GAMens (1.2)

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Applies GAMbag, GAMrsm and GAMens Ensemble Classifiers for Binary Classification.

Ensemble classifiers based upon generalized additive models 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) 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.

Maintainer: Koen W. De Bock
Author(s): Koen W. De Bock, Kristof Coussement and Dirk Van den Poel

License: GPL (>= 2)

Uses: caTools, gam, mlbench
Reverse suggests: caret

Released over 1 year ago.

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