What do Binary Classification Models target?

Binary classification models are specifically designed to address scenarios where the outcomes are limited to two distinct possibilities. This means that in a binary classification problem, the model's goal is to differentiate between two classes or labels. For instance, a binary classification model might predict whether an email is spam or not spam, whether a customer will churn or not, or whether a transaction is fraudulent or legitimate.

These models use various algorithms to analyze input data and assign it to one of the two outcome categories based on learned patterns. Since the focus is unmistakably on achieving a decision between two clear outcomes, binary classification is particularly effective for situations where the decision-making can be simplified into two paths. This contrasts with other modeling types that deal with multiple classes, continuous numerical predictions, or subjective ratings.