Features
boostingmicrosoftfasttabulargradient-boosting
Pros and Cons
Ventajas
- + Faster than XGBoost in many cases
- + Lower memory usage
- + Native categorical feature support
- + More efficient leaf-wise growth
- + Excellent for large datasets
- + GPU and distributed support
Desventajas
- - Can overfit with small data
- - Sensitive to hyperparameters
- - Leaf-wise can create unbalanced trees
- - Less complete documentation than XGBoost
- - Fewer learning resources
Use Cases
- Large dataset classification
- Regression at scale
- Result ranking
- Problems with categorical features
- Production ML pipelines
- Data competitions