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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.
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Package details |
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| Author | Greg Ridgeway [aut, cre] (<https://orcid.org/0000-0001-6911-0804>), Daniel Edwards [ctb], Brian Kriegler [ctb], Stefan Schroedl [ctb], Harry Southworth [ctb], Brandon Greenwell [ctb] (<https://orcid.org/0000-0002-8120-0084>), Bradley Boehmke [ctb] (<https://orcid.org/0000-0002-3611-8516>), Jay Cunningham [ctb], GBM Developers [aut] (https://github.com/gbm-developers) |
| Maintainer | Greg Ridgeway <gridge@upenn.edu> |
| License | GPL (>= 2) | file LICENSE |
| Version | 2.2.2 |
| URL | https://github.com/gbm-developers/gbm |
| Package repository | View on CRAN |
| Installation |
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