[it_does_follow]

I think Bishop et al. WIP book Model-Based Machine Learning[0] is a nice step in the right direction. Honestly the most important thing missing from ML that stats has is the idea that your model is a model of something. That how you construct a problem mathematically says something about how you believe the world works. Then we can ask all sorts of detailed question about "how good is this model and what does it tell me?"

I'm not sure this will ever dominate. As much as I love Bayesian approaches I sort of feel there is a push to make them ever more byzantine, recreating all of the original critiques of where frequentist stats had gone wrong. So essentially we're just seeing a different orthodoxy dominant thinking with all of the same trapping of the previous orthodoxy.

0. https://www.mbmlbook.com/