Logistic regression uses a logistic function to model a binary dependent variable. It estimates the probability that a given input point belongs to a certain class. This is achieved by applying a sigmoid function to the linear combination of the input features, thus squashing the output to a range between 0 and 1. The result can then be interpreted as the probability of belonging to the positive class.