I have done a 10 fold Cross Validation on my data and have selected the best model from the results. With cross validation, I will have 10 models trained from different folds of the data. For the final model to use, should I take the average of the models or just fit a model for the entire training set?
$\begingroup$
$\endgroup$
$\begingroup$
$\endgroup$
Typically you would use the best model parameters and then re-run the model with the portion of the data set aside for training to come up with a new 'best' that you can run against your test set.
$\begingroup$
$\endgroup$
I will suggest you to read a post on K-Fold CV
Once we have mean score of each model, we generally select the best model out of it.
