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.


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Reference manual

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install.packages("xgboost")

0.6-4 by Tong He, a year ago


https://github.com/dmlc/xgboost


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, lime, mlr, pdp, pmml, rBayesianOptimization, rattle, utiml.


See at CRAN