  Categorical encoders transform categorical features into numerical
representations that are indispensable for a wide range of machine learning
models. Existing encoder benchmark studies lack generalizability because of
their limited choice of (1) encoders, (2) experimental factors, and (3)
datasets. Additionally, inconsistencies arise from the adoption of varying
aggregation strategies. This paper is the most comprehensive benchmark of
categorical encoders to date, including an extensive evaluation of 32
configurations of encoders from diverse families, with 36 combinations of
experimental factors, and on 50 datasets. The study shows the profound
influence of dataset selection, experimental factors, and aggregation
strategies on the benchmark's conclusions -- aspects disregarded in previous
encoder benchmarks.
