Pretty simple question which should lead to a quick answer. Suppose we are building a parametric model - let's just say OLS linear or logistic regression.
After we do cross validation and examine the test MSEs to determine the model specification that produces the lowest test MSE, how do we determine the parameters of our final model?
- Let's say we did k-fold CV and thus we would have k training models. Would the coefficients of the final model be the mean or median of the k coefficients?
- Or, do we just take our entire sample data (combine our test and validation sets) and produce a model with the specifications from above and use those coefficients? Link seems to suggest this method: How to choose a predictive model after k-fold cross-validation?
- Or another method?