(1) Extend the precision of dump model to 10 fix digits for quickscorer's model loading.
https://github.com/qf6101/xgboost/commit/324d0346c5bfdd1f48b8f1f8d1968ed89774a27d
(2) Fix bug in prediction function with issue.
https://github.com/qf6101/xgboost/commit/93bc279b714ed34ecd7667f9e8f7a11d7652683b
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.
- XGBoost GPU support with fast histogram algorithm
- 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
- 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
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.
© Contributors, 2016. Licensed under an Apache-2 license.
- 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.