Multi-Label Classification

Multi-label classification is a type of supervised learning problem where each instance can be associated with multiple labels. It is an extension of single-label classification (i.e., multi-class or binary classification) aimed at improving the accuracy and comprehensiveness of classification by predicting all relevant labels for an instance. It is widely applied in areas such as image annotation, text classification, and recommendation systems.