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[D] Can a model be complicated enough to overfit every validation fold during a k-fold cross-validation process?

During k-fold cross-validation, is it possible that a model is so sophisticated (e.g., with many hyperparameters to be grid searched) that it gives a good score on almost every validation fold? It’s like the model is intricate enough to somehow leak out to fit the validation set every time (there are k times), essentially overfitting the whole training set (because the sum of the k validation folds is just the whole training set).

If this is possible, then I feel like it’s also possible that this best model will eventually have a high generalization error when tested on the final test set, essentially making cross-validation useless. Did I miss anything here?

submitted by /u/this_nicholas
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.