CatBoost is a fast, high-performance open source library for gradient boosting on decision trees. It is a machine learning method with plenty of applications, including ranking, classification, regression and other machine learning tasks for Python, R, Java, C++.
CatBoost offers superior performance over other GBDT libraries on many datasets, and has several superb features. It has best in class prediction speed, supports both numerical and categorical features, has a fast and scalable GPU version, and readily comes with visualization tools. CatBoost was developed by Yandex and is used in various areas including search, self-driving cars, personal assistance, weather prediction and more.
Features
- Exceptional prediction speed
- Novel gradient-boosting scheme that improves accuracy
- Fast GPU and multi-GPU support for training
- Supports numerical and categorical features
- Offers great quality results without parameter tuning
- Comes with visualization tools
Project Samples
Categories
Machine LearningLicense
Apache License V2.0
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