CatBoost is a high-performance open source library for gradient boosting on decision trees. It delivers excellent results with minimal parameter tuning, supports categorical features, and offers a fast, scalable GPU version. Primarily developed by Yandex, CatBoost is used in a range of applications, including search, recommendation systems, and self-driving cars. Its improved accuracy and speedy prediction make it suited for latency-critical tasks.
