14fba01b5a
* Add UpdatePredictionCache() option to updaters Some updaters (e.g. fast_hist) has enough information to quickly compute prediction cache for the training data. Each updater may override UpdaterPredictionCache() method to update the prediction cache. Note: this trick does not apply to validation data. * Respond to code review * Disable some debug messages by default * Document UpdatePredictionCache() interface * Remove base_margin logic from UpdatePredictionCache() implementation * Do not take pointer to cfg, as reference may get stale * Improve multi-threaded performance * Use columnwise accessor to accelerate ApplySplit() step, with support for a compressed representation * Parallel sort for evaluation step * Inline BuildHist() function * Cache gradient pairs when building histograms in BuildHist() * Add missing #if macro * Respond to code review * Use wrapper to enable parallel sort on Linux * Fix C++ compatibility issues * MSVC doesn't support unsigned in OpenMP loops * gcc 4.6 doesn't support using keyword * Fix lint issues * Respond to code review * Fix bug in ApplySplitSparseData() * Attempting to read beyond the end of a sparse column * Mishandling the case where an entire range of rows have missing values * Fix training continuation bug Disable UpdatePredictionCache() in the first iteration. This way, we can accomodate the scenario where we build off of an existing (nonempty) ensemble. * Add regression test for fast_hist * Respond to code review * Add back old version of ApplySplitSparseData
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.