From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation
This provides an incremental improvement for machine learning practitioners by reducing computational costs in model evaluation.
This paper tackles the computational inefficiency of k-fold cross-validation by introducing e-fold cross-validation, which dynamically stops early based on a stability criterion. On average, it required 4 fewer folds than 10-fold cross-validation, reducing evaluation time and energy use by about 40% while maintaining performance differences under 2% for larger datasets.
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.