I should mention that even though I have some basic knowledge regarding ML, it is the first big ML project I am working on and for the proposal of my research project I need to suggest an evaluation metric.
The problem is a multiclass(16 classes) classification problem where one data point can be classified in multiple classes (not ranking based though). I plan to model it as a binary classification problem for each class but for the related evaluation metrics I was not able to find a proper application. So, first of all, should I evaluate individual performance for each class (how well class A classification is working), should I go for a general evaluation (This data point belongs A,B,C but at the end classified for A and B only), or both? Second, what kind of metrics can I have a look at? Finally, I haven't started working on the data yet but I expect an unbalanced distribution for my classes. Would it affect my results?