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Correctly dealing with categorical data in a supervised learning context isstill a major issue. Furthermore, though some machine learning methods embodybuiltin methods to deal with categorical features, it is unclear whether theybring some improvements and how do they compare with usual categorical encodingmethods. In this paper, we describe several well-known categorical encodingmethods that are based on target statistics and weight of evidence. We applythem on a large and real credit card fraud detection database. Then, we trainthe encoded databases using state-of-the-art gradient boosting methods andevaluate their performances. We show that categorical encoding methodsgenerally bring substantial improvements with respect to the absence ofencoding. The contribution of this work is twofold: (1) we compare manystate-of-the-art "lite" categorical encoding methods on a large scale databaseand (2) we use a real credit card fraud detection database.

Evaluating categorical encoding methods on a real credit card fraud detection database