GAMens (1.2.1)

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

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.

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.

6 previous versions



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