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.


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Reference manual

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install.packages("gbm")

2.1.5 by Brandon Greenwell, a year ago


https://github.com/gbm-developers/gbm


Report a bug at https://github.com/gbm-developers/gbm/issues


Browse source code at https://github.com/cran/gbm


Authors: Brandon Greenwell [aut, cre] , Bradley Boehmke [aut] , Jay Cunningham [aut] , GBM Developers [aut] (https://github.com/gbm-developers)


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