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I am taking a course on machine learning and in one problem I should perform a Ridge regression to fit some given data to a known model. I was wondering if, in this case, there are any advantage in splitting the data into training set and test set. At the end of the day I am performing Ridge regression on a known model, hence there is no risk of overfitting. Is there any other reason to split the data in such way?

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    $\begingroup$ Why do you say there is no risk of overfitting? $\endgroup$ – Dave Feb 5 at 15:59
  • $\begingroup$ Maybe I am wrong, but I assume it since I know the model that generates the data. $\endgroup$ – laklica Feb 5 at 16:09
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    $\begingroup$ How does your knowledge of the data generating process enter into the ridge regression? Ridge regression alone can also overfit. $\endgroup$ – Stephan Kolassa Feb 5 at 16:18
  • $\begingroup$ Because it was given in the statement of the problem. $\endgroup$ – laklica Feb 5 at 17:01
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    $\begingroup$ It's not clear what exactly is meant by "known model". Known to whom? Why does it matter? $\endgroup$ – Andris Birkmanis Feb 5 at 18:45
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Your evaluation should always be performed on a holdout set. In the simplest setups, this reduces to train & test sets. So, you should split. Ridge regression has regularisation mechanics but it may not save you from overfitting.

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