Overview
'CatBoost' stands for 'Categorical Boosting.' It is designed to work out-of-the-box with minimal data preprocessing, particularly for datasets with many non-numerical features.
Key Advantages
- Categorical Feature Support: Uses a proprietary algorithm to handle categorical data without needing one-hot encoding or label encoding.
- Robustness: Less sensitive to hyperparameter tuning than XGBoost or LightGBM.
- Ordered Boosting: A technique to prevent 'prediction shift' and reduce overfitting.
- Fast Inference: Optimized for very fast predictions in production environments.