190 2 weka.MultiBoostAB_BFTree 1 Weka_3.7.10_8109 Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2). 2014-06-03T15:12:32 English Weka_3.7.10 C option 3 Number of sub-committees. (Default 3) D flag If set, classifier is run in debug mode and may output additional info to the console I option 10 Number of iterations. (default 10) P option 100 Percentage of weight mass to base training on. (default 100, reduce to around 90 speed up) Q flag Use resampling for boosting. S option 1 Random number seed. (default 1) W baselearner weka.classifiers.trees.BFTree Full name of base classifier. (default: weka.classifiers.trees.DecisionStump) W 160 2 weka.BFTree 1 Weka_3.7.10_8109 Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of statistics. 28(2):337-407. 2014-06-03T14:58:27 English Weka_3.7.10 A flag Use the 1 SE rule to make pruning decision. (default no). C flag true Percentage of training data size (0-1] (default 1). D flag If set, classifier is run in debug mode and may output additional info to the console G flag Don't use Gini index for splitting (default yes), if not information is used. H flag Don't use heuristic search for nominal attributes in multi-class problem (default yes). M option 2 The minimal number of instances at the terminal nodes. (default 2) N option 5 The number of folds used in the pruning. (default 5) P option POSTPRUNED The pruning strategy. (default: POSTPRUNED) R flag Don't use error rate in internal cross-validation (default yes), but root mean squared error. S option 1 Random number seed. (default 1) Verified_Supervised_Classification Verified_Supervised_Classification https://api.openml.org/data/download/30276/weka.classifiers.meta.MultiBoostAB6310602320943099694.class class ad2a381e81aa8a5e7013df93dc41794c