I am thinking about the prospective application of a trained classifier in a real-world context. We know that when we do over/under-sampling to balance our dataset, we never touch the testing set as we want to keep our dataset's real behaviour. But the part that I do not understand is the role of the test set's distribution in a classifier's performance.
Let's say I have a model that can label an email as spam or non-spam. If I launch this model in my email-service, in a specific time window, all the emails that my classifier receives might be non-spam. But the trained model has a 50-50% distribution for each category. My question is,
does this difference in the distribution--during the prospective application-- change the performance of the model? e.g. if my web-service receives 5 spams and 5 non-spams in that time window, should I receive a more accurate classification? Based on my understanding, the answer should be a No. Still, I see everywhere that people are talking about the importance of the test distribution and its role in the performance and accuracy of predictive models.