# Empirical Comparison between Cross-Validation and Mutation-Validation in Model Selection

arXiv: https://arxiv.org/abs/2311.14079 · HTML: https://mlsanity.com/paper/2311.14079/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 17 references that are in the map.
- First posted on arXiv 23 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning](https://mlsanity.com/paper/1811.12808/) (2018)
- [Statistical comparison of classifiers through Bayesian hierarchical modelling](https://mlsanity.com/paper/1609.08905/) (2016)
- [The advantages of multiple classes for reducing overfitting from test set reuse](https://mlsanity.com/paper/1905.10360/) (2019)
- [Modeling Generalization in Machine Learning: A Methodological and Computational Study](https://mlsanity.com/paper/2006.15680/) (2020)
- [Model Validation Using Mutated Training Labels: An Exploratory Study](https://mlsanity.com/paper/1905.10201/) (2019)

## Abstract (arXiv)

Mutation validation (MV) is a recently proposed approach for model selection, garnering significant interest due to its unique characteristics and potential benefits compared to the widely used cross-validation (CV) method. In this study, we empirically compared MV and k-fold CV using benchmark and real-world datasets. By employing Bayesian tests, we compared generalization estimates yielding three posterior probabilities: practical equivalence, CV superiority, and MV superiority. We also evaluated the differences in the capacity of the selected models and computational efficiency. We found that both MV and CV select models with practically equivalent generalization performance across various machine learning algorithms and the majority of benchmark datasets. MV exhibited advantages in terms of selecting simpler models and lower computational costs. However, in some cases MV selected overly simplistic models leading to underfitting and showed instability in hyperparameter selection. These limitations of MV became more evident in the evaluation of a real-world neuroscientific task of predicting sex at birth using brain functional connectivity.