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 efficiency
- Low memory usage
- Fast training speed and high efficiency
- Lower memory usage
- Better accuracy
- Parallel learning supported
-Deal with large scale of data
-Capacity of handling large scale data
For the details, please refer to [Features](https://github.com/Microsoft/LightGBM/wiki/Features).
For more details, please refer to [Features](https://github.com/Microsoft/LightGBM/wiki/Features).
The [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) on the public data also shows that LightGBM can outperform other existing boosting tools on both learning efficiency and accuracy, with significant lower memory consumption. What's more, the [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#parallel-experiment) shows that LightGBM can achieve linear speed-up by using multiple machines for training in specific settings.
The [experiments](https://github.com/Microsoft/LightGBM/wiki/Experiments#comparison-experiment) 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](https://github.com/Microsoft/LightGBM/wiki/Experiments#parallel-experiment) show that LightGBM can achieve linear speed-up by using multiple machines for training in specific settings.
