What is multi-label classification in supervised learning?

Multi-label classification allows each instance to belong to multiple classes simultaneously.

Unlike multi-class classification where each instance belongs to exactly one class, multi-label classification assigns multiple labels to a single instance. Each label is treated independently or modeled jointly. Common approaches include Binary Relevance, Classifier Chains, and deep learning sigmoid outputs. It is widely used in text tagging and image annotation tasks.