multilabelcapable
Multilabelcapable is an adjective used to describe systems, models, or datasets that support multilabel classification, a task where each instance can be associated with multiple labels. This contrasts with single-label classification, where each instance has only one label. A multilabelcapable component typically provides an output structure enabling multiple active labels and a training workflow that accounts for label co-occurrence and possible dependencies.
In practice, multilabelcapable models use a representation such as a binary indicator matrix for labels. An
Predictions typically yield per-label scores, followed by thresholding to decide active labels, or joint models that
Common evaluation metrics for multilabelcapable systems include subset accuracy, which requires all labels to match exactly,
Applications span computer vision, natural language processing, and bioinformatics, including image tagging, music genre tagging, multi-topic