Applies Multiclass AdaBoost.M1, SAMME and Bagging

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Documentation for package ‘adabag’ version 5.0

• DESCRIPTION file.

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adabag-package	Applies Multiclass AdaBoost.M1, SAMME and Bagging
adabag	Applies Multiclass AdaBoost.M1, SAMME and Bagging
adaboost.M1	Applies the AdaBoost.M1 and SAMME algorithms to a data set
autoprune	Builds automatically a pruned tree of class 'rpart'
bagging	Applies the Bagging algorithm to a data set
bagging.cv	Runs v-fold cross validation with Bagging
boosting	Applies the AdaBoost.M1 and SAMME algorithms to a data set
boosting.cv	Runs v-fold cross validation with AdaBoost.M1 or SAMME
Ensemble_ranking_IW	Ensemble methods for ranking data: Item-Weighted Boosting and Bagging Algorithms
errorevol	Shows the error evolution of the ensemble
errorevol_ranking_vector_IW	Calculate the error evolution and final predictions of an item-weighted ensemble for rankings
importanceplot	Plots the variables relative importance
MarginOrderedPruning.Bagging	MarginOrderedPruning.Bagging
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
prep_data	Prepare Ranking Data for Item-Weighted Ensemble Algorithm
simulatedRankingData	Simulated ranking data