b94fcab4dc
* Add format to the params accepted by DumpModel Currently, only the test format is supported when trying to dump a model. The plan is to add more such formats like JSON which are easy to read and/or parse by machines. And to make the interface for this even more generic to allow other formats to be added. Hence, we make some modifications to make these function generic and accept a new parameter "format" which signifies the format of the dump to be created. * Fix typos and errors in docs * plugin: Mention all the register macros available Document the register macros currently available to the plugin writers so they know what exactly can be extended using hooks. * sparce_page_source: Use same arg name in .h and .cc * gbm: Add JSON dump The dump_format argument can be used to specify what type of dump file should be created. Add functionality to dump gblinear and gbtree into a JSON file. The JSON file has an array, each item is a JSON object for the tree. For gblinear: - The item is the bias and weights vectors For gbtree: - The item is the root node. The root node has a attribute "children" which holds the children nodes. This happens recursively. * core.py: Add arg dump_format for get_dump()
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 for 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.