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I performed k-fold CV and measured the resulting average error (RMSE) for each fold. This was done with 5 folds, and 4 of the measurements gave similar errors (between 10% and 12%), but one of the tests has given a 4% error.

What can be concluded in regards to overfitting in this experiment?

Is the model overfitted because it works much better in one of the situations than in the others?

Thanks.

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In short, k-fold CV is not about over-fitting. Your samples can never be ideally identical, so you can only conclude that your error is mean±std.

If your model training process is iterative, then you can detect overfitting by checking test score over the course of training.

If you're making hyper-parameter search with k-fold CV, perhaps with many steps, then you can eventually find out that holdout score is much worse than avg. test score. That would be an overfitting too.

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