xgboost: Extreme Gradient Boosting

Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>. 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.

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)
Published: 2018-01-23
Author: Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang
Maintainer: Tong He <hetong007 at gmail.com>
BugReports: https://github.com/dmlc/xgboost/issues
License: Apache License (== 2.0) | file LICENSE
URL: https://github.com/dmlc/xgboost
NeedsCompilation: yes
In views: HighPerformanceComputing, MachineLearning
CRAN checks: xgboost results


Reference manual: xgboost.pdf
Vignettes: Discover your data
Xgboost presentation
xgboost: eXtreme Gradient Boosting
Package source: xgboost_0.6.4.1.tar.gz
Windows binaries: r-devel: xgboost_0.6.4.1.zip, r-release: xgboost_0.6.4.1.zip, r-oldrel: xgboost_0.6.4.1.zip
OS X El Capitan binaries: r-release: xgboost_0.6.4.1.tgz
OS X Mavericks binaries: r-oldrel: xgboost_0.6-4.tgz
Old sources: xgboost archive

Reverse dependencies:

Reverse imports: autoBagging, blkbox, dblr, healthcareai, iqspr, MlBayesOpt, rminer, SELF, SSL
Reverse suggests: coefplot, FeatureHashing, GSIF, lime, mlr, pdp, pmml, rattle, rBayesianOptimization, SuperLearner, utiml


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