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.

Maintainer: Tong He <hetong007 at gmail.com>

Author(s): Tianqi Chen <tianqi.tchen at gmail.com>, Tong He <hetong007 at gmail.com>, Michael Benesty <michael at benesty.fr>, Vadim Khotilovich <khotilovich at gmail.com>, Yuan Tang <terrytangyuan at gmail.com>

Install package and any missing dependencies by running this line in your R console:

install.packages("xgboost")

Depends R (>= 3.3.0)
Imports Matrix(>=1.1-0), methods, data.table(>=1.9.6), magrittr(>=1.5), stringi(>=0.5.2)
Suggests knitr, rmarkdown, ggplot2(>=1.0.1), DiagrammeR(>=0.9.0), Ckmeans.1d.dp(>=3.3.1), vcd(>=1.3), testthat, igraph(>=1.0.1)
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blkbox, rminer, SSL
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FeatureHashing, GSIF, mlr, pdp, pmml, rBayesianOptimization, SuperLearner, utiml
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Package xgboost
Materials
URL https://github.com/dmlc/xgboost
Task Views MachineLearning
Version 0.6-4
Published 2017-01-05
License Apache License (== 2.0) | file LICENSE
BugReports https://github.com/dmlc/xgboost/issues
SystemRequirements
NeedsCompilation yes
Citation
CRAN checks xgboost check results
Package source xgboost_0.6-4.tar.gz