521110 64 Rafael Gomes Mantovani 9956 Supervised Classification 2596 classif.LiblineaRL1LogReg(6) 3500 seed 1 2596 kind Mersenne-Twister 2596 normal.kind Inversion 2596 study_7 1493 one-hundred-plants-texture https://www.openml.org/data/download/1592285/phpoOxxNn -1 1791581 description https://api.openml.org/data/download/1791581/file37d21949bd86 -1 1791582 predictions https://api.openml.org/data/download/1791582/file37d2402306d1 area_under_roc_curve 0.589703 [0.5,0.5,0.5,0.79955,0.498421,0.5,0.5,0.499368,0.559657,0.5,0.5,0.497789,0.5,0.617104,0.5,0.796707,0.61584,0.5,0.5,0.558078,0.5,0.5,0.561552,0.5,0.5,0.829852,0.72789,0.497157,0.771774,0.928024,0.5,0.992419,0.5,0.763246,0.499368,0.5,0.499684,0.608891,0.5,0.856996,0.5,0.748105,0.525249,0.5,0.5,0.5,0.5,0.499368,0.5,0.499684,0.882877,0.5,0.5,0.663811,0.5,0.745578,0.967171,0.5,0.5,0.829536,0.980733,0.769563,0.499684,0.770511,0.5,0.49621,0.5,0.499052,0.497157,0.5,0.5,0.5,0.527144,0.648669,0.499684,0.5,0.5,0.9558,0.5,0.5,0.5,0.5,0.5,0.499684,0.528407,0.913495,0.5,0.5,0.5,0.863629,0.686552,0.5,0.5,0.92834,0.58996,0.5,0.499684,0.982944,0.5,0.48705] average_cost 0 f_measure 0.10239 [0,0,0,0.298507,0,0,0,0,0.148148,0,0,0,0,0.177778,0,0.263158,0.163265,0,0,0.125,0,0,0.190476,0,0,0.309859,0.170213,0,0.327273,0.466667,0,0.571429,0,0.219512,0,0,0,0.112676,0,0.282353,0,0.533333,0.055556,0,0,0,0,0,0,0,0.252427,0,0,0.123711,0,0.421053,0.833333,0,0,0.305556,0.344086,0.290323,0,0.305085,0,0,0,0,0,0,0,0,0.066667,0.222222,0,0,0,0.416667,0,0,0,0,0,0,0.076923,0.264151,0,0,0,0.375,0.48,0,0,0.474576,0.193548,0,0,0.372093,0,0] kappa 0.179406 kb_relative_information_score 297.128115 mean_absolute_error 0.016248 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 0.081741 [0,0,0,0.196078,0,0,0,0,0.181818,0,0,0,0,0.137931,0,0.166667,0.121212,0,0,0.125,0,0,0.4,0,0,0.2,0.102564,0,0.230769,0.318182,0,0.4,0,0.136364,0,0,0,0.072727,0,0.173913,0,0.571429,0.05,0,0,0,0,0,0,0,0.149425,0,0,0.074074,0,0.363636,0.75,0,0,0.196429,0.207792,0.195652,0,0.209302,0,0,0,0,0,0,0,0,0.071429,0.172414,0,0,0,0.267857,0,0,0,0,0,0,0.1,0.155556,0,0,0,0.25,0.666667,0,0,0.325581,0.2,0,0,0.228571,0,0] predictive_accuracy 0.187617 prior_entropy 6.643831 recall 0.187617 [0,0,0,0.625,0,0,0,0,0.125,0,0,0,0,0.25,0,0.625,0.25,0,0,0.125,0,0,0.125,0,0,0.6875,0.5,0,0.5625,0.875,0,1,0,0.5625,0,0,0,0.25,0,0.75,0,0.5,0.0625,0,0,0,0,0,0,0,0.8125,0,0,0.375,0,0.5,0.9375,0,0,0.6875,1,0.5625,0,0.5625,0,0,0,0,0,0,0,0,0.0625,0.3125,0,0,0,0.9375,0,0,0,0,0,0,0.0625,0.875,0,0,0,0.75,0.375,0,0,0.875,0.1875,0,0,1,0,0] relative_absolute_error 0.820589 root_mean_prior_squared_error 0.099499 root_mean_squared_error 0.127466 root_relative_squared_error 1.281085 total_cost 0 usercpu_time_millis 5.48 usercpu_time_millis_testing 0.0199999999999987 usercpu_time_millis_training 5.46 area_under_roc_curve 0.599574 [0.5,0.5,0.5,0.981013,0.5,0.5,0.5,0.496855,0.5,0.5,0.5,0.493671,0.5,0.487342,0.5,0.740506,0.743671,0.5,0.5,0.5,0.5,0.5,0.75,0.5,0.5,0.987421,0.971698,0.5,0.993671,0.496855,0.5,0.996835,0.5,0.481132,0.5,0.5,0.496855,0.471698,0.5,0.737342,0.5,0.993711,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.977987,0.5,0.5,0.731013,0.5,0.75,1,0.5,0.5,0.984277,0.981132,0.731013,0.5,0.490566,0.5,0.5,0.5,0.5,0.493711,0.5,0.5,0.5,0.993711,0.493711,0.5,0.5,0.5,0.990506,0.5,0.5,0.5,0.5,0.5,0.5,0.996855,0.987342,0.5,0.5,0.5,0.740506,0.746835,0.5,0.5,0.990506,0.740506,0.5,0.496855,0.977987,0.5,0.484277] area_under_roc_curve 0.571252 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[0.5,0.5,0.5,0.984177,0.5,0.5,0.5,0.5,0.487421,0.5,0.5,0.5,0.5,0.5,0.5,0.724684,0.496835,0.5,0.5,0.987421,0.5,0.5,0.5,0.5,0.5,0.737342,0.977987,0.493671,0.490506,0.984177,0.5,0.993711,0.5,0.990506,0.5,0.5,0.5,0.743671,0.5,0.981132,0.5,0.496855,0.487421,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.977987,0.5,0.5,0.724684,0.5,0.743671,1,0.5,0.5,0.977987,0.984177,0.5,0.496855,0.481132,0.5,0.496855,0.5,0.5,0.5,0.5,0.5,0.5,0.496835,1,0.5,0.5,0.5,0.971698,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.984177,0.5,0.5,0.5,0.996855,1,0.5,0.5,0.990566,0.987421,0.5,0.5,0.977848,0.5,0.493711] area_under_roc_curve 0.573959 [0.5,0.5,0.5,0.984277,0.493711,0.5,0.5,0.5,1,0.5,0.5,0.496855,0.5,0.496835,0.5,0.974843,0.5,0.5,0.5,0.487421,0.5,0.5,0.5,0.5,0.5,0.743671,0.731013,0.5,0.731013,0.990506,0.5,0.993711,0.5,0.737342,0.496835,0.5,0.5,0.487342,0.5,0.465409,0.5,0.996835,0.490506,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.734177,0.5,0.5,0.468553,0.5,0.5,1,0.5,0.5,0.737342,0.974684,0.990566,0.5,0.740506,0.5,0.493711,0.5,0.5,0.496835,0.5,0.5,0.5,0.5,0.731013,0.5,0.5,0.5,0.993711,0.5,0.5,0.5,0.5,0.5,0.496855,0.5,0.968354,0.5,0.5,0.5,0.487421,0.5,0.5,0.5,0.487421,0.5,0.5,0.5,0.984177,0.5,0.490506] area_under_roc_curve 0.599037 [0.5,0.5,0.5,0.493711,0.493711,0.5,0.5,0.5,1,0.5,0.5,0.5,0.5,0.493671,0.5,0.968553,0.743671,0.5,0.5,0.496855,0.5,0.5,0.496835,0.5,0.5,0.990506,0.990506,0.487421,0.987342,0.990506,0.5,0.996855,0.5,0.724684,0.5,0.5,0.5,0.490506,0.5,0.990566,0.5,0.5,0.740506,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.971519,0.5,0.5,0.968553,0.5,0.987342,0.75,0.5,0.5,0.731013,0.981013,0.487421,0.5,0.740506,0.5,0.496855,0.5,0.496835,0.5,0.5,0.5,0.5,0.5,0.496835,0.5,0.5,0.5,0.987421,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.971519,0.5,0.5,0.5,0.993711,0.5,0.5,0.5,0.996835,0.490566,0.5,0.5,0.981013,0.5,0.493671] area_under_roc_curve 0.602241 [0.5,0.5,0.5,0.481132,0.496855,0.5,0.5,0.5,0.496835,0.5,0.5,0.496835,0.5,0.990566,0.5,0.490566,0.493711,0.5,0.5,0.5,0.5,0.5,0.496855,0.5,0.5,0.740506,0.474684,0.5,0.484277,0.981013,0.5,0.993671,0.5,0.984177,0.5,0.5,0.5,0.737342,0.5,0.987342,0.5,1,0.496835,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.987342,0.5,0.5,0.968553,0.5,0.5,1,0.5,0.5,0.740506,0.977848,0.743671,0.5,0.746835,0.5,0.484277,0.5,0.5,0.496835,0.5,0.5,0.5,0.493671,0.490506,0.5,0.5,0.5,0.984177,0.5,0.5,0.5,0.5,0.5,0.5,0.496835,0.965409,0.5,0.5,0.5,0.990506,0.75,0.5,0.5,0.990506,0.993711,0.5,0.5,0.987342,0.5,0.490506] area_under_roc_curve 0.57376 