CRAN/E | adabag

adabag

Applies Multiclass AdaBoost.M1, SAMME and Bagging

Installation

About

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).

Citation adabag citation info

Key Metrics

Version 5.0
R ≥ 4.0.0
Published 2023-05-31 302 days ago
Needs compilation? no
License GPL-2
License GPL-3
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Maintainer

Maintainer

Esteban Alfaro

Esteban.Alfaro@uclm.es

Authors

Alfaro
Esteban; Gamez
Matias
Garcia
Noelia;
L. Guo
A. Albano
M. Sciandra
A. Plaia

Material

Reference manual
Package source

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

adabag archive

Depends

rpart
caret
foreach
doParallel
R ≥ 4.0.0

Imports

methods
tidyr
dplyr
ConsRank ≥ 2.1.3

Suggests

mlbench

Reverse Depends

m6Aboost

Reverse Imports

pheble
rminer
traineR

Reverse Suggests

crtests
MachineShop
mlr
pdp