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