Lightgbm is a framework that is used for implementing gradient boosting algorithms. It is programmed to be distributed efficiently with accuracy.
Comparing Lightgbm with other Frameworks
In a comparison of other boosting related framework, it has the following advantages -
- Training speed faster without compromising efficiency.
- The memory usage is also low.
- It provides better accuracy.
- It supports two types of learning parallel and GPU.
- It has the capability of handling large scale data.
Best Uses of Lightgbm Framework
It is recommended to use Lightgbm with small datasets because it is considered to be prone to overfitting, which can easily overfit on small datasets also.