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
- Licensefile LICENSE
- Needs compilation?Yes
- Last release06/28/2024
Documentation
Team
Greg Ridgeway
Daniel Edwards
Show author detailsRolesContributorBrian Kriegler
Show author detailsRolesContributorStefan Schroedl
Show author detailsRolesContributorHarry Southworth
Show author detailsRolesContributorBrandon Greenwell
Bradley Boehmke
Jay Cunningham
Show author detailsRolesContributorGBM Developers
Show author detailsRolesAuthor
Insights
Last 30 days
Last 365 days
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Depends1 package
- Imports3 packages
- Suggests9 packages
- Reverse Depends2 packages
- Reverse Imports54 packages
- Reverse Suggests43 packages
- Reverse Enhances1 package