566,0.993671,0.740506,1] area_under_roc_curve 0.945552 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.746835,1,1,1,0.746835,0.746835,0.484277,1,1,1,1,1,0.743671,0.995253,1,1,1,1,1,1,1,1,0.996855,0.993711,1,1,0.746835,1,1,1,1,1,1,1,1,0.996835,0.746835,1,0.746835,1,0.996835,1,0.490566,1,0.493711,1,0.743671,1,0.996855,0.746835,0.996835,1,0.996835,1,1,0.996855,0.996855,1,1,1,1,1,0.996835,0.996835,1,1,0.996835,0.746835,1,1,0.493711,0.743671,1,1,1,1,1,0.746835,1,1,1,1,1,1,1,0.746835,1] area_under_roc_curve 0.945649 [0.731013,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.468354,1,0.716772,1,1,0.468354,1,1,0.96519,1,1,1,1,1,1,0.734177,0.72943,1,1,0.996855,1,1,1,0.731013,1,1,1,0.996835,1,0.734177,1,1,1,1,1,1,1,1,0.734177,1,1,0.996855,0.734177,1,1,1,1,1,0.996835,1,1,1,0.734177,1,1,1,1,0.993711,1,1,0.995253,0.996835,0.732595,0.996855,1,1,0.732595,1,1,1,1,0.996835,1,1,1,1,0.734177,1,1,0.996835,1,1,1] area_under_roc_curve 0.950897 [0.996835,1,1,1,0.72943,1,1,1,1,1,1,1,1,1,1,0.996835,1,0.971519,0.459119,0.955696,1,1,1,1,1,1,1,1,1,1,1,1,1,0.727848,1,1,1,1,1,0.72943,1,1,1,1,1,1,1,1,0.996835,1,1,1,1,0.726266,1,0.996835,1,1,1,1,0.993711,1,0.998418,1,1,0.969937,0.724684,1,1,0.72943,0.72943,0.727848,0.459119,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.72943,1,1,1,1,0.459119,0.996835,0.996835,0.459119,0.72943,1,0.993711,1] area_under_roc_curve 0.928568 [1,0.471698,1,1,1,1,1,1,0.471698,0.977848,0.996835,1,1,1,1,1,0.982595,0.734177,1,0.726266,0.471519,1,1,1,1,1,0.727848,1,1,0.735759,1,1,1,0.708861,1,1,1,0.707278,1,1,0.993671,1,1,1,1,1,1,1,1,1,0.471698,0.468553,0.735759,0.996855,0.996835,0.984177,1,1,1,1,1,1,1,1,1,1,0.468553,1,1,0.732595,1,1,1,1,0.735759,1,1,0.735759,1,1,0.996855,1,0.737342,0.996835,0.996855,1,1,1,1,0.996835,1,0.735759,0.996855,0.471698,0.735759,1,0.996835,1,0.993711,1] area_under_roc_curve 0.947671 [0.727848,1,1,1,1,0.731013,1,1,1,0.731013,0.996835,1,0.462264,1,1,1,0.966772,1,1,0.981013,1,1,1,1,0.996835,0.732595,0.987342,1,1,1,1,0.996855,1,0.996835,1,0.985759,0.996835,0.702532,1,1,0.996835,0.996835,1,0.996855,1,1,1,1,1,0.731013,1,0.996855,0.727848,1,0.731013,1,1,1,1,1,1,1,1,1,1,1,1,0.731013,1,0.731013,1,0.996835,1,1,1,1,1,0.731013,1,1,1,0.72943,1,1,1,1,1,1,0.996835,1,1,1,0.462264,1,1,0.996855,0.996835,0.462264,0.993711,1] area_under_roc_curve 0.936315 [0.996835,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.490566,0.745253,0.740506,1,1,0.993671,1,0.996835,0.493711,0.496855,1,1,1,1,1,1,0.993711,1,1,0.743671,0.746835,0.996835,1,0.745253,1,1,1,1,1,0.748418,1,1,1,1,0.996835,1,1,0.748418,0.745253,1,1,0.496835,0.996855,1,1,1,0.748418,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,0.493711,1,1,0.748418,1,1,0.993671,1,1,1,0.496855,1,1,1,1,0.748418,1,0.996855,1,1,1,0.496835,1] area_under_roc_curve 0.955528 [0.735759,1,1,1,1,1,0.735759,1,1,1,1,1,1,1,1,1,1,0.465409,0.993671,0.996835,1,1,1,1,1,0.471698,1,1,1,0.735759,1,1,1,0.955975,1,1,0.737342,1,1,1,1,1,1,1,1,1,1,1,0.996855,1,0.468553,0.735759,1,1,1,0.984177,1,1,0.996835,0.471698,1,0.996835,1,1,1,0.468553,0.946541,1,1,0.996835,1,1,1,1,0.996835,1,1,1,1,1,0.993671,1,0.735759,0.735759,0.735759,0.981013,1,1,1,1,1,1,0.996835,0.996855,1,1,1,1,1,1] area_under_roc_curve 0.940022 [1,1,1,1,1,1,1,1,0.996835,1,1,1,1,1,1,1,1,0.993711,1,0.481132,1,1,0.477987,0.740506,1,1,0.993711,1,1,1,1,1,1,0.981132,1,1,1,0.738924,0.740506,1,0.481132,1,1,1,1,1,0.996855,1,0.996855,1,0.988924,0.740506,1,0.737342,0.740506,0.987421,1,1,0.737342,0.740506,1,1,0.481013,1,1,1,1,1,0.996835,0.996855,1,0.477987,0.993671,1,1,1,1,0.474843,0.996855,0.738924,0.996835,0.965409,1,0.993711,1,1,1,0.740506,0.996855,1,0.987342,1,1,1,0.959119,1,1,1,0.738924,1] area_under_roc_curve 0.937014 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.737342,0.468553,1,1,1,1,1,0.474843,0.968553,1,1,1,1,1,1,0.990566,1,0.477848,1,1,0.738924,1,1,1,1,1,1,1,0.996855,0.738924,1,1,1,1,1,1,1,1,1,0.477987,0.984177,0.992089,1,1,0.738924,1,0.996855,1,0.738924,1,0.737342,1,1,0.477987,0.735759,1,0.732595,0.738924,1,1,1,1,1,0.474843,1,0.471698,1,0.735759,0.996835,1,1,1,1,1,1,0.738924,1,1,0.996855,1,1,1] 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 f_measure 0.776875 [0.666667,0.666667,1,1,1,0.666667,1,0.666667,0.666667,1,0.666667,1,1,0,1,1,1,0,0.5,0.666667,1,1,1,1,0,0.5,0.5,0.8,0.666667,1,1,1,1,0,1,0.666667,1,0.666667,1,1,0.666667,1,0.666667,0.8,1,1,1,0.5,1,0.666667,1,0.666667,0.666667,0.8,0,0,1,1,0,0.8,1,1,0,1,1,1,0.666667,1,1,0,1,0.666667,1,1,0,1,0.666667,0,1,1,1,1,1,0.666667,1,0.666667,1,1,0.666667,1,0.666667,1,1,0.8,1,1,0,0.666667,0.4,1] f_measure 0.869583 [0.666667,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,0.666667,0.666667,0,1,1,1,1,1,0.5,0.8,1,1,1,1,1,1,1,1,0.666667,0,1,1,0.666667,1,1,1,1,1,1,1,1,0.8,0.666667,0.8,0.666667,1,0.666667,1,0,1,0,1,0.5,1,0.666667,0.666667,0.8,1,0.8,1,1,0.666667,0.666667,1,1,1,1,1,0.8,0.666667,0.8,1,0.8,0.666667,1,1,0,0.5,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,0.666667,1] f_measure 0.850208 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,0,0,0.333333,1,1,0,1,1,0.5,0.666667,1,1,1,1,1,0.666667,0.5,1,1,0.666667,0,1,1,0.5,0.666667,1,1,0.8,1,0.666667,1,1,1,1,1,1,0.8,1,0.666667,1,1,0.666667,0.666667,1,1,1,1,1,0.8,1,1,0,0.666667,1,1,1,1,0.666667,1,1,0.8,0.8,0.666667,0.666667,1,1,0.666667,1,1,1,1,0.8,1,1,1,1,0.666667,1,1,0.8,1,0.5,1] f_measure 0.862083 [0.8,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,0.8,1,0.5,0,0.4,1,1,1,1,1,0.666667,0.8,1,1,1,1,1,1,0.666667,1,0.666667,1,0.666667,1,0.666667,0.8,1,1,1,1,1,1,1,0.8,0.8,1,1,1,0.5,1,0.666667,1,1,1,0.666667,0.4,1,0.8,1,1,0.666667,0.5,1,1,0.666667,0.666667,0.666667,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,0,0.8,0.8,0,0.666667,1,0,1] f_measure 0.778839 [1,0,1,1,1,1,1,1,0,0.666667,0.8,1,1,0.5,1,1,0.666667,0,0.4,0.285714,0,1,1,1,1,0,0,1,1,0.666667,1,1,1,0,1,1,0.666667,0,1,1,0.666667,1,1,1,1,1,1,1,1,1,0,0,0.666667,0.666667,0.8,0.571429,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,0.666667,1,1,0.666667,1,1,0.666667,0.8,0,0.666667,0.666667,1,1,1,1,0.8,1,0.666667,0.666667,0,0.666667,1,0.8,1,0.666667,1] f_measure 0.834167 [0.5,1,1,1,1,0.666667,1,1,1,0.666667,0.8,1,0,1,1,1,0.666667,1,1,0,1,1,1,1,0.8,0.666667,0.333333,1,1,1,1,0.666667,1,0.5,1,0.666667,0.666667,0,1,1,0.8,0.8,1,0.666667,1,1,1,1,1,0.666667,1,0.666667,0.5,1,0.666667,0.8,1,1,1,1,1,1,1,1,1,1,0.666667,0.666667,1,0.666667,0.666667,0.8,1,1,1,1,1,0.666667,1,1,1,0.666667,1,1,1,1,1,1,0.666667,1,1,1,0,1,1,0.666667,0.8,0,0,0.8] f_measure 