R: Applies Multiclass AdaBoost.M1, SAMME and Bagging

# Applies Multiclass AdaBoost.M1, SAMME and Bagging

## Documentation for package ‘adabag’ version 5.0

- DESCRIPTION file.

## Help Pages

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