What type of prediction model is logistic regression designed for?

Logistic regression is specifically designed for binary classification tasks, where the goal is to predict one of two possible outcomes based on a set of predictor variables. The model uses the logistic function to constrain the predicted values between 0 and 1, allowing for the interpretation of these values as probabilities.

In a binary classification problem, logistic regression estimates the likelihood that an instance belongs to a particular class. The output is typically expressed in terms of odds, which can be easily translated into a probability score. For example, predicting whether an email is spam or not is a classic binary classification problem suitable for logistic regression.

While logistic regression can be extended to multi-class classification through techniques like one-vs-all (also known as one-vs-rest), its foundational purpose is to address scenarios where there are only two classes. This focus on binary outcomes distinguishes it from other types of models, such as those used for regression analysis, which predict continuous numerical outcomes, or clustering models, which group data points into clusters rather than predicting a specific outcome.