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.

Documentation

Manual: gbts.pdf
Vignette: None available.

Maintainer: Waley W. J. Liang <wliang10 at gmail.com>

Author(s): Waley W. J. Liang

Install package and any missing dependencies by running this line in your R console:

install.packages("gbts")

Depends R (>= 3.3.0)
Imports doParallel, doRNG, foreach, gbm, earth
Suggests testthat
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Package gbts
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Version 1.2.0
Published 2017-02-27
License GPL (>= 2) | file LICENSE
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NeedsCompilation no
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CRAN checks gbts check results
Package source gbts_1.2.0.tar.gz