Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) . This package is its R interface. The package includes efficient linear model solver and tree learning algorithms. The package can automatically do parallel computation on a single machine which could be more than 10 times faster than existing gradient boosting packages. 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.


Reference manual

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0.6-4 by Tong He, a year ago


Report a bug at https://github.com/dmlc/xgboost/issues

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

Authors: Tianqi Chen <tianqi.tchen@gmail.com>, Tong He <hetong007@gmail.com>, Michael Benesty <michael@benesty.fr>, Vadim Khotilovich <khotilovich@gmail.com>, Yuan Tang <terrytangyuan@gmail.com>

Documentation:   PDF Manual  

Task views: Machine Learning & Statistical Learning

Apache License (== 2.0) | file LICENSE license

Imports Matrix, methods, data.table, magrittr, stringi

Suggests knitr, rmarkdown, ggplot2, DiagrammeR, Ckmeans.1d.dp, vcd, testthat, igraph

Imported by MlBayesOpt, SELF, SSL, autoBagging, blkbox, dblr, healthcareai, iqspr, rminer.

Suggested by FeatureHashing, GSIF, SuperLearner, coefplot, lime, mlr, pdp, pmml, rBayesianOptimization, rattle, utiml.

See at CRAN