Yup. This has been shown over and over by Meehl and that gang. > The real benefit of any form of machine learning is detecting functional relationships between

Yup. This has been shown over and over by Meehl and that gang. > The real benefit of any form of machine learning is detecting functional relationships between variables (ie, "when this AND NOT that OR this"). It's just that these relationships don't provide a real benefit in 99/100 real world use cases. And the real problem with humans, ironically enough, is that due to narrative fallacy, confirmation bias, etc., humans vastly overvalue the contributions of these special circumstances. If a dumb 2--3 variable rule predicts your friend will go to the movies in the weekend, you are likely to go, "Yeah, maybe. But she complained of that headache this morning, maybe there's something deeper there that also makes them not want to go to the movies." In most cases, you'll be wrong to override the dumb rule. So why do people do this? Well, the original movie-going prediction might only have been a 55 % shot. So 45 % of the time, your friend won't go to the movies, headache or not. But if they don't, you'll think you made the right call considering that headache. If they do go, you'll end up thinking, "Right, of course. They had the headache but their friend really wanted to go so of course they would endure it." In other words, you'll think of a way to frame your incorrect call as the right one. (Hindsight bias.)