xgboost (0.3-0)

eXtreme Gradient Boosting.


This package is a R wrapper of xgboost, which is short for eXtreme Gradient Boosting. It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. 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 user are also allowed to define there own objectives easily.

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

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

Uses: Matrix
Reverse suggests: Boruta, breakDown, butcher, CBDA, coefplot, DALEX, FeatureHashing, flashlight, forecastML, GSIF, iBreakDown, ingredients, lime, MachineShop, mlr, mlr3learners, modelplotr, nlpred, ParBayesianOptimization, parsnip, pdp, pmml, r2pmml, rattle, rBayesianOptimization, SuperLearner, tidypredict, utiml, vimp, vip, xspliner

Released about 5 years ago.