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.
| Version: | 1.1.1.1 |
| 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, lintr, igraph (≥ 1.0.1), jsonlite, float |
| Published: | 2020-06-14 |
| Author: | Tianqi Chen [aut],
Tong He [aut, cre],
Michael Benesty [aut],
Vadim Khotilovich [aut],
Yuan Tang  [aut],
Hyunsu Cho [aut],
Kailong Chen [aut],
Rory Mitchell [aut],
Ignacio Cano [aut],
Tianyi Zhou [aut],
Mu Li [aut],
Junyuan Xie [aut],
Min Lin [aut],
Yifeng Geng [aut],
Yutian Li [aut],
XGBoost contributors [cph] (base XGBoost implementation) |
| 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 |
| SystemRequirements: | GNU make, C++11 |
| In views: | HighPerformanceComputing, MachineLearning, ModelDeployment |
| CRAN checks: | xgboost results |
| Reference manual: | xgboost.pdf |
| Vignettes: |
Discover your data Xgboost presentation XGBoost from JSON xgboost: eXtreme Gradient Boosting |
| Package source: | xgboost_1.1.1.1.tar.gz |
| Windows binaries: | r-devel: xgboost_1.1.1.1.zip, r-release: xgboost_1.1.1.1.zip, r-oldrel: xgboost_1.1.1.1.zip |
| macOS binaries: | r-release: xgboost_1.1.1.1.tgz, r-oldrel: xgboost_1.1.1.1.tgz |
| Old sources: | xgboost archive |
| Reverse imports: | adapt4pv, autoBagging, blkbox, causalweight, ccmap, creditmodel, dblr, EIX, expose, fdm2id, FLAME, GeneralisedCovarianceMeasure, GNET2, healthcareai, inTrees, KOBT, modeltime, nsga3, oncrawlR, predictoR, radiant.model, regressoR, rminer, scds, SELF, SHAPforxgboost, traineR, trena, wactor, xgb2sql, xrf |
| Reverse suggests: | bigsnpr, biotmle, Boruta, breakDown, butcher, CBDA, coefplot, DALEXtra, DriveML, embed, fastshap, FeatureHashing, flashlight, forecastML, GSIF, lime, MachineShop, mlr, mlr3learners, modelplotr, modelStudio, nlpred, ParBayesianOptimization, parsnip, pdp, pmml, r2pmml, rattle, rBayesianOptimization, SSLR, SuperLearner, superml, tidypredict, utiml, vimp, vip, xspliner |
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