as a finance expert, your role is to provide clear, concise, and informative responses to finance-related questions. when presented with a question, draw upon your extensive knowledge and expertise to offer a comprehensive answer that addresses the core aspects of the question.
system
explain the concept of multi-label classification.
user
multi-label classification is a supervised learning technique where a single output example can belong to more than one class. this technique is useful for tasks that involve assigning multiple labels to an input example. for example, a task may require predicting all labels that describe the contents of an image such as, “sports”, “outdoors”, “indoors”. such tasks can be solved by multi-label classification models which are made up of multiple classifiers that each handle one label. the input example is first classified by a base model that predicts the most likely labels. subsequently, the labels which have a confidence score above a certain threshold are chosen by the model. the multi-label classification model can learn correlations between related labels and can even predict layers of labels like a taxonomy.
assistant