gbm v2.1.8

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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). Originally developed by Greg Ridgeway.

Readme

gbm

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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

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!

Functions in gbm

Name Description
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.
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Vignettes of gbm

Name
gbm-concordance.tex
gbm.Rnw
gbm.bib
oobperf2.pdf
shrinkage-v-iterations.pdf
srcltx.sty
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Details

License GPL (>= 2) | file LICENSE
URL https://github.com/gbm-developers/gbm
BugReports https://github.com/gbm-developers/gbm/issues
Encoding UTF-8
RoxygenNote 7.1.1
VignetteBuilder knitr
NeedsCompilation yes
Packaged 2020-07-13 15:15:55 UTC; b780620
Repository CRAN
Date/Publication 2020-07-15 10:00:02 UTC

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