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CatBoost is a gradient boosting library based on binary decision trees. Target leakage and prediction shifts are avoided by grouping categories with target statistics (TS). The log loss and zero-one loss were better than the traditional greedy algorithm.
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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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