dmlc/xgboost ↗
Created Dec 29, 2020 · View the dmlc/xgboost repository page
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
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Contributors
522
Lines of Code
17,932
From
Feb 6, 2014
To
Dec 29, 2020
About dmlc/xgboost
XGBoost is an optimized distributed gradient boosting library implementing the Gradient Boosting framework for machine learning. The library is designed to be highly efficient, flexible, and portable, providing parallel tree boosting (also known as GBDT or GBM) that can solve data science problems with speed and accuracy. The same codebase runs across major distributed environments including Kubernetes, Hadoop, Spark, Dask, and Flink, and can handle problems with billions of examples.
The project provides language bindings for Python, R, Java, Scala, and C++, making it accessible to a wide range of practitioners and developers. XGBoost is widely used in industry and competitive machine learning because of its computational efficiency and strong predictive performance. The library originated from research at the University of Washington and is maintained by an active community of developers and contributors.