# Logistic Regression

## Definition of Logistic Regression

Logistic regression is a machine learning algorithm used for classification and regression analysis. It is a type of linear regression, where the outcome variable is categorical rather than continuous. Logistic regression is used to predict the probability of a particular event occurring, such as whether or not a customer will churn.

## How is Logistic Regression used?

Logistic Regression is a widely-used supervised machine learning algorithm that is used for classification problems. It is a statistical model that estimates the probability of an event occurring based on certain input features or independent variables. It works by applying a logistic function (also known as the sigmoid function) to the linear combination of the inputs, to output a probability between 0 and 1.

Logistic Regression makes use of maximum likelihood estimation (MLE) to find the best fit parameters that define the logistic function. MLE maximizes the likelihood of predicting correctly given certain input features by calculating how close each prediction is from its true value. The process involves maximizing a function called “likelihood”, which measures how likely it is for a given set of independent variables to produce an observed outcome. In other words, the goal of MLE in Logistic Regression is to find coefficients for each independent variable that minimize the error rate and maximize accuracy when making predictions about unseen data points.

Logistic Regression can be used for a wide range of tasks such as predicting whether an email is spam or not, identifying whether someone will default on loan payments, diagnosing medical conditions based on patient test results, recognizing images and speech, and much more. Generally speaking, Logistic Regression can be used whenever you need to predict discrete outcomes – such as yes/no answers – using one or more explanatory variables. In addition to being one of the most effective models for binary classification tasks it also has good properties with regards to interpretability since it provides an overall understanding of how different factors influence outcomes in an easily interpreted manner.