# Is it a fair evaluation if each model of k-fold cross validation is trained with different epochs and the mean AUC score gathered out of k folds?

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?