gbm-package: Generalized Boosted Regression Models (GBMs)

In gbm: Generalized Boosted Regression Models

Generalized Boosted Regression Models (GBMs)

Description

This package implements extensions to Freund and Schapire's AdaBoost algorithm and J. Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, logistic, Poisson, Cox proportional hazards partial likelihood, multinomial, t-distribution, AdaBoost exponential loss, Learning to Rank, and Huberized hinge loss. This gbm package is no longer under further development. Consider https://github.com/gbm-developers/gbm3 for the latest version.

Details

Further information is available in vignette: browseVignettes(package = "gbm")

Author(s)

Greg Ridgeway [e-mail] with contributions by Daniel Edwards, Brian Kriegler, Stefan Schroedl, Harry Southworth, and Brandon Greenwell

References

Y. Freund and R.E. Schapire (1997) “A decision-theoretic generalization of on-line learning and an application to boosting,” Journal of Computer and System Sciences, 55(1):119-139.

G. Ridgeway (1999). “The state of boosting,” Computing Science and Statistics 31:172-181.

J.H. Friedman, T. Hastie, R. Tibshirani (2000). “Additive Logistic Regression: a Statistical View of Boosting,” Annals of Statistics 28(2):337-374.

J.H. Friedman (2001). “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics 29(5):1189-1232.

J.H. Friedman (2002). “Stochastic Gradient Boosting,” Computational Statistics and Data Analysis 38(4):367-378.

The MART website.

See Also

Useful links:

• https://github.com/gbm-developers/gbm

• Report bugs at https://github.com/gbm-developers/gbm/issues

gbm documentation built on June 28, 2024, 9:07 a.m.