What is binary classification?

Binary classification refers to the task of categorizing data into exactly two distinct classes or categories. This type of classification is commonly used in various contexts, such as determining whether an email is spam or not, diagnosing a disease as either present or absent, or classifying images into two groups, such as cats and dogs. In binary classification, the goal is to develop a model that can accurately predict which of the two classes a given input belongs to, based on the features presented in the data.

The primary focus in this type of classification is on making clear and distinct predictions that can be evaluated based on performance metrics, such as accuracy, precision, recall, and the F1 score. Understanding binary classification is essential in AI and machine learning, as it forms the basis for more complex classification tasks and helps in building intuitive models that operate under a straightforward premise of duality.

In contrast, other options such as categorizing data into multiple classes, regression analysis, or clustering methods do not align with the definition of binary classification, each describing different mechanisms or tasks in data analysis and machine learning.