CatBoost
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About
CatBoost is a high-performance open-source library and algorithm for gradient boosting on decision trees, developed by Yandex researchers and engineers. It is widely used in applications like search, recommendation systems, personal assistants, self-driving cars, and weather prediction at Yandex and other companies such as CERN, Cloudflare, and Careem taxi. Key features include achieving great quality with default parameters, robust support for categorical features without pre-processing, a fast and scalable GPU implementation supporting multi-card configurations, improved accuracy through a novel gradient-boosting scheme to reduce overfitting, and fast prediction capabilities for latency-critical tasks. Being open-source, CatBoost is freely available for anyone to use.
Platform
Task
Features
• fast and scalable gpu version
• improved accuracy
• fast prediction
• categorical features support
• great quality without parameter tuning
Pricing Plans
Free
Free Plan• High-performance gradient boosting
• Categorical features support
• Fast and scalable GPU version
• Improved accuracy
• Fast prediction
• Open-source library
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