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CatBoost

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About

CatBoost is an open-source algorithm for gradient boosting on decision trees, developed by Yandex. It is widely used in critical applications like search, recommendation systems, personal assistants, self-driving cars, and weather prediction at Yandex and other major companies such as CERN and Cloudflare. Key features include achieving great quality without extensive parameter tuning, robust support for categorical features without pre-processing, a fast and scalable GPU implementation for large datasets, improved accuracy through a novel gradient-boosting scheme to reduce overfitting, and highly efficient, fast prediction capabilities for latency-critical tasks.

Platform

Task

predictive modeling

Features

• fast and scalable gpu version

• improved accuracy

• fast prediction

• categorical features support

• great quality without parameter tuning

Pricing Plans

Open Source

• High-performance gradient boosting

• Great quality without parameter tuning

• Categorical features support

• Fast and scalable GPU version

• Improved accuracy to reduce overfitting

• Fast prediction capabilities

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