gbm-package: Generalized Boosted Regression Models (GBMs)
In gbm-developers/gbm: Generalized Boosted Regression Models
gbm-package R Documentation
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 gridge@upenn.edu 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:
gbm-developers/gbm documentation built on Feb. 16, 2024, 6:13 p.m.
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| gbm-package | R Documentation |
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 gridge@upenn.edu 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:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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