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[0.5,0.5,0.5,0.740446,0.5,0.5,0.5,0.5,0.496815,0.5,0.5,0.5,0.5,0.987342,0.5,0.737261,0.481013,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.981013,0.737261,0.5,0.990506,0.993671,0.5,0.996815,0.5,0.481013,0.5,0.5,0.5,0.974684,0.5,0.714968,0.5,0.75,0.490446,0.5,0.5,0.5,0.5,0.496835,0.5,0.5,0.727707,0.5,0.5,0.724522,0.5,1,0.996815,0.5,0.5,0.993631,0.977848,0.734076,0.5,0.740446,0.5,0.5,0.5,0.5,0.496815,0.5,0.5,0.5,0.493671,0.496815,0.5,0.5,0.5,0.730892,0.5,0.5,0.5,0.5,0.5,0.5,0.496815,0.981013,0.5,0.5,0.5,0.984076,0.496815,0.5,0.5,0.990446,0.5,0.5,0.5,0.987342,0.5,0.487261] 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 0.125675 [0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0.333333,0.4,0,0,0,0,0,0.666667,0,0,0.333333,0.181818,0,0.666667,0,0,0.8,0,0,0,0,0,0,0,0.285714,0,0.5,0,0,0,0,0,0,0,0,0.222222,0,0,0.222222,0,0.666667,1,0,0,0.285714,0.25,0.222222,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0.571429,0,0,0,0,0,0,0.666667,0.5,0,0,0,0.333333,0.5,0,0,0.571429,0.333333,0,0,0.222222,0,0] f_measure 0.07116 [0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0.285714,0,0,0,0,0,0,0,0,0.333333,0.333333,0,0.571429,0,0.2,0,0,0,0,0,0.285714,0,0,0,0,0,0,0,0,0,0,0.153846,0,0,0,0,0.571429,0.5,0,0,0.25,0.222222,0,0,0,0,0,0,0,0,0,0,0,0,0.285714,0,0,0,0.666667,0,0,0,0,0,0,0,0.4,0,0,0,0.571429,0,0,0,0.285714,0,0,0,0.25,0,0] f_measure 0.09951 [0,0,0,0.285714,0,0,0,0,0,0,0,0,0,0.285714,0,0.666667,0.5,0,0,0,0,0,0,0,0,0.5,0.166667,0,0.8,0.571429,0,0.5,0,0.25,0,0,0,0.25,0,0.4,0,0.666667,0,0,0,0,0,0,0,0,0.181818,0,0,0,0,0,1,0,0,0,0.363636,0.4,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0,0,0,0.153846,0,0,0,0.285714,1,0,0,0.333333,0,0,0,0.5,0,0] f_measure 0.091601 [0,0,0,0.444444,0,0,0,0,0,0,0,0,0,0,0,0.181818,0,0,0,0.333333,0,0,0,0,0,0.285714,0.222222,0,0,0.444444,0,0.5,0,0.571429,0,0,0,0.4,0,0.25,0,0,0,0,0,0,0,0,0,0,0.222222,0,0,0.181818,0,0.4,1,0,0,0.222222,0.444444,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.181818,0,0,0,0,0,0,0,0.444444,0,0,0,0.666667,1,0,0,0.4,0.333333,0,0,0.363636,0,0] f_measure 0.08249 [0,0,0,0.285714,0,0,0,0,1,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0.4,0.222222,0,0.222222,0.571429,0,0.5,0,0.285714,0,0,0,0,0,0,0,0.8,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,1,0,0,0.285714,0.333333,0.4,0,0.333333,0,0,0,0,0,0,0,0,0,0.222222,0,0,0,0.5,0,0,0,0,0,0,0,0.285714,0,0,0,0,0,0,0,0,0,0,0,0.444444,0,0] f_measure 0.108546 [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.166667,0.4,0,0,0,0,0,0,0,0,0.571429,0.571429,0,0.5,0.571429,0,0.666667,0,0.181818,0,0,0,0,0,0.4,0,0,0.333333,0,0,0,0,0,0,0,0.307692,0,0,0.166667,0,0.5,0.666667,0,0,0.222222,0.4,0,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0,0,0,0.307692,0,0,0,0.5,0,0,0,0.8,0,0,0,0.4,0,0] f_measure 0.126142 [0,0,0,0,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0.4,0,0.666667,0,0.444444,0,0,0,0.285714,0,0.5,0,1,0,0,0,0,0,0,0,0,0.5,0,0,0.166667,0,0,1,0,0,0.333333,0.363636,0.4,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.444444,0,0,0,0,0,0,0,0.153846,0,0,0,0.571429,0.666667,0,0,0.571429,0.5,0,0,0.5,0,0] f_measure 