gbm

Generalized Boosted Regression Models

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).

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Description file content

Package
gbm
Version
2.1.3
Date
2017-03-21
Title
Generalized Boosted Regression Models
Author
Greg Ridgeway with contributions from others
Maintainer
ORPHANED
Depends
R (>= 2.9.0), survival, lattice, splines, parallel
Suggests
RUnit
Description
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).
License
GPL (>= 2) | file LICENSE
URL
Packaged
2017-03-21 06:44:01 UTC; ripley
NeedsCompilation
yes
Repository
CRAN
Date/Publication
2017-03-21 06:48:03 UTC
X-CRAN-Original-Maintainer
Harry Southworth
X-CRAN-Comment
Orphaned on 2017-03-21 as long-standing errors were not corrected. NMU by CRAN team.

install.packages('gbm')

2.1.3

a year ago

http://code.google.com/p/gradientboostedmodels/

ORPHANED

GPL (>= 2) | file LICENSE

Depends on

R (>= 2.9.0), survival, lattice, splines, parallel

Suggests

RUnit

Discussions