Logistic regression is used to estimate the probability of an event occurring, based on a given dataset of independent variables. This logit model is often used for classification and predictive analytics, and since this outcome is a probability, the dependent variable is bounded between 0 and 1. Thus, in logistic regression, data is divided into regions using the sigmoid curve. Essentially, predictions fall into classes based on probability. The Sigmoid Activation Function and the Sigmoid Neuron are implementations of logistic regression.
