An implementation of extensions to Freund and Schapire's AdaBoost algorithm and Friedman's gradient boosting machine. Includes regression methods for least squares, absolute loss, t-distribution loss, quantile regression, logistic, multinomial logistic, Poisson, Cox proportional hazards partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and Learning to Rank measures (LambdaMart).

Documentation

Manual: gbm.pdf
Vignette: None available.

Maintainer: ORPHANED

Author(s): Greg Ridgeway <gregridgeway at gmail.com> with contributions from others

Install package and any missing dependencies by running this line in your R console:

install.packages("gbm")

Depends R (>= 2.9.0), survival, lattice, splines, parallel
Imports
Suggests RUnit
Enhances
Linking to

Package gbm
Materials
URL http://code.google.com/p/gradientboostedmodels/
Task Views MachineLearning , Survival
Version 2.1.3
Published 2017-03-21
License GPL (>= 2) | file LICENSE
BugReports
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks gbm check results
Package source gbm_2.1.3.tar.gz