CatBoost
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About
CatBoost is a high-performance, open-source library designed for gradient boosting on decision trees. It offers great quality without extensive parameter tuning and supports categorical features directly, reducing preprocessing time. The library includes a fast GPU implementation, enhancing training speed on large datasets while improving model accuracy and minimizing overfitting. CatBoost is developed by Yandex and used in various applications such as search engines, recommendation systems, and autonomous vehicles. It has garnered attention for its ability to handle trillions of requests in training. Furthermore, it enables swift predictions even in latency-sensitive tasks, making it suitable for diverse machine learning challenges.
Platform
Features
• great quality without parameter tuning
• fast and scalable gpu version
• improved accuracy
• fast prediction
• support for categorical features
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