Yeah, dude, I know. But why is that piece deemed important? Is it truly important? If it is not truly important, then the algorithm is overtrained -- that is

Yeah, dude, I know. But why is that piece deemed important? Is it truly important? If it is not truly important, then the algorithm is overtrained -- that is, it has found "patterns" that exist in the training set but only by random chance, and which would not exist in a sample set. But as I mentioned, there seem like there are reasonable reasons to suspect that this is not just overtraining.