LightGBM is a gradient boosting framework that uses tree based learning algorithms. It offers faster training speed and lower memory usage for large-scale data.
Project overview
It addresses the problem that training gradient boosting models on large-scale data is typically slow and memory-intensive by delivering faster training speed and higher efficiency with lower memory usage compared to existing boosting frameworks.
Project type
Data Processing
Deployment
Refer to project documentation
License
MIT
Best for
Developers who need to train gradient boosting models on large-scale data with faster training speed and lower memory usage.
Data teams and researchers seeking a distributed gradient boosting framework that handles large-scale data.
Key capabilities
A gradient boosting framework that uses tree based learning algorithms.
Support for distributed learning capable of handling large-scale data.
Support for GPU learning to speed up computation.
Getting started
The project is available on PyPI and conda-forge, making installation straightforward. Run `pip install lightgbm` to get started.