gbts: Hyperparameter Search for Gradient Boosted Trees

An implementation of hyperparameter optimization for Gradient Boosted Trees on binary classification and regression problems. The current version provides two optimization methods: Bayesian optimization and random search. Instead of giving the single best model, the final output is an ensemble of Gradient Boosted Trees constructed via the method of ensemble selection.

Version: 1.2.0
Depends: R (≥ 3.3.0)
Imports: doParallel, doRNG, foreach, gbm, earth
Suggests: testthat
Published: 2017-02-27
Author: Waley W. J. Liang
Maintainer: Waley W. J. Liang <wliang10 at gmail.com>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: gbts results

Downloads:

Reference manual: gbts.pdf
Package source: gbts_1.2.0.tar.gz
Windows binaries: r-devel: gbts_1.2.0.zip, r-release: gbts_1.2.0.zip, r-oldrel: gbts_1.2.0.zip
OS X El Capitan binaries: r-release: gbts_1.2.0.tgz
OS X Mavericks binaries: r-oldrel: gbts_1.2.0.tgz
Old sources: gbts archive

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