When do we say that the dataset is not classifiable?
Take a sample element from one class and a sample element from the other class. Is it possible for these two elements to have the exact same feature vector? If that can ever happen then the two classes are not completely seperable using your current feature vectors (since the classification decision is based entirely in the feature vector for a given element).

On the other hand, if *every" element in one class has a corresponding element in the other class such that the two elements have the same feature vectors, then the two classes are indistinguishable using your current feature vectors. 

Furthermore, if that condition holds for only some of your elements and not others, then you are somewhere in between, and you can use that as a basis to measure how well you can hope a classifier to perform using your current feature set. 

All of these evaluations can be used to argue to varying degrees that you need to extract more features.