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.

Tests Vignettes

Available Snapshots

This version of gbm can be found in the following snapshots:


Imports/Depends/LinkingTo/Enhances (1)
  • R
  • Suggests (8)
  • covr
  • gridExtra
  • knitr
  • pdp
  • RUnit
  • tinytest
  • vip
  • viridis
  • Version History