CatBoost
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About
CatBoost is a high-performance, open-source library for gradient boosting on decision trees. Developed by Yandex researchers and engineers, it's used in various applications, including search, recommendation systems, and self-driving cars. CatBoost is designed to deliver great quality without extensive parameter tuning and supports non-numeric categorical features. It also offers a fast and scalable GPU version for training on large datasets and reduces overfitting with a novel gradient-boosting scheme. The model applier allows for quick and efficient prediction.
Platform
Task
Features
• fast and scalable gpu version
• improved accuracy
• fast prediction
• categorical features support
• gradient boosting on decision trees
Pricing Plans
Free
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