I am studying multi-label learning methods, where for a given observation, you can assign more than one (a set of) target labels. One example is multi-label k-Nearest Neighbor.
I am seeking a way to describe to a panel of people unfamiliar with multi-label learning methods, a way to visualize how they work. For multi-label kNN, I need a visualization, much like the single-label multi-label approach found here: How to plot decision boundary of a k-nearest neighbor classifier from Elements of Statistical Learning?.
Note: This is not a duplicate of the above question (that I linked to above), because this is a multi-label version of kNN. The single-label solution is an intuitive visualization process, but the multi-label version is giving me trouble.
Can anyone help me understand how to visualize the predictions from a multi-label k-Nearest Neighbor classifier?