LightGBM is a high-performance gradient boosting framework optimized for efficiency and scalability. It supports parallel and distributed training, GPU acceleration, and provides flexible interfaces for Python and R. LightGBM is widely used in production systems and ML competitions.
Features
- Fast training speed and low memory usage, suitable for large datasets
- Support for parallel, distributed, and GPU-based training
- Extensive parameterization and compatibility with hyperparameter tuning tools (Optuna, FLAML)
- Multiple language bindings and integration with deployment toolchains (Treelite, Hummingbird)
Use Cases
- Classification and regression on tabular data
- Learning to rank for search and recommendation systems
- Competitive ML projects and rapid prototyping
- Production deployments requiring efficient training and inference
Technical Details
- Core implementation in C++ with Python and R bindings
- Supports CUDA-based GPU acceleration and multi-node distributed training
- Documentation and getting started guides: https://lightgbm.readthedocs.io/