Yes that's correct: there's a subtlety here that I think is worth mentioning:

If you're doing multi-label classification, and have ![image](https://cloud.githubusercontent.com/assets/3706681/2935898/d73ff160-d833-11e3-97f7-2d66a8d7bd09.png) and ![image](https://cloud.githubusercontent.com/assets/3706681/2935901/e0d42d0e-d833-11e3-8b2d-24793bb4950c.png) (let's say) as your class variables, then effectively your classification problem is assigning a label ![image](https://cloud.githubusercontent.com/assets/3706681/2935902/fd950ca6-d833-11e3-8fc8-9934e242dd18.png) to one class variable and ![image](https://cloud.githubusercontent.com/assets/3706681/2935904/1f7621de-d834-11e3-8637-9fd886e05984.png). Given that, you can still pose this a single-class problem by constructing a new class variable ![image](https://cloud.githubusercontent.com/assets/3706681/2935906/388d4d5a-d834-11e3-86e1-0065b39e355e.png) and predicting a joint label ![image](https://cloud.githubusercontent.com/assets/3706681/2935909/4ff3198e-d834-11e3-85c3-1d759c253464.png). Having *ClassIndex* as a slice rather than a value means you effectively have to deal with this possibility in every single classifier, so it might be better to create a pre-processing step which can do this. 

For regression-type problems, it indeed be more appropriate to jointly predict multiple values - perhaps a special *Attribute* type for those situations?