Would you say lack of domain knowledge is what makes stats and CS not always the best majors for ML/AI? It seems like problem formulation is critical for applied modeling and this stuff is not easy to learn if all you learn is tools

The whole explainability thing is becoming ridiculous, because all these fancy techniques while explainable are still not going to be explainable to someone without math knowledge.

And even simple regressions have problems like Table 2 fallacy. Completely overrated

Meanwhile in most typical business settings summary stats/t tests, linear, logistic, and random forest/xgboost is all thats used