Extreme Gradient Boosting, which is an efficient implementation of the gradient boosting framework
from Chen & Guestrin (2016) <10.1145>. 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.
For more information visit the xgboost website.
These versions of xgboost are available on the BEAR systems (BlueBEAR, BEAR Cloud VMs, and CaStLeS VMs). These will be retained in accordance with our Applications Support and Retention Policy.