Model fitting is only a useful analogy only in the very abstract sense of "is there something missing in my model". But usually people mean model fitting in the

Model fitting is only a useful analogy only in the very abstract sense of "is there something missing in my model". But usually people mean model fitting in the sense of choosing parameters in some restrictive model schema (i.e. selecting from a set of machine learning models), but those typically don't provide any meaningful understanding of the system, unless you apply a much weaker definition of "explanatory" or "understanding" than is typical. From my perspective, when you fit a linear regression I don't think you have any understanding of why the parameters are what they are, in the same way that I don't think a coder who tweaks constants in a buggy program until it works has any understanding why those constants are what they are. > I would be interested to see a counter example - a small model that fits a particular set of data well, but does not provide any explanatory power. As I said above, a linear regression typically doesn't lend itself to understanding. In fact, I would be interested in an example of a statistical model that does provide any meaningful explanation. Most ML and AI researchers don't even seem to pursue scientific understanding as a goal; predictive power is their measure of success. That is Chomsky's criticism.