LightGBM is a gradient boosting framework that is using tree based learning algorithms. It is designed to be distributed and efficient with following advantages:
- Fast training speed and high efficiency
- Lower memory usage
- Better accuracy
- Parallel learning supported
- Capability of handling large-scaling data
For more details, please refer to Features.
The experiments on public datasets show that LightGBM outperform other existing boosting tools on both efficiency and accuracy, with significant lower memory consumption. What's more, the experiments show that LightGBM can achieve linear speed-up by using multiple machines for training in specific settings.
For a quick start, please follow the Installation Guide and Quick Start.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.