0.08145 [0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0.5,0,0.571429,0,0,0,0,0,0,0,0.4,0,0,0,0,0,0,0,0,0,0,0.142857,0,0,0,0,0,0.8,0,0,0.333333,0.571429,0.4,0,0.4,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.2,0.666667,0,0,0.4,0,0,0,0.363636,0,0] f_measure 0.126773 [0,0,0,0.285714,0,0,0,0,0,0,0,0,0,0.285714,0,0.333333,0,0,0,0,0,0,0.666667,0,0,0.25,0.166667,0,0,0.5,0,0.363636,0,0.25,0,0,0,0,0,0.444444,0,0.666667,0,0,0,0,0,0,0,0,0.5,0,0,0.222222,0,0.4,0.8,0,0,0.8,0.285714,0.444444,0,0.666667,0,0,0,0,0,0,0,0,0,0.571429,0,0,0,0.666667,0,0,0,0,0,0,0,0.181818,0,0,0,0.333333,0.5,0,0,0.8,0,0,0,0.333333,0,0] f_measure 0.099731 [0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0.333333,0,0.285714,0,0,0,0,0,0,0,0,0,0.25,0.285714,0,0.4,0.5,0,0.8,0,0,0,0,0,0.2,0,0.142857,0,0.666667,0,0,0,0,0,0,0,0,0.2,0,0,0.181818,0,1,0.8,0,0,0.666667,0.222222,0.25,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0,0,0.222222,0,0,0,0,0,0,0,0.25,0,0,0,0.444444,0,0,0,0.571429,0,0,0,0.333333,0,0] kappa 0.198833 kappa 0.142294 kappa 0.186152 kappa 0.179875 kappa 0.147795 kappa 0.197916 kappa 0.2042 kappa 0.147357 kappa 0.21711 kappa 0.174092 kb_relative_information_score 32.718837 kb_relative_information_score 23.700299 kb_relative_information_score 30.714717 kb_relative_information_score 29.712658 kb_relative_information_score 24.702359 kb_relative_information_score 32.718708 kb_relative_information_score 33.720767 kb_relative_information_score 24.70223 kb_relative_information_score 35.724887 kb_relative_information_score 28.712653 mean_absolute_error 0.015875 mean_absolute_error 0.017 mean_absolute_error 0.016125 mean_absolute_error 0.01625 mean_absolute_error 0.016875 mean_absolute_error 0.015875 mean_absolute_error 0.01575 mean_absolute_error 0.016875 mean_absolute_error 0.0155 mean_absolute_error 0.016352 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 0.107902 [0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0.25,0.333333,0,0,0,0,0,1,0,0,0.2,0.1,0,0.5,0,0,0.666667,0,0,0,0,0,0,0,0.2,0,0.333333,0,0,0,0,0,0,0,0,0.125,0,0,0.142857,0,1,1,0,0,0.166667,0.142857,0.142857,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0.4,0,0,0,0,0,0,0.5,0.333333,0,0,0,0.25,0.5,0,0,0.4,0.25,0,0,0.125,0,0] precision 0.049303 [0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0,0.2,0,0,0,0,0,0,0,0,0.25,0.25,0,0.4,0,0.111111,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0.083333,0,0,0,0,0.4,0.333333,0,0,0.142857,0.125,0,0,0,0,0,0,0,0,0,0,0,0,0.166667,0,0,0,0.5,0,0,0,0,0,0,0,0.25,0,0,0,0.4,0,0,0,0.166667,0,0,0,0.142857,0,0] precision 0.072295 [0,0,0,0.2,0,0,0,0,0,0,0,0,0,0.2,0,0.5,0.333333,0,0,0,0,0,0,0,0,0.333333,0.090909,0,0.666667,0.4,0,0.333333,0,0.166667,0,0,0,0.166667,0,0.25,0,0.5,0,0,0,0,0,0,0,0,0.1,0,0,0,0,0,1,0,0,0,0.222222,0.25,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0.090909,0,0,0,0.166667,1,0,0,0.2,0,0,0,0.333333,0,0] precision 