# From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation

arXiv: https://arxiv.org/abs/2410.09463 · HTML: https://mlsanity.com/paper/2410.09463/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 27 references that are in the map.
- First posted on arXiv 12 Oct 2024.

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

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Towards Green Automated Machine Learning: Status Quo and Future Directions](https://mlsanity.com/paper/2111.05850/) (2021)
- [Resource-Efficient Neural Networks for Embedded Systems](https://mlsanity.com/paper/2001.03048/) (2020)
- [Towards energy-efficient Deep Learning: An overview of energy-efficient approaches along the Deep Learning Lifecycle](https://mlsanity.com/paper/2303.01980/) (2023)
- [From Clicks to Carbon: The Environmental Toll of Recommender Systems](https://mlsanity.com/paper/2408.08203/) (2024)
- [Don't Waste Your Time: Early Stopping Cross-Validation](https://mlsanity.com/paper/2405.03389/) (2024)

## Abstract (arXiv)

This paper introduces e-fold cross-validation, an energy-efficient alternative to k-fold cross-validation. It dynamically adjusts the number of folds based on a stopping criterion. The criterion checks after each fold whether the standard deviation of the evaluated folds has consistently decreased or remained stable. Once met, the process stops early. We tested e-fold cross-validation on 15 datasets and 10 machine-learning algorithms. On average, it required 4 fewer folds than 10-fold cross-validation, reducing evaluation time, computational resources, and energy use by about 40%. Performance differences between e-fold and 10-fold cross-validation were less than 2% for larger datasets. More complex models showed even smaller discrepancies. In 96% of iterations, the results were within the confidence interval, confirming statistical significance. E-fold cross-validation offers a reliable and efficient alternative to k-fold, reducing computational costs while maintaining comparable accuracy.