An implementation of extensions to Freund and Schapire’s AdaBoost algorithm and Friedman’s gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.

Home https://github.com/gbm-developers/gbm
Versions 2.1.3, 2.1.1
License GPL (>= 2)
Recipe https://github.com/bioconda/bioconda-recipes/tree/master/recipes/r-gbm


With an activated Bioconda channel (see 2. Set up channels), install with:

conda install r-gbm

and update with:

conda update r-gbm


A Docker container is available at https://quay.io/repository/biocontainers/r-gbm.