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 is part of DMLC projects.

• Documentation and Tutorials
• Code Examples
• Installation
• Contribute to XGBoost

• XGBoost brick Release

• Easily accessible through CLI, python, R, Julia
• Its fast! Benchmark numbers comparing xgboost, H20, Spark, R - benchm-ml numbers
• Memory efficient - Handles sparse matrices, supports external memory
• Accurate prediction, and used extensively by data scientists and kagglers - highlight links
• Distributed version runs on Hadoop (YARN), MPI, SGE etc., scales to billions of examples.

• 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. Everyone is more than welcome to contribute. It is a way to make the project better and more accessible to more users.

• Check out Feature Wish List 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.
• 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, 2015. Licensed under an Apache-2 license.