In my view, it is almost impossible to successfully optimize both accurate prediction of previously unseen observations, and understanding of the phenomenon. Better to focus appropriately.---ljubomir
ljumboir wrote this comment to explain an answer he wrote that compares Machine Learning to Statistics:
I'll argue that the distinction between machine learning and statistical inference is clear. In short, machine learning = prediction of future observations; statistics = explanation.
Ijumboir's answer is excellent, and his distinction between prediction and explanation makes a lot of intuitive sense. But I am confused why that distinction even exist in the first place. Why is it so hard to both predict the future and explain the present? I would have thought that if you can predict something, you can also explain it (and vice versa), but it seems that isn't true in the real world...and I'm curious why that's the case.