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.

这篇研究论文是迄今为止最全面的分类编码器基准测试，包括对来自不同家族的 32 种配置的编码器进行的广泛评估，36 种实验因素的组合以及 50 个数据集的测试。研究显示数据集选择、实验因素和汇总策略对基准测试的结论产生了深远影响，而这些方面在以往的编码器基准测试中被忽视。