What type of problem is logistic regression primarily suited for?

Logistic regression is primarily suited for binary classification problems. This statistical method is used to model the probability of a binary outcome based on one or more predictor variables. It effectively estimates the relationship between the dependent variable (which has two possible outcomes, often coded as 0 and 1) and one or more independent variables.

The core principle behind logistic regression is the logistic function, which converts the linear combination of the input variables into a probability that falls within the range of 0 to 1. This makes it ideal for scenarios where the goal is to classify observations into two distinct classes, such as predicting whether an email is spam or not, whether a patient has a certain disease, or determining if a customer will purchase a product.

While logistic regression can be extended to handle multiple classes through techniques like one-vs-all (also known as one-vs-rest) or softmax regression, its foundational purpose is built around binary outcomes, making it a primary choice for binary classification problems. This ability to predict probabilities and classify data points into two categories is what firmly establishes logistic regression as an effective tool in the realm of classification tasks.