a5a58102e5
This PR replaces the original RABIT implementation with a new one, which has already been partially merged into XGBoost. The new one features: - Federated learning for both CPU and GPU. - NCCL. - More data types. - A unified interface for all the underlying implementations. - Improved timeout handling for both tracker and workers. - Exhausted tests with metrics (fixed a couple of bugs along the way). - A reusable tracker for Python and JVM packages.
XGBoost R Package for Scalable GBM
Resources
- XGBoost R Package Online Documentation
- Check this out for detailed documents, examples and tutorials.
Installation
We are on CRAN now. For stable/pre-compiled(for Windows and OS X) version, please install from CRAN:
install.packages('xgboost')
For more detailed installation instructions, please see here.
Examples
- Please visit walk through example.
- See also the example scripts for Kaggle Higgs Challenge, including speedtest script on this dataset and the one related to Otto challenge, including a RMarkdown documentation.
Development
- See the R Package section of the contributors guide.