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xgboost

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Extreme Gradient Boosting

View on CRAN: Click here

Download and install xgboost package within the R console
Install from CRAN:

 install.packages("xgboost")

Install from Github:

 library("remotes")

                        install_github("cran/xgboost")

Install by package version:

 library("remotes")

                        install_version("xgboost", "1.7.7.1")

Attach the package and use:

 library("xgboost")

Maintained by
Jiaming Yuan
[Scholar Profile | Author Map]

All associated links for this package

First Published: 2014-09-01

Latest Update: 2023-12-06

Description:

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.

How to cite:

Jiaming Yuan (2014). xgboost: Extreme Gradient Boosting. R package version 1.7.7.1, https://cran.r-project.org/web/packages/xgboost

Previous versions and publish date:

0.3-0 (2014-09-01 19:47), 0.3-1 (2014-09-07 09:26), 0.3-2 (2014-09-07 21:54), 0.3-3 (2015-03-03 11:05), 0.4-1 (2015-08-01 01:24), 0.4-2 (2015-08-02 08:23), 0.4-3 (2016-02-15 14:41), 0.4-4 (2016-07-12 10:55), 0.6-0 (2016-12-16 09:25), 0.6-2 (2016-12-18 11:23), 0.6-3 (2016-12-31 22:01), 0.6-4 (2017-01-05 10:40), 0.6.4.1 (2018-01-23 22:57), 0.71.1 (2018-05-16 07:18), 0.71.2 (2018-06-09 06:24), 0.81.0.1 (2019-01-31 10:10), 0.82.1 (2019-03-12 00:40), 0.90.0.1 (2019-07-25 22:40), 0.90.0.2 (2019-08-01 21:20), 1.0.0.1 (2020-03-23 09:00), 1.0.0.2 (2020-03-25 15:10), 1.1.1.1 (2020-06-14 16:40), 1.2.0.1 (2020-09-02 07:40), 1.3.1.1 (2021-01-05 21:00), 1.3.2.1 (2021-01-18 11:10), 1.4.1.1 (2021-04-22 11:20), 1.5.0.1 (2021-11-08 09:00), 1.5.0.2 (2021-11-21 17:30), 1.5.2.1 (2022-02-21 11:00), 1.6.0.1 (2022-04-16 17:50), 1.7.3.1 (2023-01-14 22:20), 1.7.5.1 (2023-03-30 21:40), 1.7.6.1 (2023-12-06 09:50)

Other packages that cited xgboost R package

View xgboost citation profile

Other R packages that xgboost depends, imports, suggests or enhances

View xgboost dependency map

Functions, R codes and Examples using the xgboost R package

Some associated functions: a-compatibility-note-for-saveRDS-save . agaricus.test . agaricus.train . callbacks . cb.cv.predict . cb.early.stop . cb.evaluation.log . cb.gblinear.history . cb.print.evaluation . cb.reset.parameters . cb.save.model . dim.xgb.DMatrix . dimnames.xgb.DMatrix . getinfo . normalize . predict.xgb.Booster . prepare.ggplot.shap.data . print.xgb.Booster . print.xgb.DMatrix . print.xgb.cv . setinfo . slice.xgb.DMatrix . xgb.Booster.complete . xgb.DMatrix . xgb.DMatrix.save . xgb.attr . xgb.config . xgb.create.features . xgb.cv . xgb.dump . xgb.gblinear.history . xgb.importance . xgb.load . xgb.load.raw . xgb.model.dt.tree . xgb.parameters . xgb.plot.deepness . xgb.plot.importance . xgb.plot.multi.trees . xgb.plot.shap . xgb.plot.shap.summary . xgb.plot.tree . xgb.save . xgb.save.raw . xgb.serialize . xgb.shap.data . xgb.train . xgb.unserialize . xgbConfig . xgboost-deprecated .
Some associated R codes: callbacks.R . utils.R . xgb.Booster.R . xgb.DMatrix.R . xgb.DMatrix.save.R . xgb.config.R . xgb.create.features.R . xgb.cv.R . xgb.dump.R . xgb.ggplot.R . xgb.importance.R . xgb.load.R . xgb.load.raw.R . xgb.model.dt.tree.R . xgb.plot.deepness.R . xgb.plot.importance.R . xgb.plot.multi.trees.R . xgb.plot.shap.R . xgb.plot.tree.R . xgb.save.R . xgb.save.raw.R . xgb.serialize.R . xgb.train.R . xgb.unserialize.R . xgboost.R . Full xgboost package functions and examples

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