dmlc/xgboost

XGBoost is an optimized distributed gradient boosting library written primarily in C++ that implements machine learning algorithms under the Gradient Boosting framework. The library is designed to be highly efficient, flexible, and portable, providing parallel tree boosting capabilities also known as GBDT, GBM, or GBRT. It solves data science problems in a fast and accurate manner and can handle datasets with billions of examples. The same codebase runs across major distributed environments including Kubernetes, Hadoop, SGE, Dask, Spark, PySpark, and DataFlow, making it suitable for both single-machine and large-scale distributed computing scenarios.

The repository originated from a research project at the University of Washington and was published as a peer-reviewed paper by Tianqi Chen and Carlos Guestrin at the 22nd SIGKDD Conference on Knowledge Discovery and Data Mining in 2016. The library provides language bindings for Python, R, Java, Scala, C++, and additional languages, enabling broad accessibility across different development ecosystems. XGBoost is licensed under Apache 2.0, allowing free use and modification by the community.

The most frequently requested features are tracked through feature-request labels with 105 items, while status updates and bug reports represent significant portions of ongoing work with 99 status-need-update items and 30 bug reports.

The project is supported by prominent sponsors including NVIDIA, Intel, Comet, and Databento, with funding directed toward continuous integration and testing infrastructure hosted at xgboost-ci.net. This sponsorship model reflects the library's importance to the broader data science and machine learning ecosystem. The repository emphasizes community contribution and maintains active documentation, community pages, and contributor guidelines to facilitate ongoing development and user support.