dmlc XGBoost
Empowering data scientists with high-performance and flexible open-source machine learning solutions through XGBoost by Dmlc.
Dmlc XGBoost is an open-source machine learning library that provides the efficient and flexible XGBoost library, implementing gradient boosting algorithms. It offers parallel tree boosting and runs on major distributed environments, supporting billions of examples and integrating with Optuna for automated machine learning. The project has a strong community presence, sponsorship from the Open Source Collective, and is available under a BSD-3 license.
About dmlc XGBoost
Dmlc XGBoost is a leading open-source machine learning library that develops and provides the optimized distributed gradient boosting library, XGBoost. This library is designed to be highly efficient, flexible, and portable, implementing machine learning algorithms under the gradient boosting framework. XGBoost offers parallel tree boosting, also known as GBDT or GBM, which solves various data science problems in a fast and accurate manner. The same code runs on major distributed environments such as Kubernetes, Hadoop, SGE, Dask, Spark, and PySpark, enabling the solution of problems beyond billions of examples. XGBoost integrates with Optuna for automated machine learning and is sponsored by the Open Source Collective. The project's source code is available on GitHub under a BSD-3 license, and it has a strong community presence with contributions from various developers.