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From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation
Machine Learning · Submitted 12 Oct 2024
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim E-fold cross-validation offers a reliable and efficient alternative to k-fold, reducing computational costs while maintaining comparable accuracy.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | This paper introduces e-fold cross-validation, an energy-efficient alternative to k-fold cross-validation. | contribution 1.00 | 0.73 | 0.15 | asserted 1.00 |
| s1 | It dynamically adjusts the number of folds based on a stopping criterion. | method 0.95 | 0.58 | 0.03 | asserted 1.00 |
| s2 | The criterion checks after each fold whether the standard deviation of the evaluated folds has consistently decreased or remained stable. | method 1.00 | 0.55 | 0.02 | asserted 1.00 |
| s3 | Once met, the process stops early. | method 1.00 | 0.42 | 0.03 | asserted 0.98 |
| s4 | We tested e-fold cross-validation on 15 datasets and 10 machine-learning algorithms. | method 0.90 | 0.96 | 0.03 | asserted 1.00 |
| s5 | On average, it required 4 fewer folds than 10-fold cross-validation, reducing evaluation time, computational resources, and energy use by about 40%. | result 1.00 | 0.96 | 0.20 | asserted 1.00 |
| s6 | Performance differences between e-fold and 10-fold cross-validation were less than 2% for larger datasets. | result 1.00 | 0.95 | 0.11 | asserted 1.00 |
| s7 | More complex models showed even smaller discrepancies. | result 0.99 | 0.89 | 0.03 | asserted 1.00 |
| s8 | In 96% of iterations, the results were within the confidence interval, confirming statistical significance. | result 0.89 | 0.94 | 0.07 | asserted 1.00 |
| s9 | E-fold cross-validation offers a reliable and efficient alternative to k-fold, reducing computational costs while maintaining comparable accuracy. | interpretation 0.98 | 0.69 | 0.93 | asserted 1.00 |
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The paper
Original abstract
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.
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Papers whose main result relates to this one's
States the same result 1 paper
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We conclude that e-fold cross validation is a promising approach that has the potential to be an energy efficient but still reliable alternative to k-fold cross validation.
e-Fold Cross-Validation for Recommender-System Evaluation · 0.86
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