8ceeb32bad
Prior to this commit XGBoostModel.predict produced an RDD with an array of predictions for each partition, effectively changing the shape wrt the input RDD. A more natural contract for prediction API is that given an RDD it returns a new RDD with the same number of elements. This allows the users to easily match inputs with predictions. This commit removes one layer of nesting in XGBoostModel.predict output. Even though the change is clearly non-backward compatible, I still think it is well justified. See discussion in 06bd5dca for motivation.
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
- 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
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.