  Multi-label classification is a type of classification task, it is used when
there are two or more classes, and the data point we want to predict may belong
to none of the classes or all of them at the same time. In the real world, many
applications are actually multi-label involved, including information
retrieval, multimedia content annotation, web mining, and so on. A game
theory-based framework known as SHapley Additive exPlanations (SHAP) has been
applied to explain various supervised learning models without being aware of
the exact model. Herein, this work further extends the explanation of
multi-label classification task by using the SHAP methodology. The experiment
demonstrates a comprehensive comparision of different algorithms on well known
multi-label datasets and shows the usefulness of the interpretation.
