# Encoding categorical data: Is there yet anything 'hotter' than one-hot encoding?

arXiv: https://arxiv.org/abs/2312.16930 · HTML: https://mlsanity.com/paper/2312.16930/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 25 references that are in the map.
- First posted on arXiv 28 Dec 2023.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [An Open Source AutoML Benchmark](https://mlsanity.com/paper/1907.00909/) (2019)
- [Encoding high-cardinality string categorical variables](https://mlsanity.com/paper/1907.01860/) (2019)
- [Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features](https://mlsanity.com/paper/2104.00629/) (2021)
- [Fairness Implications of Encoding Protected Categorical Attributes](https://mlsanity.com/paper/2201.11358/) (2022)
- [Evaluating categorical encoding methods on a real credit card fraud detection database](https://mlsanity.com/paper/2112.12024/) (2021)

## Built on by (most cited first, 3 of 3)

- [TabRep: Training Tabular Diffusion Models with a Simple and Effective Continuous Representation](https://mlsanity.com/paper/2504.04798/) (2025)
- [EVNextTrade: Learning-to-Rank-Based Recommendation of Next Charging Nodes for EV-EV Energy Trading](https://mlsanity.com/paper/2603.26688/) (2026)
- [CLeAN: Continual Learning Adaptive Normalization in Dynamic Environments](https://mlsanity.com/paper/2603.17548/) (2026)

## Abstract (arXiv)

Categorical features are present in about 40% of real world problems, highlighting the crucial role of encoding as a preprocessing component. Some recent studies have reported benefits of the various target-based encoders over classical target-agnostic approaches. However, these claims are not supported by any statistical analysis, and are based on a single dataset or a very small and heterogeneous sample of datasets. The present study explores the encoding effects in an exhaustive sample of classification problems from OpenML repository. We fitted linear mixed-effects models to the experimental data, treating task ID as a random effect, and the encoding scheme and the various characteristics of categorical features as fixed effects. We found that in multiclass tasks, one-hot encoding and Helmert contrast coding outperform target-based encoders. In binary tasks, there were no significant differences across the encoding schemes; however, one-hot encoding demonstrated a marginally positive effect on the outcome. Importantly, we found no significant interactions between the encoding schemes and the characteristics of categorical features. This suggests that our findings are generalizable to a wide variety of problems across domains.