A benchmark of categorical encoders for binary classification

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 48 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 conclusionsaspects disregarded in previous encoder benchmarks. Our code is available at https://github.com/DrCohomology/EncoderBenchmarking . This version of the paper is identical to the one accepted at the 37th Conference on Neural Information Processing Systems (NeurIPS 2023), Track on Datasets and Benchmarks. Preprint. Under review.