0
$\begingroup$

I am working with 15k image dataset for binary classification. This is a patient-based medical image dataset. Is it enough to use a randomized holdout strategy (train, validation, and test)? Should I use k-fold cross-validation (test and train(10-fold))? Which is best for this amount of image dataset. TIA

$\endgroup$

1 Answer 1

1
$\begingroup$

The benefit of k-fold is that it gives you a better idea of how your model will generalise in the real world.

If you plan to make a model that is useful in the real world I recommend using a k-fold cross validation approach (or a leave p out approach if you have time), so that you can construct some nonparametric confidence intervals for your model.

In each fold you can split the training data into training and validation if you need a validation set, e.g. for early stopping.

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.