adabag v4.2


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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.

Functions in adabag

Name Description Runs v-fold cross validation with AdaBoost.M1 or SAMME
errorevol Shows the error evolution of the ensemble Runs v-fold cross validation with Bagging
boosting Applies the AdaBoost.M1 and SAMME algorithms to a data set
adabag-internal Internal adabag functions
adabag-package Applies Multiclass AdaBoost.M1, SAMME and Bagging
autoprune Builds automatically a pruned tree of class rpart
bagging Applies the Bagging algorithm to a data set
importanceplot Plots the variables relative importance
margins Calculates the margins
plot.errorevol Plots the error evolution of the ensemble
plot.margins Plots the margins of the ensemble
predict.bagging Predicts from a fitted bagging object
predict.boosting Predicts from a fitted boosting object
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Type Package
Date 2018-01-10
License GPL (>= 2)
LazyLoad yes
NeedsCompilation no
Packaged 2018-01-18 09:32:23 UTC; emilio
Repository CRAN
Date/Publication 2018-01-19 14:52:39 UTC

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