LightGBM is a gradient boosting framework that uses tree based learning algorithms. It is designed to be distributed and efficient with the following advantages:
- Faster training speed and higher efficiency
- Lower memory usage
- Better accuracy
- Parallel learning supported
- Capable of handling large-scale data
For more details, please refer to Features.
Experiments on public datasets show that LightGBM can outperform other existing boosting framework on both efficiency and accuracy, with significant lower memory consumption. What's more, the experiments show that LightGBM can achieve a linear speed-up by using multiple machines for training in specific settings.
12/05/2016 : Categorical Features as input directly(without one-hot coding). Experiment on Expo data shows about 8x speed-up with same accuracy compared with one-hot coding (refer to categorical log and one-hot log). For the setting details, please refer to IO Parameters.
12/02/2016 : Release python-package beta version, welcome to have a try and provide issues and feedback.
To get started, please follow the Installation Guide and Quick Start.
- Wiki
- Installation Guide
- Quick Start
- Examples
- Features
- Parallel Learning Guide
- Configuration
- Document Indexer
LightGBM has been developed and used by many active community members. Your help is very valuable to make it better for everyone.
- Check out call for contributions to see what can be improved, or open an issue if you want something.
- Contribute to the tests to make it more reliable.
- Contribute to the documents to make it clearly for everyone.
- Contribute to the examples to share your experience with other users.
- Check out Development Guide.
- Open issue if you met problems during development.
This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.