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 |
| 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 |
| 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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