0.067557 [0,0,0,0.285714,0,0,0,0,0,0,0,0,0,0,0,0.111111,0,0,0,0.2,0,0,0,0,0,0.2,0.125,0,0,0.285714,0,0.333333,0,0.4,0,0,0,0.333333,0,0.142857,0,0,0,0,0,0,0,0,0,0,0.125,0,0,0.111111,0,0.333333,1,0,0,0.125,0.285714,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0.1,0,0,0,0,0,0,0,0.285714,0,0,0,0.5,1,0,0,0.25,0.2,0,0,0.222222,0,0] precision 0.061186 [0,0,0,0.166667,0,0,0,0,1,0,0,0,0,0,0,0.111111,0,0,0,0,0,0,0,0,0,0.333333,0.142857,0,0.142857,0.4,0,0.333333,0,0.2,0,0,0,0,0,0,0,0.666667,0,0,0,0,0,0,0,0,0.166667,0,0,0,0,0,1,0,0,0.2,0.2,0.25,0,0.25,0,0,0,0,0,0,0,0,0,0.142857,0,0,0,0.333333,0,0,0,0,0,0,0,0.166667,0,0,0,0,0,0,0,0,0,0,0,0.285714,0,0] precision 0.083961 [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0.090909,0.333333,0,0,0,0,0,0,0,0,0.4,0.4,0,0.333333,0.4,0,0.5,0,0.111111,0,0,0,0,0,0.25,0,0,0.25,0,0,0,0,0,0,0,0.181818,0,0,0.090909,0,0.333333,1,0,0,0.142857,0.25,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0.2,0,0,0,0,0,0,0,0.181818,0,0,0,0.333333,0,0,0,0.666667,0,0,0,0.25,0,0] precision 0.103197 [0,0,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0.25,0,0,0,0.25,0,0.5,0,0.285714,0,0,0,0.2,0,0.333333,0,1,0,0,0,0,0,0,0,0,0.333333,0,0,0.090909,0,0,1,0,0,0.25,0.222222,0.333333,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.285714,0,0,0,0,0,0,0,0.083333,0,0,0,0.4,1,0,0,0.4,0.333333,0,0,0.333333,0,0] precision 0.06316 [0,0,0,0.2,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0.111111,0,0,0,0.333333,0,0.4,0,0,0,0,0,0,0,0.25,0,0,0,0,0,0,0,0,0,0,0.083333,0,0,0,0,0,0.666667,0,0,0.25,0.4,0.25,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0.333333,0,0,0,0,0,0,0,0,0,0,0,0.125,1,0,0,0.333333,0,0,0,0.222222,0,0] precision 0.099638 [0,0,0,0.2,0,0,0,0,0,0,0,0,0,0.166667,0,0.25,0,0,0,0,0,0,0.5,0,0,0.142857,0.1,0,0,0.333333,0,0.222222,0,0.142857,0,0,0,0,0,0.285714,0,1,0,0,0,0,0,0,0,0,0.333333,0,0,0.142857,0,0.25,0.666667,0,0,0.666667,0.166667,0.285714,0,0.5,0,0,0,0,0,0,0,0,0,0.4,0,0,0,0.5,0,0,0,0,0,0,0,0.1,0,0,0,0.25,0.5,0,0,0.666667,0,0,0,0.2,0,0] precision 0.079253 [0,0,0,0.25,0,0,0,0,0,0,0,0,0,0.2,0,0.2,0,0,0,0,0,0,0,0,0,0.142857,0.2,0,0.25,0.333333,0,0.666667,0,0,0,0,0,0.111111,0,0.083333,0,1,0,0,0,0,0,0,0,0,0.125,0,0,0.111111,0,1,0.666667,0,0,0.5,0.125,0.166667,0,0.25,0,0,0,0,0,0,0,0,0,0,0,0,0,0.142857,0,0,0,0,0,0,0,0.142857,0,0,0,0.285714,0,0,0,0.4,0,0,0,0.2,0,0] predictive_accuracy 0.20625 predictive_accuracy 0.15 predictive_accuracy 0.19375 predictive_accuracy 0.1875 predictive_accuracy 0.15625 predictive_accuracy 0.20625 predictive_accuracy 0.2125 predictive_accuracy 0.15625 predictive_accuracy 0.225 predictive_accuracy 0.18239 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 0.20625 [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0,0,0,0,0.5,0,0,1,1,0,1,0,0,1,0,0,0,0,0,0,0,0.5,0,1,0,0,0,0,0,0,0,0,1,0,0,0.5,0,0.5,1,0,0,1,1,0.5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,1,0,0,0,0.5,0.5,0,0,1,0.5,0,0,1,0,0] recall 0.15 [0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0.5,0,0,0,0,0,0,0,0,0.5,0.5,0,1,0,1,0,0,0,0,0,0.5,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0] recall 