0.8375 [0.8,1,1,1,1,0.666667,1,1,0.666667,1,1,1,1,1,1,1,1,0,0.4,0.4,1,1,0.666667,1,0.8,0,0,1,1,1,1,1,1,0.666667,1,1,0.5,0.666667,0.8,1,0.5,1,1,1,1,1,0.666667,1,1,1,1,0.8,1,0.666667,0.666667,0,1,1,0,0.666667,1,1,1,0.666667,0.666667,1,0.666667,1,1,1,1,1,1,1,1,0.666667,1,0,1,1,0.666667,1,1,0.666667,1,1,1,0,1,1,1,1,0.666667,1,0.666667,1,1,1,0,1] f_measure 0.863333 [0.666667,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,0,0.666667,0.666667,1,1,1,1,1,0,1,1,1,0.666667,1,1,1,0,1,0.8,0,0.8,1,1,0.666667,1,1,1,1,1,1,1,0.666667,1,0,0.666667,1,1,1,0.666667,1,1,0.8,0,1,0.8,1,1,1,0,0,1,1,0.8,1,1,1,1,0.8,1,1,0.666667,1,1,0.666667,1,0.666667,0.666667,0.666667,0.666667,1,1,1,1,1,1,0.8,0.666667,1,1,1,1,0.8,1] f_measure 0.81 [1,1,1,1,1,1,1,1,0.8,1,1,1,1,1,1,0.666667,1,0.5,1,0,1,1,0,0.666667,1,1,0,1,1,1,1,1,1,0,1,1,1,0,0.666667,1,0,1,1,1,1,1,0.666667,1,0.666667,1,0.666667,0.666667,1,0.4,0.666667,0,1,1,0.5,0,1,1,0,1,1,1,0.8,1,0.8,0.666667,1,0,0.5,1,1,1,1,0,0.666667,0.5,0.666667,0,1,0.5,1,1,1,0.666667,0.666667,1,0.666667,1,1,1,0,1,1,1,0.666667,1] f_measure 0.822917 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,1,1,0,0.666667,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.4,0.8,0,1,1,0.666667,1,1,1,1,1,1,1,0.666667,0.5,1,0.666667,1,1,1,0.666667,1,0.666667,1,0,0.5,0.5,1,1,0,1,0.666667,1,0.666667,1,0.666667,0.666667,1,0,0.5,1,0.4,0.666667,1,0,1,0.8,1,0,1,0,1,0.5,0.8,1,1,1,1,1,1,0.666667,1,1,0.666667,1,1,1] kappa 0.797852 kappa 0.873638 kappa 0.861001 kappa 0.867309 kappa 0.804154 kappa 0.842028 kappa 0.854683 kappa 0.873623 kappa 0.823121 kappa 0.829438 kb_relative_information_score 128.326136 kb_relative_information_score 139.956347 kb_relative_information_score 139.278675 kb_relative_information_score 140.306387 kb_relative_information_score 129.742135 kb_relative_information_score 135.616729 kb_relative_information_score 136.858233 kb_relative_information_score 140.773549 kb_relative_information_score 132.627427 kb_relative_information_score 132.945153 mean_absolute_error 0.003992 mean_absolute_error 0.0025 mean_absolute_error 0.002746 mean_absolute_error 0.002623 mean_absolute_error 0.003904 mean_absolute_error 0.003126 mean_absolute_error 0.002918 mean_absolute_error 0.002516 mean_absolute_error 0.003486 mean_absolute_error 0.003373 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 [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] precision 0.802083 [0.5,1,1,1,1,0.5,1,0.5,1,1,1,1,1,0,1,1,1,0,0.5,0.5,1,1,1,1,0,0.333333,0.5,0.666667,1,1,1,1,1,0,1,0.5,1,0.5,1,1,0.5,1,1,0.666667,1,1,1,0.333333,1,1,1,1,0.5,0.666667,0,0,1,1,0,0.666667,1,1,0,1,1,1,1,1,1,0,1,0.5,1,1,0,1,1,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.666667,1,1,0,0.5,0.333333,1] precision 0.901042 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,1,0.5,0.666667,1,1,1,1,1,1,1,1,0.5,0,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,0.666667,1,1,1,1,0,1,0,1,0.5,1,0.5,1,0.666667,1,0.666667,1,1,0.5,0.5,1,1,1,1,1,0.666667,1,0.666667,1,0.666667,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1] precision 0.872917 