Overview of Logistic Regression
Logistic regression is a fundamental statistical method used for binary classification problems in machine learning (mostly, but as we’ll see shortly, it can be extended to serve multiple classes and even instances where an observation may belong to multiple classes – multilabel regression).
Unlike linear regression, which predicts continuous outcomes, logistic regression predicts the probability that a given input point belongs to a particular category or class. This is achieved by applying the logistic function (also known as the sigmoid function) to the linear combination of the input features, which transforms the output into a value between 0 and 1. This makes logistic regression particularly useful for scenarios where the outcome variable is categorical, such as determining whether an email is spam or not, or predicting whether a patient has a certain disease...
