xgboost 1.0.0.2-foss-2019b-R-3.6.2

Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework from Chen & Guestrin (2016) . 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.

Accessing xgboost 1.0.0.2-foss-2019b-R-3.6.2

To load the module for xgboost 1.0.0.2-foss-2019b-R-3.6.2 please use this command on the BEAR systems (BlueBEAR and BEAR Cloud VMs):

📋 module load xgboost/1.0.0.2-foss-2019b-R-3.6.2

BEAR Apps Version

2019b

Architectures

EL8-cascadelake — EL8-icelake — EL8-sapphirerapids

The listed architectures consist of two part: OS-CPU. The OS used is represented by EL and there are several different processor (CPU) types available on BlueBEAR. More information about the processor types on BlueBEAR is available on the BlueBEAR Job Submission page.

Extensions

xgboost 1.0.0.2

More Information

For more information visit the xgboost website.

Dependencies

This version of xgboost has a direct dependency on: foss/2019b R/3.6.2-foss-2019b

Last modified on 6th May 2020