xgboost
type: Item
swMATH work ID: 15875
instance of: R package
software version identifier: 1.7.5.1
software version identifier: 0.3-0
software version identifier: 0.3-1
software version identifier: 0.3-2
software version identifier: 0.3-3
software version identifier: 0.4-1
software version identifier: 0.4-2
software version identifier: 0.4-3
software version identifier: 0.4-4
software version identifier: 0.6-0
software version identifier: 0.6-2
software version identifier: 0.6-3
software version identifier: 0.6-4
software version identifier: 0.6.4.1
software version identifier: 0.71.1
software version identifier: 0.71.2
software version identifier: 0.81.0.1
software version identifier: 0.82.1
software version identifier: 0.90.0.1
software version identifier: 0.90.0.2
software version identifier: 1.0.0.1
software version identifier: 1.0.0.2
software version identifier: 1.1.1.1
software version identifier: 1.2.0.1
software version identifier: 1.3.1.1
software version identifier: 1.3.2.1
software version identifier: 1.4.1.1
software version identifier: 1.5.0.1
software version identifier: 1.5.0.2
software version identifier: 1.5.2.1
software version identifier: 1.6.0.1
software version identifier: 1.7.3.1
software version identifier: 1.7.6.1
software version identifier: 1.7.7.1
maintained by: Jiaming Yuan
CRAN project: xgboost
programmed in: R
last update: 2024-01-25T00:00:00Z
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.
author: Tianqi Chen
author: Tong He
author: Michael Benesty
author: Vadim Khotilovich
author: Yuan Tang
author: Hyunsu Cho
author: Kailong Chen
author: Rory Mitchell
author: Ignacio Cano
author: Tianyi Zhou
author: Mu Li
author: Junyuan Xie
author: Min Lin
author: Yifeng Geng
author: Yutian Li
author: Jiaming Yuan
copyright license: Apache License
copyright license: File License
imports: Matrix
imports: methods
imports: data.table
imports: jsonlite
cites work: XGBoost
MaRDI profile type: Software
depends on software: R
source code repository: xgboost
description: Extreme Gradient Boosting