FilteredClassifier using weka.classifiers.meta.AdaBoostM1 -P 100 -S 1 -I 10 -W weka.classifiers.trees.REPTree -- -M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0 on data filtered through weka.filters.unsupervised.attribute.Standardize Filtered Header @relation solar-flare-weka.filters.unsupervised.attribute.Standardize @attribute class {B,C,D,E,F,H} @attribute largest_spot_size {A,H,K,R,S,X} @attribute spot_distribution {C,I,O,X} @attribute Activity {1,2} @attribute Evolution {1,2,3} @attribute Previous_24_hour_flare_activity_code {1,2,3} @attribute Historically-complex {1,2} @attribute Did_region_become_historically_complex {1,2} @attribute Area {1,2} @attribute Area_of_the_largest_spot {1} @attribute C-class_flares_production_by_this_region {0,1,2,3,4,5,6,8} @attribute M-class_flares_production_by_this_region {0,1,2,3,4,5} @attribute X-class_flares_production_by_this_region {0,1,2} @data Classifier Model AdaBoostM1: Base classifiers and their weights: REPTree ============ : 0 (710/3) [356/2] Size of the tree : 1 Weight: 5.36 REPTree ============ class = B : 0 (46.22/0) [27.63/0] class = C : 0 (71.84/0) [34.16/0] class = D : 0 (81.38/0) [144.78/106.6] class = E | Previous_24_hour_flare_activity_code = 1 : 0 (30.14/0) [11.05/0] | Previous_24_hour_flare_activity_code = 2 : 0 (1.51/0) [0/0] | Previous_24_hour_flare_activity_code = 3 : 1 (215.21/2.01) [2.01/2.01] class = F : 1 (123.68/17.08) [110.12/3.52] class = H : 0 (104.99/0) [61.29/0] Size of the tree : 10 Weight: 1.96 Number of performed Iterations: 2