Multi-Label Classification
A predictive modeling task where a single data instance can be simultaneously assigned to multiple overlapping categories or classes.
Think of It Like This
Like tagging a movie on Netflix as both 'Action', 'Sci-Fi', and 'Thriller' rather than forcing it into one single genre.
Unlike multi-class classification (where classes are mutually exclusive), multi-label models use independent sigmoid activations in the final layer. This allows the model to output a distinct probability for every class independently. It is widely used in text categorization, medical diagnosis, and image tagging.