LightGBM

Exclusive Feature Bundling (EFB) is a key LightGBM optimization that reduces the number of features by merging sparse, mutually exclusive columns—cutting memory usage and training time without sacrificing accuracy.

Gradient-based One-Side Sampling (GOSS) is a key innovation in LightGBM that accelerates model training without losing accuracy. By focusing on high-gradient (hard-to-learn) data points and selectively sampling low-gradient ones, GOSS strikes the perfect balance between speed and performance, making LightGBM faster and more efficient than traditional boosting methods.

Discover how LightGBM optimizes gradient boosting with faster training, memory efficiency, and advanced split finding. Learn its unique leaf-wise growth strategy, objective function, and why it outperforms traditional methods like XGBoost.