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Cross-validation is recognized as a fundamental technique in Machine Learning for ensuring robust model performance evaluation and preventing overfitting. However, a significant challenge arises when data subsets, or folds, fail to adequately represent the original dataset's diversity, potentially leading to biased performance estimates. To ...
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