gbm v2.1.5

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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) 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

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!

Functions in gbm

Name Description
gbm.roc.area Compute Information Retrieval measures.
gbmCrossVal Cross-validate a gbm
plot.gbm Marginal plots of fitted gbm objects
interact.gbm Estimate the strength of interaction effects
predict.gbm Predict method for GBM Model Fits
pretty.gbm.tree Print gbm tree components
relative.influence Methods for estimating relative influence
reconstructGBMdata Reconstruct a GBM's Source Data
grid.arrange Arrange multiple grobs on a page
print.gbm Print model summary
summary.gbm Summary of a gbm object
quantile.rug Quantile rug plot
test.gbm Test the gbm package.
shrink.gbm L1 shrinkage of the predictor variables in a GBM
shrink.gbm.pred Predictions from a shrunked GBM
gbm Generalized Boosted Regression Modeling (GBM)
gbm.fit Generalized Boosted Regression Modeling (GBM)
gbm.more Generalized Boosted Regression Modeling (GBM)
gbm.object Generalized Boosted Regression Model Object
guessDist gbm internal functions
gbm-package Generalized Boosted Regression Models (GBMs)
gbm.perf GBM performance
basehaz.gbm Baseline hazard function
calibrate.plot Calibration plot
No Results!

Vignettes of gbm

Name
gbm.Rnw
gbm.bib
oobperf2.pdf
shrinkage-v-iterations.pdf
srcltx.sty
No Results!

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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
RoxygenNote 6.1.1
VignetteBuilder knitr
NeedsCompilation yes
Packaged 2019-01-14 14:21:52 UTC; bgreenwell
Repository CRAN
Date/Publication 2019-01-14 15:00:03 UTC

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