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
DOI: 10.32614/CRAN.package.gbts
Author: Waley W. J. Liang
Maintainer: Waley W. J. Liang <wliang10 at>
License: GPL-2 | GPL-3 | file LICENSE [expanded from: GPL (≥ 2) | file LICENSE]
NeedsCompilation: no
Materials: README NEWS
CRAN checks: gbts results


Reference manual: gbts.pdf


Package source: gbts_1.2.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): gbts_1.2.0.tgz, r-oldrel (arm64): gbts_1.2.0.tgz, r-release (x86_64): gbts_1.2.0.tgz, r-oldrel (x86_64): gbts_1.2.0.tgz
Old sources: gbts archive


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