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When creating different hyperparameter combinations, does the function evaluate combination 1 on the same fold as combination 2? As in, are the folds the same across combinations? I understand that for example for cv = 3, a third of the dataset will be held out for evaluation. There will be three such splits, each containing a test fold. Will the split be different for another combination? I also think that it is unnecessary computation to split again but does it happen?

I could not find strong reference to this fact in the documentation except for a part about cv_results_ or it is just assumed that, this is the way it is.

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The probability that there's a mistake in SK-Learn is very low.

Reasons:

  • It's a mature library

  • Among the contributors is Sebastian Raschka. Those guys are very meticulous.

https://sebastianraschka.com/faq/docs/evaluate-a-model.html

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