0.19375 [0,0,0,0.5,0,0,0,0,0,0,0,0,0,0.5,0,1,1,0,0,0,0,0,0,0,0,1,1,0,1,1,0,1,0,0.5,0,0,0,0.5,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,1,0,0,0,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0.5,0,0,0,1,1,0,0,1,0,0,0,1,0,0] recall 0.1875 [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,1,0,0,0,0,0,0.5,1,0,0,1,0,1,0,1,0,0,0,0.5,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0.5,0,0.5,1,0,0,1,1,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,1,0,0,1,1,0,0,1,0,0] recall 0.15625 [0,0,0,1,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0.5,1,0,1,0,0.5,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,1,0,0,0.5,1,1,0,0.5,0,0,0,0,0,0,0,0,0,0.5,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0] recall 0.20625 [0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0.5,0,0,0,0,0,0,0,0,1,1,0,1,1,0,1,0,0.5,0,0,0,0,0,1,0,0,0.5,0,0,0,0,0,0,0,1,0,0,1,0,1,0.5,0,0,0.5,1,0,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0] recall 0.2125 [0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,1,0,1,0,1,0,0,0,0.5,0,1,0,1,0,0,0,0,0,0,0,0,1,0,0,1,0,0,1,0,0,0.5,1,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,1,0.5,0,0,1,1,0,0,1,0,0] recall 0.15625 [0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,1,0,0,0.5,1,1,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0.5,0.5,0,0,0.5,0,0,0,1,0,0] recall 0.225 [0,0,0,0.5,0,0,0,0,0,0,0,0,0,1,0,0.5,0,0,0,0,0,0,1,0,0,1,0.5,0,0,1,0,1,0,1,0,0,0,0,0,1,0,0.5,0,0,0,0,0,0,0,0,1,0,0,0.5,0,1,1,0,0,1,1,1,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,1,0,0,0,0.5,0.5,0,0,1,0,0,0,1,0,0] recall 0.18239 [0,0,0,0.5,0,0,0,0,0,0,0,0,0,1,0,0.5,0,0,0,0,0,0,0,0,0,1,0.5,0,1,1,0,1,0,0,0,0,0,1,0,0.5,0,0.5,0,0,0,0,0,0,0,0,0.5,0,0,0.5,0,1,1,0,0,1,1,0.5,0,0.5,0,0,0,0,0,0,0,0,0,0,0,0,0,0.5,0,0,0,0,0,0,0,1,0,0,0,1,0,0,0,1,0,0,0,1,0,0] relative_absolute_error 0.801766 relative_absolute_error 0.858585 relative_absolute_error 0.814393 relative_absolute_error 0.820706 relative_absolute_error 0.852271 relative_absolute_error 0.801769 relative_absolute_error 0.795456 relative_absolute_error 0.852275 relative_absolute_error 0.78283 relative_absolute_error 0.825871 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 0.125996 root_mean_squared_error 0.130384 root_mean_squared_error 0.126984 root_mean_squared_error 0.127475 root_mean_squared_error 0.129904 root_mean_squared_error 0.125996 root_mean_squared_error 0.125499 root_mean_squared_error 0.129904 root_mean_squared_error 0.124499 root_mean_squared_error 0.127876 root_relative_squared_error 1.266306 root_relative_squared_error 1.310407 root_relative_squared_error 1.276238 root_relative_squared_error 1.281175 root_relative_squared_error 1.30558 root_relative_squared_error 1.26631 root_relative_squared_error 1.261315 root_relative_squared_error 1.305585 root_relative_squared_error 1.251265 root_relative_squared_error 1.285202 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