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.25,1,1,0,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,0.5,0,1,1,0.5,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,0.666667,1,1,1,1,0.5,1,1,1,1,1,1,0.666667,1,1,0,1,1,1,1,1,0.5,1,1,0.666667,0.666667,1,0.5,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,0.666667,1,0.333333,1] precision 0.893229 [0.666667,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.666667,1,0.5,0,0.333333,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,0.666667,1,1,1,1,1,1,1,0.666667,0.666667,1,1,1,0.5,1,1,1,1,1,1,0.25,1,0.666667,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.666667,0.666667,0,1,1,0,1] precision 0.793437 [1,0,1,1,1,1,1,1,0,1,0.666667,1,1,0.333333,1,1,1,0,0.25,0.2,0,1,1,1,1,0,0,1,1,1,1,1,1,0,1,1,0.5,0,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0,1,0.5,0.666667,0.4,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0.5,0.666667,0,1,0.5,1,1,1,1,0.666667,1,1,0.5,0,1,1,0.666667,1,0.5,1] precision 0.866667 [0.5,1,1,1,1,1,1,1,1,1,0.666667,1,0,1,1,1,1,1,1,0,1,1,1,1,0.666667,1,0.25,1,1,1,1,0.5,1,0.5,1,1,1,0,1,1,0.666667,0.666667,1,0.5,1,1,1,1,1,1,1,0.5,0.5,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0.666667,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,1,1,0.5,0.666667,0,0,0.666667] precision 0.851042 [0.666667,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.333333,0.333333,1,1,0.5,1,0.666667,0,0,1,1,1,1,1,1,0.5,1,1,0.5,1,0.666667,1,0.5,1,1,1,1,1,1,1,1,1,1,0.666667,1,0.5,1,0,1,1,0,0.5,1,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,0.5,1,1,1,0,1,1,1,1,1,1,0.5,1,1,1,0,1] precision 0.888542 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0,1,1,1,1,1,1,1,0,1,0.666667,0,0.666667,1,1,1,1,1,1,1,1,1,1,0.5,1,0,1,1,1,1,1,1,1,0.666667,0,1,0.666667,1,1,1,0,0,1,1,0.666667,1,1,1,1,0.666667,1,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0.666667,0.5,1,1,1,1,0.666667,1] precision 0.827083 [1,1,1,1,1,1,1,1,0.666667,1,1,1,1,1,1,0.5,1,0.333333,1,0,1,1,0,1,1,1,0,1,1,1,1,1,1,0,1,1,1,0,1,1,0,1,1,1,1,1,0.5,1,0.5,1,1,1,1,0.333333,1,0,1,1,0.5,0,1,1,0,1,1,1,0.666667,1,0.666667,0.5,1,0,0.5,1,1,1,1,0,0.5,0.5,0.5,0,1,0.333333,1,1,1,1,0.5,1,1,1,1,1,0,1,1,1,1,1] precision 0.843229 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,0.25,0.666667,0,1,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,1,1,1,1,1,0.5,1,0,0.5,0.5,1,1,0,1,0.5,1,1,1,1,0.5,1,0,0.5,1,0.333333,1,1,0,1,0.666667,1,0,1,0,1,0.5,0.666667,1,1,1,1,1,1,1,1,1,0.5,1,1,1] predictive_accuracy 0.8 predictive_accuracy 0.875 predictive_accuracy 0.8625 predictive_accuracy 0.86875 predictive_accuracy 0.80625 predictive_accuracy 0.84375 predictive_accuracy 0.85625 predictive_accuracy 0.875 predictive_accuracy 0.825 predictive_accuracy 0.83125 prior_entropy 6.643856 prior_entropy 6.643856 prior_entropy 6.643856 prior_entropy 6.643856 prior_entropy 6.643856 prior_entropy 6.643856 prior_entropy 6.643856 prior_entropy 6.643856 prior_entropy 6.643856 prior_entropy 6.643856 recall 0.8 [1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,0,1,1,1,0,0.5,1,1,1,1,1,0,1,0.5,1,0.5,1,1,1,1,0,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1,0.5,1,0.5,1,1,0,0,1,1,0,1,1,1,0,1,1,1,0.5,1,1,0,1,1,1,1,0,1,0.5,0,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,0,1,0.5,1] recall 0.875 