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)|
|Author:||Tianqi Chen, Tong He, Michael Benesty, Vadim Khotilovich, Yuan Tang|
|Maintainer:||Tong He <hetong007 at gmail.com>|
|License:||Apache License (== 2.0) | file LICENSE|
|CRAN checks:||xgboost results|
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xgboost: eXtreme Gradient Boosting
|Windows binaries:||r-devel: xgboost_0.6-4.zip, r-release: xgboost_0.6-4.zip, r-oldrel: xgboost_0.4-4.zip|
|OS X Mavericks binaries:||r-release: xgboost_0.6-4.tgz, r-oldrel: xgboost_0.6-2.tgz|
|Old sources:||xgboost archive|
|Reverse imports:||blkbox, gbts, rminer, SSL|
|Reverse suggests:||FeatureHashing, GSIF, mlr, pdp, rBayesianOptimization, SuperLearner, utiml|
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