# The logistic regression

Despite its name, *the logistic regression is a classifier*. As a matter of fact, the logistic regression is one of the most used discriminative learning techniques because of its simplicity and its ability to leverage a large variety of optimization algorithms. The technique is used to quantify the relationship between an observed target variable *y* and a set of variables *x* that it depends on. Once the model is created (trained), it is used to classify real-time data.

A logistic regression can be either binomial (two classes) or multinomial (three and more classes). In a binomial classification, the observed outcome is defined as {true, false}, {0, 1}, or {-1, +1}.

## The logit function

The conditional probability in a linear regression model is a linear function of its weights [6:13]. The logistic regression model addresses the nonlinear regression problem by defining the logarithm of the conditional probability as a linear function of its parameters.

First, let's introduce...