2b5b96d760
* [R] xgb.save must work when handle in nil but raw exists * [R] print.xgb.Booster should still print other info when handle is nil * [R] rename internal function xgb.Booster to xgb.Booster.handle to make its intent clear * [R] rename xgb.Booster.check to xgb.Booster.complete and make it visible; more docs * [R] storing evaluation_log should depend only on watchlist, not on verbose * [R] reduce the excessive chattiness of unit tests * [R] only disable some tests in windows when it's not 64-bit * [R] clean-up xgb.DMatrix * [R] test xgb.DMatrix loading from libsvm text file * [R] store feature_names in xgb.Booster, use them from utility functions * [R] remove non-functional co-occurence computation from xgb.importance * [R] verbose=0 is enough without a callback * [R] added forgotten xgb.Booster.complete.Rd; cran check fixes * [R] update installation instructions
eXtreme Gradient Boosting
Documentation | Resources | Installation | Release Notes | RoadMap
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable. It implements machine learning algorithms under the Gradient Boosting framework. XGBoost provides a parallel tree boosting (also known as GBDT, GBM) that solve many data science problems in a fast and accurate way. The same code runs on major distributed environment (Hadoop, SGE, MPI) and can solve problems beyond billions of examples.
What's New
- XGBoost4J: Portable Distributed XGboost in Spark, Flink and Dataflow, see JVM-Package
- Story and Lessons Behind the Evolution of XGBoost
- Tutorial: Distributed XGBoost on AWS with YARN
- XGBoost brick Release
Ask a Question
- For reporting bugs please use the xgboost/issues page.
- For generic questions or to share your experience using XGBoost please use the XGBoost User Group
Help to Make XGBoost Better
XGBoost has been developed and used by a group of active community members. Your help is very valuable to make the package better for everyone.
- Check out call for contributions and Roadmap to see what can be improved, or open an issue if you want something.
- Contribute to the documents and examples to share your experience with other users.
- Add your stories and experience to Awesome XGBoost.
- Please add your name to CONTRIBUTORS.md and after your patch has been merged.
- Please also update NEWS.md on changes and improvements in API and docs.
License
© Contributors, 2016. Licensed under an Apache-2 license.
Reference
- Tianqi Chen and Carlos Guestrin. XGBoost: A Scalable Tree Boosting System. In 22nd SIGKDD Conference on Knowledge Discovery and Data Mining, 2016
- XGBoost originates from research project at University of Washington, see also the Project Page at UW.