 | |
| Original author(s) | Guolin Ke[1] / Microsoft Research |
|---|---|
| Developer(s) | Microsoft and LightGBM contributors[2] |
| Initial release | 2016 |
| Stable release | v4.3.0[3]
/ January 15, 2024 |
| Repository | github |
| Written in | C++, Python, R, C |
| Operating system | Windows, macOS, Linux |
| Type | Machine learning, gradient boosting framework |
| License | MIT License |
| Website | lightgbm |
LightGBM, short for Light Gradient-Boosting Machine, is a free and open-source distributed gradient-boosting framework for machine learning, originally developed by Microsoft.[4][5] 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.
- ^ "Guolin Ke". GitHub.
- ^ "microsoft/LightGBM". GitHub. 7 July 2022.
- ^ "Releases · microsoft/LightGBM". GitHub.
- ^ Brownlee, Jason (March 31, 2020). "Gradient Boosting with Scikit-Learn, XGBoost, LightGBM, and CatBoost".
- ^ Kopitar, Leon; Kocbek, Primoz; Cilar, Leona; Sheikh, Aziz; Stiglic, Gregor (July 20, 2020). "Early detection of type 2 diabetes mellitus using machine learning-based prediction models". Scientific Reports. 10 (1): 11981. Bibcode:2020NatSR..1011981K. doi:10.1038/s41598-020-68771-z. PMC 7371679. PMID 32686721 – via www.nature.com.