If you regularly read this blog then you know I am not one to jump on the “AI Bandwagon”, being quickly weary of anyone flashing the “It’s Artificial Intelligence” joker card. Don’t get me wrong, I understand it is a sexy term I, but to me it always feels a bit like a sales pitch.
If the machine does anything (artificially) intelligent it means that the model at the back is complex, and complex models need massive (massive I say) amounts of data. This is because of the infamous Curse of dimensionality.
I know it. You know it. Complex models need a lot of data. You have read this fact, even wrote it at some point. But why is it the case? “So we get a good estimate of the parameter, and a good forecast thereafter”, you reply. I accept. But.. what is it about simple models that they could suffice themselves with much less data compared to complex models? Why do I always recommend to start simple? and why the literature around shrinkage and overfitting is as prolific as it is?