> My question is, when refit=True, what does it use to train the final model? X?
As per the documentation says: "Refit an estimator using the best found parameters on the whole dataset." So it refit on the entire `X` using the best parameters found during the cross-validation procedure with respect to a given metric. On Tue, 16 Sept 2025 at 15:27, Sole Galli via scikit-learn < [email protected]> wrote: > Hey team, > > In RandomisedSearchCV or SuccessiveHalving, we can pass indexes in the > fold parameter, if we want to test the hyperparameters on specific folds. > > Say I have a dataset of 12 rows, indexes 1 to 12, and I pass as fold to > the randomized search or SH the following folds: > > [1,2] [9, 10, 11, 12] > [3, 10] [5,6,7,8] > [7, 11] [1,2,3,4] > > It will use [1,2], [3,10] and [7,11] to train the model, and the second > part with the 4 rows as the test set. > > My question is, when refit=True, what does it use to train the final > model? X? the sum of the training folds? the sum of the test folds? > something else? > > It's a follow up question to this reply on SO: > https://stackoverflow.com/questions/79748461/how-to-pass-pre-computed-folds-to-successivehalving-in-sklearn > <https://stackoverflow.com/questions/79748461/how-to-pass-pre-computed-folds-to-successivehalving-in-sklearn?noredirect=1#comment140703041_79748461> > > Thanks a lot! > > Best wishes, > > Soledad Galli > https://www.trainindata.com/ > _______________________________________________ > scikit-learn mailing list -- [email protected] > To unsubscribe send an email to [email protected] > https://mail.python.org/mailman3//lists/scikit-learn.python.org > Member address: [email protected] > -- Guillaume Lemaitre
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