Package: gbm 2.2.2

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_2.2.2.tar.gz
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gbm.pdf |gbm.html✨
gbm/json (API)
NEWS

# 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

14.05 score 52 stars 91 packages 6.8k scripts 19k downloads 250 mentions 42 exports 3 dependencies

Last updated 8 months agofrom:59cc7a9592. Checks:1 OK, 10 NOTE. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 10 2025
R-4.5-win-x86_64NOTEFeb 10 2025
R-4.5-mac-x86_64NOTEFeb 10 2025
R-4.5-mac-aarch64NOTEFeb 10 2025
R-4.5-linux-x86_64NOTEFeb 10 2025
R-4.4-win-x86_64NOTEFeb 10 2025
R-4.4-mac-x86_64NOTEFeb 10 2025
R-4.4-mac-aarch64NOTEFeb 10 2025
R-4.3-win-x86_64NOTEFeb 10 2025
R-4.3-mac-x86_64NOTEFeb 10 2025
R-4.3-mac-aarch64NOTEFeb 10 2025

Exports:basehaz.gbmcalibrate.plotcheckIDcheckMissingcheckOffsetcheckWeightsgbmgbm.concgbm.fitgbm.lossgbm.moregbm.perfgbm.roc.areagbmClustergbmCrossValgbmCrossValErrgbmCrossValModelBuildgbmCrossValPredictionsgbmDoFoldgetCVgroupgetStratifygetVarNamesguessDistinteract.gbmir.measure.aucir.measure.concir.measure.mapir.measure.mrrir.measure.ndcgperf.pairwisepermutation.test.gbmplot.gbmpredict.gbmpretty.gbm.treequantile.rugreconstructGBMdatarelative.influenceshow.gbmsummary.gbmtest.gbmtest.relative.influencevalidate.gbm

Dependencies:latticeMatrixsurvival

Generalized Boosted Models: A guide to the gbm package

Rendered fromgbm.Rnwusingknitr::knitron Feb 10 2025.

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

Readme and manuals

Help Manual

Help pageTopics
Generalized Boosted Regression Models (GBMs)gbm-package
Baseline hazard functionbasehaz.gbm
Calibration plotcalibrate.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 Objectgbm.object
GBM performancegbm.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 gbmgbmCrossVal gbmCrossValErr gbmCrossValModelBuild gbmCrossValPredictions gbmDoFold
gbm internal functionscheckID checkMissing checkOffset checkWeights gbmCluster getCVgroup getStratify getVarNames guessDist
Estimate the strength of interaction effectsinteract.gbm
Marginal plots of fitted gbm objectsplot.gbm
Predict method for GBM Model Fitspredict.gbm
Print gbm tree componentspretty.gbm.tree
Print model summaryprint.gbm show.gbm
Quantile rug plotquantile.rug
Reconstruct a GBM's Source DatareconstructGBMdata
Methods for estimating relative influencegbm.loss permutation.test.gbm relative.influence
Summary of a gbm objectsummary.gbm
Test the 'gbm' package.test.gbm test.relative.influence validate.gbm