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gbm

Overview

The gbm package, which stands for generalized boosted models, provides 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")

# Alternatively, you can install the development version from GitHub:
if (!requireNamespace("remotes")) {
  install.packages("remotes")
}
remotes::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 remains 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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Version

Install

install.packages('gbm')

Monthly Downloads

23,852

Version

2.1.8

License

GPL (>= 2) | file LICENSE

Issues

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Last Published

July 15th, 2020

Functions in gbm (2.1.8)

gbm.fit

Generalized Boosted Regression Modeling (GBM)
guessDist

gbm internal functions
gbm.perf

GBM performance
gbm

Generalized Boosted Regression Modeling (GBM)
gbm.more

Generalized Boosted Regression Modeling (GBM)
calibrate.plot

Calibration plot
gbm.object

Generalized Boosted Regression Model Object
basehaz.gbm

Baseline hazard function
gbm-package

Generalized Boosted Regression Models (GBMs)
pretty.gbm.tree

Print gbm tree components
gbm.roc.area

Compute Information Retrieval measures.
reconstructGBMdata

Reconstruct a GBM's Source Data
summary.gbm

Summary of a gbm object
relative.influence

Methods for estimating relative influence
quantile.rug

Quantile rug plot
print.gbm

Print model summary
plot.gbm

Marginal plots of fitted gbm objects
predict.gbm

Predict method for GBM Model Fits
gbmCrossVal

Cross-validate a gbm
interact.gbm

Estimate the strength of interaction effects
test.gbm

Test the gbm package.