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.


Reference manual

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2.1.5 by Brandon Greenwell, a year ago

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Authors: Brandon Greenwell [aut, cre] , Bradley Boehmke [aut] , Jay Cunningham [aut] , GBM Developers [aut] (

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning, Survival Analysis

GPL (>= 2) | file LICENSE license

Imports gridExtra, lattice, parallel, survival

Suggests knitr, pdp, RUnit, splines, viridis

Imported by EZtune, EnsembleBase, IPMRF, MiDA, Plasmode, SDMPlay, SDMtune, SSDM, aurelius, biomod2, bst, bujar, ebirdst, gbts, horserule, inTrees, lilikoi, mvtboost, regressoR, scorecardModelUtils, spm, statVisual, tsensembler.

Depended on by BigTSP, ecospat, gbm2sas, mma, personalized, twang.

Suggested by BiodiversityR, DALEX, DALEXtra, MachineShop, SuperLearner, WeightIt, caretEnsemble, creditmodel, crimelinkage, dismo, featurefinder, fscaret, imputeR, insight, mboost, mlr, opera, pdp, plotmo, pmml, preprosim, riskRegression, vip.

See at CRAN