gbm-package: 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, and AdaBoost exponential loss.Details
ll{
Package: gbm
Version: 1.5-6
Date: 2006-1-20
Depends: R (>= 2.1.0), survival, lattice, mgcv
License: GPL (version 2 or newer)
URL: http://www.i-pensieri.com/gregr/gbm.shtml
Built: R 2.2.1; i386-pc-mingw32; 2006-02-24 18:09:42; windows
}
Index:
basehaz.gbm Baseline hazard function
calibrate.plot Calibration plot
gbm Generalized Boosted Regression Modeling
gbm.object Generalized Boosted Regression Model Object
gbm.perf GBM performance
plot.gbm Marginal plots of fitted gbm objects
predict.gbm Predict method for GBM Model Fits
pretty.gbm.tree Print gbm tree components
quantile.rug Quantile rug plot
relative.influence Methods for estimating relative influence
shrink.gbm L1 shrinkage of the predictor variables in a
GBM
shrink.gbm.pred Predictions from a shrunked GBM
summary.gbm Summary of a gbm object
Further information is available in the following vignettes:
ll{
gbm Generalized Boosted Models: A guide to the gbm package (source, pdf)
}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.
http://www.i-pensieri.com/gregr/gbm.shtml
http://www-stat.stanford.edu/~jhf/R-MART.html