Irls

logistic regression is the method that seems only ever to be used for classification yet insists on calling itself regression. the resolution: it is regression — of the log-odds of a bernoulli success probability onto a linear predictor. 𐃏 this page develops it the honest way, as a generalised linear model: bernoulli response, canonical logit link, likelihood fitted by fisher scoring, inference through the deviance. the machine-learning reading (cross-entropy loss, linear decision boundaries) falls out at the end as a corollary.