Features
boostingcategoricalyandextabulargradient-boosting
Pros and Cons
Ventajas
- + Best handling of categorical features
- + Less tuning needed than XGBoost
- + Robustness against overfitting
- + Excellent GPU support
- + Symmetric trees for better generalization
- + Good defaults out of the box
Desventajas
- - Slower than LightGBM in some cases
- - Larger binaries
- - Less popular than XGBoost
- - Less extensive documentation
- - Fewer integrations available
Use Cases
- Data with many categorical features
- Fast classification and regression
- Problems where XGBoost overfits
- Click-through rate prediction
- Recommendation systems
- Applications with mixed data