adabag (Q20039)

It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Baggingalgorithm using classification trees as individual classifiers. Once these classifiers have beentrained, they can be used to predict on new data. Also, cross validation estimation of the error canbe done. Since version 2.0 the function margins() is available to calculate the margins for theseclassifiers. Also a higher flexibility is achieved giving access to the rpart.control() argumentof 'rpart'. Four important new features were introduced on version 3.0, AdaBoost-SAMME (Zhu et al., 2009) is implemented and a new function errorevol() shows the error of the ensembles asa function of the number of iterations. In addition, the ensembles can be pruned using the option 'newmfinal' in the predict.bagging() and predict.boosting() functions and the posterior probability ofeach class for observations can be obtained. Version 3.1 modifies the relative importance measureto take into account the gain of the Gini index given by a variable in each tree and the weights of these trees. Version 4.0 includes the margin-based ordered aggregation for Bagging pruning (Guoand Boukir, 2013) and a function to auto prune the 'rpart' tree. Moreover, three new plots are also available importanceplot(), plot.errorevol() and plot.margins(). Version 4.1 allows to predict on unlabeled data. Version 4.2 includes the parallel computation option for some of the functions. Version 5.0 includes the Boosting and Bagging algorithms for label ranking (Albano, Sciandraand Plaia, 2023).