gbm (2.1.5)

3 users

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.

Maintainer: Brandon M. Greenwell
Author(s): Brandon Greenwell [aut, cre] (<>), Bradley Boehmke [aut] (<>), Jay Cunningham [aut], GBM Developers [aut] (

License: GPL (>= 2) | file LICENSE

Uses: gridExtra, lattice, survival, RUnit, knitr, viridis, pdp
Reverse depends: BigTSP, biomod2, bst, bujar, CompModSA, ecospat, gbm2sas, imputation, mma, ModelMap, mseq, personalized, soil.spec, twang
Reverse suggests: AzureML, BiodiversityR, biomod2, caret, caretEnsemble, creditmodel, crimelinkage, DALEX, DALEXtra, dismo, featurefinder, fscaret, imputeR, ingredients, insight, MachineShop, mboost, mlr, mlr3proba, modelcf, ModelMap, opera, pdp, plotmo, pmml, preprosim, riskRegression, soil.spec, SuperLearner, vip, WeightIt

Released over 1 year ago.

10 previous versions



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