R: Generalized Boosted Regression Models

gbm-package {gbm} | R Documentation

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

### Details

Package: | gbm
Version: | 2.1
Date: | 2013-05-10
Depends: | R (>= 2.9.0), survival, lattice, mgcv
License: | GPL (version 2 or newer)
URL: | http://code.google.com/p/gradientboostedmodels/

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:

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

### Author(s)

Greg Ridgeway gregridgeway@gmail.com 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.

[Package gbm version 2.1.3 Index]