Papers › A benchmark of categorical encoders for binary classification

A benchmark of categorical encoders for binary classification

17 Jul 2023NeurIPS 2023 11arXiv:2307.09191archive 2025-07-28

Federico Matteucci, Vadim Arzamasov, Klemens Boehm

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.

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