Overview
CatBoost is an open-source gradient boosting framework developed by Yandex. It excels in handling categorical features directly, eliminating the need for extensive pre-processing. The algorithm utilizes a novel gradient boosting scheme to reduce overfitting, leading to improved accuracy and generalization. Its architecture is designed for both CPU and GPU environments, enabling scalable training on large datasets, even with multi-card configurations. CatBoost is used in various applications, including search, recommendation systems, personal assistants, self-driving cars, and weather prediction. The library's fast prediction capabilities make it suitable for latency-critical tasks.
Common tasks