LightGBM

LightGBM, short for Light Gradient Boosted Machine, is a free and open source distributed gradient boosting framework for machine learning developed by Microsoft.[2][3] It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks. The development focus is on performance and scalability. The framework supports different algorithms including GBT, GBDT, GBRT, GBM, and MART.[4][5]

LightGBM
Original author(s)Microsoft Research
Developer(s)Microsoft
Initial release2016 (2016)
Stable release
v2.3.1[1] / November 26, 2019 (2019-11-26)
Preview release
v3.0.0rc1 / August 7, 2020 (2020-08-07)
Repositorygithub.com/microsoft/LightGBM
Written inC++, Python, R, C
Operating systemWindows, macOS, Linux
TypeGradient boosting framework
LicenseMIT License
Websitelightgbm.readthedocs.io

The source code is licensed under MIT License and available on GitHub.[6]

See also

References
  1. https://github.com/microsoft/LightGBM/tags
  2. Gradient Boosting with Scikit-Learn, XGBoost, LightGBM, and CatBoost
  3. Early detection of type 2 diabetes mellitus using machine learning-based prediction models | Scientific Reports
  4. Understanding LightGBM Parameters (and How to Tune Them) - neptune.ai
  5. An Overview of LightGBM
  6. https://github.com/microsoft/LightGBM

Further reading
  • Guolin Ke, Qi Meng, Thomas Finely, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, Tie-Yan Liu (2017). "LightGBM: A Highly Efficient Gradient Boosting Decision Tree" (PDF). Cite journal requires |journal= (help)CS1 maint: uses authors parameter (link)
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