Package

r-adabag

Description

Applies Multiclass AdaBoost.M1, SAMME and Bagging. It implements Freund
and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm
using classification trees as individual classifiers. Once these
classifiers have been trained, they can be used to predict on new data.
Also, cross validation estimation of the error can be done. Since
version 2.0 the function margins() is available to calculate the margins
for these classifiers. Also a higher flexibility is achieved giving
access to the rpart.control() argument of '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 as a 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 of each class for observations can be obtained. Version 3.1
modifies the relative importance measure to 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 (Guo and 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.

Note