# A benchmark of categorical encoders for binary classification

arXiv: https://arxiv.org/abs/2307.09191 · HTML: https://mlsanity.com/paper/2307.09191/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 52 references that are in the map.
- First posted on arXiv 17 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Accounting for Variance in Machine Learning Benchmarks](https://mlsanity.com/paper/2103.03098/) (2021)
- [The Benchmark Lottery](https://mlsanity.com/paper/2107.07002/) (2021)
- [A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking](https://mlsanity.com/paper/2210.07494/) (2022)
- [Similarity encoding for learning with dirty categorical variables](https://mlsanity.com/paper/1806.00979/) (2018)
- [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)
- [Over-optimism in benchmark studies and the multiplicity of design and analysis options when interpreting their results](https://mlsanity.com/paper/2106.02447/) (2021)
- [Endgame Analysis of Dou Shou Qi](https://mlsanity.com/paper/1604.07312/) (2016)
- [Data mining application for cyber space users tendency in blog writing: a case study](https://mlsanity.com/paper/1307.7432/) (2013)

## Built on by (most cited first, 6 of 6)

- [Representation Learning for Tabular Data: A Comprehensive Survey](https://mlsanity.com/paper/2504.16109/) (2025)
- [FairJob: A Real-World Dataset for Fairness in Online Systems](https://mlsanity.com/paper/2407.03059/) (2024)
- [Exact Functional ANOVA Decomposition for Categorical Inputs Models](https://mlsanity.com/paper/2603.02673/) (2026)
- [Efficient Generation of Hidden Outliers for Improved Outlier Detection](https://mlsanity.com/paper/2402.03846/) (2024)
- [Generalizability of experimental studies](https://mlsanity.com/paper/2406.17374/) (2024)
- [Kernel-based External Validity of Experimental Studies](https://mlsanity.com/paper/2610.13625/) (2026)

## Abstract (arXiv)

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.