Features
boostinggradient-boostingtabularkaggleensemble
Pros and Cons
Ventajas
- + Very fast and memory efficient
- + Excellent performance on tabular data
- + Built-in regularization
- + Automatic missing value handling
- + GPU support
- + Winner of many Kaggle competitions
Desventajas
- - Requires hyperparameter tuning
- - Can overfit without care
- - Not ideal for non-tabular data
- - Less interpretable than simple models
- - API can be confusing between interfaces
Use Cases
- Tabular classification and regression
- ML competitions (Kaggle)
- Credit risk prediction
- Fraud detection
- Ranking and recommendations
- Feature importance analysis