I have developed a logistic regression model for a classification problem and obtained an AUC (Area Under the Curve) score of approximately 0.9. The model was estimated by splitting the available data using the common 70-30 rule (70% training data, 30% testing data).
Now, I would like to understand if my model is indeed overfitting the training data. In particular, I'm interested in understanding whether or not the good achieved model performance (e.g., ROC-AUC score ~0.9) is due to overfitting.
Could someon guide me on how to evaluate the presence of overfitting in my logistic regression model given these circumstances, please? I thought about applying my model on the testing data to obtain model-implied estimates but such approach would not lead anywhere since the real-world observations of my target variable are not available yet and won't be in the near future.
Any suggestions is welcome. Thanks in advance!