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Old 07-09-2021, 05:33 PM
gverhoev gverhoev is offline
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Default Is a test set needed after cross validation?

It is said that the CV error Ecv is an unbiased estimate of Eout (N-1), hence it is used for model selection.
However, many books say that after CV, one should have a third dataset (often called the test dataset) to truly measure the performance (i.e. Eout) of the chosen final hypothesis. However, if Ecv is already an unbiased estimate of Eout (N - 1), why would one then even need this third / test set to check how well the model does? Is there something that I miss here?
Is this maybe because the CV approach is technically data snooping (because we make a model choice influenced by the data, so the data has less ability to evaluate the final outcome), so it is still best to test the real performance on never seen data? Or are these books simply wrong?
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cross validation, data snooping, out-of-sample error, test set

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