Package: gbm 2.2.3

Greg Ridgeway

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). Originally developed by Greg Ridgeway. Newer version available at github.com/gbm-developers/gbm3.

Authors:Greg Ridgeway [aut, cre], Daniel Edwards [ctb], Brian Kriegler [ctb], Stefan Schroedl [ctb], Harry Southworth [ctb], Brandon Greenwell [ctb], Bradley Boehmke [ctb], Jay Cunningham [ctb], GBM Developers [aut]

gbm_2.2.3.tar.gz
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gbm_2.2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
gbm/json (API)

# Install 'gbm' in R:

install.packages('gbm', repos = c('https://gbm-developers.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/gbm-developers/gbm/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

cpp

14.37 score 54 stars 97 packages 11k scripts 44k downloads 250 mentions 42 exports 3 dependencies

Last updated from:2ca3eed3b7. Checks:2 ERROR, 11 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	ERROR	164
linux-devel-x86_64	OK	195
source / vignettes	OK	210
linux-release-arm64	ERROR	167
linux-release-x86_64	OK	165
macos-release-arm64	OK	123
macos-release-x86_64	OK	278
macos-oldrel-arm64	OK	141
macos-oldrel-x86_64	OK	279
windows-devel	OK	154
windows-release	OK	159
windows-oldrel	OK	153
wasm-release	OK	119

Exports:basehaz.gbm calibrate.plot checkID checkMissing checkOffset checkWeights gbm gbm.conc gbm.fit gbm.loss gbm.more gbm.perf gbm.roc.area gbmCluster gbmCrossVal gbmCrossValErr gbmCrossValModelBuild gbmCrossValPredictions gbmDoFold getCVgroup getStratify getVarNames guessDist interact.gbm ir.measure.auc ir.measure.conc ir.measure.map ir.measure.mrr ir.measure.ndcg perf.pairwise permutation.test.gbm plot.gbm predict.gbm pretty.gbm.tree quantile.rug reconstructGBMdata relative.influence show.gbm summary.gbm test.gbm test.relative.influence validate.gbm

Dependencies:lattice Matrix survival

Generalized Boosted Models: A guide to the gbm package

Rendered fromgbm.Rnwusingknitr::knitr

Last update: 2017-10-25
Started: 2017-10-25

Help page	Topics
Generalized Boosted Regression Models (GBMs)	gbm-package
Baseline hazard function	basehaz.gbm
Calibration plot	calibrate.plot
Generalized Boosted Regression Modeling (GBM)	gbm
Generalized Boosted Regression Modeling (GBM)	gbm.fit
Generalized Boosted Regression Modeling (GBM)	gbm.more
Generalized Boosted Regression Model Object	gbm.object
GBM performance	gbm.perf
Compute Information Retrieval measures.	gbm.conc gbm.roc.area ir.measure.auc ir.measure.conc ir.measure.map ir.measure.mrr ir.measure.ndcg perf.pairwise
Cross-validate a gbm	gbmCrossVal gbmCrossValErr gbmCrossValModelBuild gbmCrossValPredictions gbmDoFold
gbm internal functions	checkID checkMissing checkOffset checkWeights gbmCluster getCVgroup getStratify getVarNames guessDist
Estimate the strength of interaction effects	interact.gbm
Marginal plots of fitted gbm objects	plot.gbm
Predict method for GBM Model Fits	predict.gbm
Print gbm tree components	pretty.gbm.tree
Print model summary	print.gbm show.gbm
Quantile rug plot	quantile.rug
Reconstruct a GBM's Source Data	reconstructGBMdata
Methods for estimating relative influence	gbm.loss permutation.test.gbm relative.influence
Summary of a gbm object	summary.gbm
Test the 'gbm' package.	test.gbm test.relative.influence validate.gbm

Package: gbm 2.2.3

gbm: Generalized Boosted Regression Models

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