Deriving GLMs

A cool property of ML is that we can interpret the logits, outputs of the model, as anything we want. This leads to all sorts of rich interpretations . Well, almost anything. The interpretations from logits don’t actually come from thin air, but derived from a few simple assumptions about the prior distribution and linear relationship. So from here our interpretation of logits, and our subsequent…