Following our examination of XGBoost and LightGBM, we now focus on CatBoost, a gradient boosting library optimized for a specific, yet common, challenge: effectively handling categorical features. Standard approaches often involve preprocessing steps that can be suboptimal or lead to issues like target leakage. CatBoost integrates innovative solutions for categorical data directly into its algorithm.

This chapter covers:

• The difficulties traditional boosting methods encounter with categorical variables.
• Ordered Target Statistics (Ordered TS): CatBoost's method for encoding categoricals while minimizing target leakage.
• Ordered Boosting: A technique to counteract prediction shift during training.
• Automatic Feature Combinations: How CatBoost generates interactions between categorical features.
• Oblivious Trees: The symmetric decision trees employed by CatBoost.
• Using the CatBoost API: Key parameters and practical implementation details.

Upon completing this chapter, you will grasp CatBoost's distinct methods and be prepared to apply them, particularly to problems involving significant categorical data.