The problem with metrics is a big problem for AI
> However, the researchers found that several of the most predictive factors (such as accidental injury, a benign breast lump, or colonoscopy) don’t make sense as risk factors for stroke. So, just what is going on? It turned out that the model was just identifying people who utilize health care a lot.A good analogous example of this is PCA. If your first component has a dominating effect, then this will drown everything out (you can compensate by looking at components 2 and 3). (Examples: [monetary] inflation, year or month effects.) It's a cool exercise to do PCA on datasets and to see whether things like this pop out. This is also why PCA (which does maximisation) is an explorative analysis and should not be used as an authoritative "result" or "metric". You could, but you have to be careful. Even in picking your components you introduce curation and bias.Imagine a scenario where we regress back to the dark ages and the eugenics inclined doctor says: "Sorry Stan, your second component value is an outlier, no kids for you; your genes are not considered adequate."