Thanks for your reply Professor Neal. Why does it make sense to use the test-set performance to judge performance on an arbitrary/future dataset? Does the test-set have some other interpretation that I am missing? If we wanted to judge future performance on real data or compare two model by future performance on real data then shouldn’t we just calculate the most likely performance on a arbitrary dataset?
I’m not sure what you’re asking here. The test set should of course be drawn from the same distribution as the future cases you actually care about. In practice, it can sometimes be hard to ensure that. But judging by performance on an arbitrary data set isn’t an option, since performance in the future does depend on what data shows up in the future (for a classification problem, on both the inputs, and of course on the class labels). I think I’m missing what you’re getting at....
Thanks for your reply Professor Neal. Why does it make sense to use the test-set performance to judge performance on an arbitrary/future dataset? Does the test-set have some other interpretation that I am missing? If we wanted to judge future performance on real data or compare two model by future performance on real data then shouldn’t we just calculate the most likely performance on a arbitrary dataset?
I’m not sure what you’re asking here. The test set should of course be drawn from the same distribution as the future cases you actually care about. In practice, it can sometimes be hard to ensure that. But judging by performance on an arbitrary data set isn’t an option, since performance in the future does depend on what data shows up in the future (for a classification problem, on both the inputs, and of course on the class labels). I think I’m missing what you’re getting at....