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).
Downloads:
Reverse dependencies:
Reverse depends: |
BigTSP, ecospat, gbm2sas, mma, ModelMap, twang |
Reverse imports: |
biomod2, bst, bujar, EnsembleBase, imputeR, inTrees, mvtboost |
Reverse suggests: |
BiodiversityR, caretEnsemble, crimelinkage, dismo, fscaret, mboost, mlr, subsemble, SuperLearner |