How CatBoost Works - Amazon SageMaker AI
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How CatBoost Works

CatBoost implements a conventional Gradient Boosting Decision Tree (GBDT) algorithm with the addition of two critical algorithmic advances:

  1. The implementation of ordered boosting, a permutation-driven alternative to the classic algorithm

  2. An innovative algorithm for processing categorical features

Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms.

The CatBoost algorithm performs well in machine learning competitions because of its robust handling of a variety of data types, relationships, distributions, and the diversity of hyperparameters that you can fine-tune. You can use CatBoost for regression, classification (binary and multiclass), and ranking problems.

For more information on gradient boosting, see How the SageMaker AI XGBoost algorithm works. For in-depth details about the additional GOSS and EFB techniques used in the CatBoost method, see CatBoost: unbiased boosting with categorical features.