## Logistic regression

Despite its name, the *logistic regression is a classifier*. As a matter of fact, the logistic regression is one of the most commonly 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 (or expected) variable *y* and a set of variables *x* that it depends on. Once the model is created (trained), it is available to classify real-time data.

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

### Logistic 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...