xgboost (0.3-3)

eXtreme Gradient Boosting.


Xgboost is short for eXtreme Gradient Boosting, which is an efficient and scalable implementation of gradient boosting framework. This package is an R wrapper of xgboost. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm. It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that users are also allowed to define their own objectives easily.

Maintainer: Tong He
Author(s): Tianqi Chen <tianqi.tchen@gmail.com>, Tong He <hetong007@gmail.com>, Michael Benesty <michael@benesty.fr>

License: Apache License (== 2.0) | file LICENSE

Uses: Does not use any package
Reverse suggests: Boruta, breakDown, butcher, CBDA, coefplot, DALEX, FeatureHashing, GSIF, iBreakDown, ingredients, lime, MachineShop, mlr, mlr3learners, modelplotr, ParBayesianOptimization, parsnip, pdp, pmml, r2pmml, rattle, rBayesianOptimization, SuperLearner, tidypredict, utiml, vimp, vip, xspliner

Released over 4 years ago.