gbm: Generalized Boosted Regression Models

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.

Package details

AuthorBrandon Greenwell [aut, cre] (<>), Bradley Boehmke [aut] (<>), Jay Cunningham [aut], GBM Developers [aut] (
MaintainerBrandon Greenwell <>
LicenseGPL (>= 2) | file LICENSE
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:

Try the gbm package in your browser

Any scripts or data that you put into this service are public.

gbm documentation built on July 15, 2020, 5:08 p.m.