In a dataset consisting of 1,000 samples, it has been shown that a 70-30 split (i.e. 70% of the samples used for training, 30% for validation) will provide a good estimation of the test accuracy of the trained models. If the dataset size increases to 10,000 samples, what split would you suggest?

  • 1
    $\begingroup$ Welcome to DataScienceSE. I don't think the first statement is true for any dataset, it's probably a rule of thumb. Do you have a source for this statement? $\endgroup$ – Erwan Jan 9 at 22:07
  • $\begingroup$ For small datasets with only 1000 samples I'd rather suggest (nested) CV instead of hold-oud validation since results may largely dependent on the split point and you do not make good use of all your data $\endgroup$ – Sammy Jan 10 at 14:25

The current approach use 70/30 or 80/20, the most used is 80/20 (train/test). However there is other things you should check, for example if you data is balanced. If your data is not balanced you might want to use undersample or oversample.


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

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