In creating a model, more than its accuracy, there should be a balance between the precision and recall of the model depending on the domain of the problem. Is there some sort of a "base rule" when it comes to what the acceptable area under the precision-recall curve is? Can you suggest any good references on this?

Thanks,

Cedric