Description
CatBoost (Categorical Boosting) is an open-source gradient boosting framework developed by Yandex, designed for high accuracy and efficiency. It excels at handling categorical features without extensive preprocessing and provides robust performance with minimal hyperparameter tuning. CatBoost is optimized for speed, supports GPU acceleration, and is widely used in machine learning applications, including finance, e-commerce, and recommendation systems.
