Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for
Binary Classification
Description
Implements the GAMbag, GAMrsm and GAMens ensemble
classifiers for binary classification (De Bock et al., 2010) . The ensembles
implement Bagging (Breiman, 1996) , the Random Subspace Method (Ho, 1998)
, or both, and use Hastie and Tibshirani's (1990, ISBN:978-0412343902) generalized additive models (GAMs)
as base classifiers. Once an ensemble classifier has been trained, it can
be used for predictions on new data. A function for cross validation is also
included.