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,0.5,0,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,0.5,1,0,1,0,1,0.5,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1] recall 0.8625 [0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0,0.5,1,1,0,1,1,0.5,0.5,1,1,1,1,1,0.5,0.5,1,1,1,0,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1] recall 0.86875 [1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,1,1,1,0.5,1,1,1,1,1,0.5,0.5,1,1,0.5,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,1,0,1,1,0,0.5,1,0,1] recall 0.80625 [1,0,1,1,1,1,1,1,0,0.5,1,1,1,1,1,1,0.5,0,1,0.5,0,1,1,1,1,0,0,1,1,0.5,1,1,1,0,1,1,1,0,1,1,1,1,1,1,1,1,1,1,1,1,0,0,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,0.5,1,1,0.5,1,1,1,1,0,0.5,1,1,1,1,1,1,1,0.5,1,0,0.5,1,1,1,1,1] recall 0.84375 [0.5,1,1,1,1,0.5,1,1,1,0.5,1,1,0,1,1,1,0.5,1,1,0,1,1,1,1,1,0.5,0.5,1,1,1,1,1,1,0.5,1,0.5,0.5,0,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,0.5,1,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,0.5,0.5,1,1,1,1,1,1,0.5,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,1,1,1,0,0,1] recall 0.85625 [1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,1,0,0.5,0.5,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,1,0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,0.5,0,1,1,0,1,1,1,1,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,0,1,1,1,1,0.5,1,1,1,1,1,0,1] recall 0.875 [0.5,1,1,1,1,1,0.5,1,1,1,1,1,1,1,1,1,1,0,1,0.5,1,1,1,1,1,0,1,1,1,0.5,1,1,1,0,1,1,0,1,1,1,0.5,1,1,1,1,1,1,1,1,1,0,0.5,1,1,1,0.5,1,1,1,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,0.5,0.5,0.5,1,1,1,1,1,1,1,1,1,1,1,1,1,1] recall 0.825 [1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,0,1,1,0,0.5,1,1,0,1,1,1,1,1,1,0,1,1,1,0,0.5,1,0,1,1,1,1,1,1,1,1,1,0.5,0.5,1,0.5,0.5,0,1,1,0.5,0,1,1,0,1,1,1,1,1,1,1,1,0,0.5,1,1,1,1,0,1,0.5,1,0,1,1,1,1,1,0.5,1,1,0.5,1,1,1,0,1,1,1,0.5,1] recall 0.83125 [1,1,1,1,1,1,1,1,1,1,1,1,1,0.5,1,1,1,0,0.5,0,1,1,1,1,1,0,0,1,1,1,1,1,1,1,1,0,1,1,0.5,1,1,1,1,1,1,1,1,0.5,1,1,1,1,1,0.5,1,1,1,0,0.5,0.5,1,1,0,1,1,1,0.5,1,0.5,1,1,0,0.5,1,0.5,0.5,1,0,1,1,1,0,1,0,1,0.5,1,1,1,1,1,1,1,0.5,1,1,1,1,1,1] relative_absolute_error 0.201631 relative_absolute_error 0.126263 relative_absolute_error 0.138676 relative_absolute_error 0.132458 relative_absolute_error 0.197169 relative_absolute_error 0.157868 relative_absolute_error 0.147375 relative_absolute_error 0.127057 relative_absolute_error 0.176077 relative_absolute_error 0.170337 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 0.063123 root_mean_squared_error 0.049953 root_mean_squared_error 0.051325 root_mean_squared_error 0.050403 root_mean_squared_error 0.061883 root_mean_squared_error 0.055016 root_mean_squared_error 0.053755 root_mean_squared_error 0.049614 root_mean_squared_error 0.058484 root_mean_squared_error 0.058026 root_relative_squared_error 0.634408 root_relative_squared_error 0.502045 root_relative_squared_error 0.515838 root_relative_squared_error 0.506567 root_relative_squared_error 0.621946 root_relative_squared_error 0.552928 root_relative_squared_error 0.540263 root_relative_squared_error 0.49864 root_relative_squared_error 0.587785 root_relative_squared_error 0.583182 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