I'm building a classifier of multi-class problem. In my context I have to classify vehicles into five categories: cars, vans, trucks, buses and motorcycles. I would like to use some measure to represent the classification performance for each class and for all of them. In the samples I have the motorcycles are few meanwhile there are a lot of cars so the classes are clearly unbalanced. I read that the Matthews correlation coefficient (MCC) is better in this case but I didn't find any reference that confirm this.

Please, could you help me on this I'm a little bit lost and I don't know which measure to use.


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https://eva.fing.edu.uy/pluginfile.php/69453/mod_resource/content/1/7633-10048-1-PB.pdf I think this will be a good reference for you.

To my understanding, F-measure is a "mean" of precision and recall (2*precision*recall/(precision+recall)). For an unbalanced testing set, precision, recall, or F-measure could be confusing and misleading because a random guessing would make F-measure higher than 0.5. But MCC will give you a value around 0 in this situation. So I think MCC is a better choise in your case.


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