tkcrown/LightGBM

LightGBM is a fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. It is under the umbrella of the DMTK(http://github.com/microsoft/dmtk) project of Microsoft.

LightGBM, Light Gradient Boosting Machine

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.

Get Started

For a quick start, please follow the Installation Guide and Quick Start.

Documents

• Wiki
• Installation Guide
• Quick Start
• Examples
• Features
• Parallel Learning Guide
• Configuration

Microsoft Open Source Code of Conduct

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.