gbm-package: Generalized Boosted Regression Models

In gbm-developers/gbm3: Generalized Boosted Regression Models

Generalized Boosted Regression Models

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, t-distribution, AdaBoost exponential loss, Learning to Rank, and Huberized hinge loss.

Details

Further information is available in the following vignettes:

gbm	Generalized Boosted Models: A guide to the gbm package (source, pdf)

Author(s)

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

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.

gbm-developers/gbm3 documentation built on March 8, 2024, 4:48 p.m.