CatBoost

CatBoost is an open-source gradient boosting library on decision trees, developed by Yandex as the successor to its MatrixNet algorithm. It natively supports categorical features without preprocessing, offers a fast GPU implementation for large datasets, and reduces overfitting via a proprietary gradient boosting scheme. The library is used for tasks including search, recommendation systems, and weather prediction at Yandex.

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0 shouts Added Sep 23, 2026 catboost.ai
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