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adabag (version 5.0)

Applies Multiclass AdaBoost.M1, SAMME and Bagging

Description

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. Version 5.0 includes the Boosting and Bagging algorithms for label ranking (Albano, Sciandra and Plaia, 2023).

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Version

Install

install.packages('adabag')

Monthly Downloads

3,069

Version

5.0

License

GPL (>= 2)

Maintainer

Last Published

May 31st, 2023

Functions in adabag (5.0)

predict.bagging

Predicts from a fitted bagging object
predict.boosting

Predicts from a fitted boosting object
prep_data

Prepare Ranking Data for Item-Weighted Ensemble Algorithm
adabag-internal

Internal adabag functions
Ensemble_ranking_IW

Ensemble methods for ranking data: Item-Weighted Boosting and Bagging Algorithms
MarginOrderedPruning.Bagging

MarginOrderedPruning.Bagging
adabag-package

Applies Multiclass AdaBoost.M1, SAMME and Bagging
autoprune

Builds automatically a pruned tree of class rpart
bagging.cv

Runs v-fold cross validation with Bagging
boosting.cv

Runs v-fold cross validation with AdaBoost.M1 or SAMME
errorevol_ranking_vector_IW

Calculate the error evolution and final predictions of an item-weighted ensemble for rankings
errorevol

Shows the error evolution of the ensemble
simulatedRankingData

Simulated ranking data
bagging

Applies the Bagging algorithm to a data set
boosting

Applies the AdaBoost.M1 and SAMME algorithms to a data set
margins

Calculates the margins
plot.errorevol

Plots the error evolution of the ensemble
importanceplot

Plots the variables relative importance
plot.margins

Plots the margins of the ensemble