Binary classification is a type of machine learning task where the goal is to categorize data into one of two distinct classes. For example, a model might be trained to identify whether an email is spam or not spam. This process involves using algorithms that analyze features of the data to make predictions based on learned patterns. In binary classification, the output is typically represented as a 0 or 1, where each number corresponds to one of the two classes. Common algorithms used for binary classification include logistic regression, support vector machines, and decision trees. These models help automate decision-making processes in various applications, such as medical diagnosis and fraud detection.