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Let's say I have a dataset for binary classification. And I am going to conduct 5-fold cross-validation and get AUC scores for each fold (mean AUC score too). However, if I set the training epoch to $30$, and, with that classifier, test all my test folds iteratively, I will be getting AUC scores of test folds at specific weights of a classifier (at $30$s epochs).

Is it a fair evaluation if I watch some metrics (like validation loss) until stagnation and reduce the learning rate accordingly for each fold separately and get their respective AUC score, then report the mean AUC score?

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