2715baef64
* Bugfix 1: Fix segfault in multithreaded ApplySplitSparseData() When there are more threads than rows in rowset, some threads end up with empty ranges, causing them to crash. (iend - 1 needs to be accessible as part of algorithm) Fix: run only those threads with nonempty ranges. * Add regression test for Bugfix 1 * Moving python_omp_test to existing python test group Turns out you don't need to set "OMP_NUM_THREADS" to enable multithreading. Just add nthread parameter. * Bugfix 2: Fix corner case of ApplySplitSparseData() for categorical feature When split value is less than all cut points, split_cond is set incorrectly. Fix: set split_cond = -1 to indicate this scenario * Bugfix 3: Initialize data layout indicator before using it data_layout_ is accessed before being set; this variable determines whether feature 0 is included in feat_set. Fix: re-order code in InitData() to initialize data_layout_ first * Adding regression test for Bugfix 2 Unfortunately, no regression test for Bugfix 3, as there is no way to deterministically assign value to an uninitialized variable.
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.