Introduction

When we train a machine learning model, it is important to evaluate it in a way that gives a fair and reliable picture of its performance. Cross-validation is one of the most commonly used techniques for this purpose. However, sometimes we notice that the model’s performance metrics, such as accuracy or F1-score, fluctuate significantly across different folds of cross-validation. These fluctuations make it difficult to judge how well the model will perform on unseen data.

The reason for these fluctuations is the variance introduced by the particular data splits used in cross-validation. If a certain fold happens to include more difficult or unbalanced examples, the model may perform worse on that fold, even if it generally performs well. This randomness leads to high variance in the evaluation results.

One effective way to reduce this variance is by using Repeated k-Fold Cross-Validation. Instead of performing k-fold cross-validation only once, we repeat the entire process multiple times with different random splits of the dataset. For each repetition, the model is trained and tested on new partitions. Finally, the results are averaged across all repetitions. This averaging reduces the impact of any one “lucky” or “unlucky” split, giving us a more stable and reliable estimate of the model’s performance.

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Conclusion

In summary, when cross-validation results vary greatly across folds, the problem lies in variance due to data partitioning. By applying Repeated k-Fold Cross-Validation, we can smooth out these fluctuations and obtain a more trustworthy evaluation of our model. This method ensures that performance metrics are not overly dependent on a single data split, making the evaluation both robust and credible.