LightGBM is a gradient boosting framework that uses tree based learning algorithms.
It is designed to be distributed and efficient with the following advantages:
- Faster training speed and higher efficiency.
- Lower memory usage.
- Better accuracy.
- Support of parallel, distributed, and GPU learning.
- Capable of handling large-scale data.
Gradient-Based One-Side Sampling (GOSS) is a method that leverages the fact that there is no native weight for data instance in GBDT.
Since data instances with different gradients play different roles in the computation of information gain, the instances with larger gradients will contribute more to the information gain.
Thus, in order to retain the accuracy of the information, GOSS keeps the instances with large gradients and randomly drops the instances with small gradients.