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. Newer version available at github.com/gbm-developers/gbm3.
- Version2.2.2
- R version≥ 2.9.0
- LicenseGPL-2
- LicenseGPL-3
- LicenseLICENSE
- Needs compilation?Yes
- gbm.pdf
- Generalized Boosted Models: A guide to the gbm package
- Last release06/28/2024
Documentation
Team
Greg Ridgeway
Bradley Boehmke
Show author detailsRolesContributorBrandon Greenwell
Show author detailsRolesContributorStefan Schroedl
Show author detailsRolesContributorHarry Southworth
Show author detailsRolesContributorDaniel Edwards
Show author detailsRolesContributorBrian Kriegler
Show author detailsRolesContributorJay Cunningham
Show author detailsRolesContributorGBM Developers
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- Imports2 packages
- Suggests8 packages
- Reverse Depends2 packages
- Reverse Imports52 packages
- Reverse Suggests41 packages
- Reverse Enhances1 package