> This sounds like it's approaching some sort of model mysticism. Models don't make forecasts, humans do. By forecasts I only mean output of a model, I've been

> This sounds like it's approaching some sort of model mysticism. Models don't make forecasts, humans do. By forecasts I only mean output of a model, I've been wrapped up in time series methods where that's the usual term for model outputs. Assigning confidence to the conclusions drawn by an analyst using some model as a tool is a different task that may or may not roll up formal model output uncertainties and usually involves a lot of subjectivity. This is an important thing too, but is downstream. Uncertainty is inherently tied to a specific model, since it characterizes how the model propagates uncertainty of inputs and its own fit/structure/assumptions onto its outputs. If you aren't building uncertainties contingent on the characteristics of a specific model then it isn't an uncertainty. But there's no mysticism about models possibly being unintuitive, most of the popular model forms nowadays are mystery black boxes. Some function fit to a specific dataset until it finds a local minimum in a loss function that happens to do a good job (simplifying). There's plenty of work that shows ML models often exploit features and correlations that are highly unintuitive to a human or are just plain spurious.