How to study the overfitting of a classification model in my research paper? I do research on a dataset having texts, I split them into 80% training and 20% for testing. I submitted my research article to a journal and the reviewers responded with major revisions that include a suggestion to make an ablation study on overfitting because I stated that I use a specific procedure to prevent overfitting according to some references which claimed that this procedure prevents overfitting. The problem is that I didn't evaluate the validation set before I submit the paper to the journal because I evaluated only the test set on well-known metrics(recall, precision, etc.) and when I tested the model, the learning curve appeared as shown in the below figure. If that is considered overfitting, then my question is what is the best way to address this comment? Thank you in advance.


You should always have a validation set. When I review research papers I'm always a little suspicious of papers that only present performances on the test set: there's a high chance that they tuned their approach/model to the test set which is a big no.
Anyway, the reviewers are asking for a simple ablation study. You basically should evaluate your approach twice - once using the procedure and once without - and compare the performances.
In the future you should always conduct ablation studies. It not only helps you examine contributions of specific components, but it also provides the readers with a better overview of things.
