How LightGBM works

LightGBM implements a conventional Gradient Boosting Decision Tree (GBDT) algorithm with the addition of two novel techniques: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). These techniques are designed to significantly improve the efficiency and scalability of GBDT.

The LightGBM algorithm performs well in machine learning competitions because of its robust handling of a variety of data types, relationships, distributions, and the diversity of hyperparameters that you can fine-tune. You can use LightGBM for regression, classification (binary and multiclass), and ranking problems.

For more information on gradient boosting, see How the SageMaker AI XGBoost algorithm works. For in-depth details about the additional GOSS and EFB techniques used in the LightGBM method, see LightGBM: A Highly Efficient Gradient Boosting Decision Tree.