Logistic regression

Summary

Logistic regression models the probability of a binary outcome as the logistic function of a linear combination of regressors. Equivalently, the log-odds of the outcome is linear in the regressors. So each coefficient is the change in log-odds for a unit change of its regressor. It is the generalized linear model for Bernoulli responses with the canonical logit link, and it is fitted by maximum likelihood. It is one of the most used models in medicine, the social sciences and machine learning, where it is a basic linear classifier.