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gbm

Overview

The gbm package (which stands for generalized boosted models) implements extensions to Freund and Schapire’s AdaBoost algorithm and Friedman’s gradient boosting machine. It 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 (i.e., LambdaMart).

Installation

# The easiest way to get gbm is to it install from CRAN:
install.packages("gbm")

# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("gbm-developers/gbm")

Lifecycle

The gbm package is retired and no longer under active development. We will only make the necessary changes to ensure that gbm remain on CRAN. For the most part, no new features will be added, and only the most critical of bugs will be fixed.

This is a maintained version of gbm back compatible to CRAN versions of gbm 2.1.x. It exists mainly for the purpose of reproducible research and data analyses performed with the 2.1.x versions of gbm. For newer development, and a more consistent API, try out the gbm3 package!

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Install

install.packages('gbm')

Monthly Downloads

42,603

Version

2.1.4

License

GPL (>= 2) | file LICENSE

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Last Published

September 16th, 2018

Functions in gbm (2.1.4)