Archive for November, 2010

This question appears very trivial and might just be meaningless, so I put it up at the risk of embarrassing myself. However, since it is a genuine question. I still put it up. All thoughts are welcome.

Question: When trying to quantify the performance of a classifier. What advantages does RMSE offer over the Area under the Curve under the ROC metric? And what does the AUC offer that the RMSE does not? I find AUC very intuitive and prefer using it for classification tasks. But can I give a theoretical reason for using it above RMSE and the vice versa? Review committees have different preferences, some journals prefer reporting the RMSE while some prefer the AUC, some ask for both. Another example being – The 2010 KDD Cup used RMSE while the 2010 UCSD data mining competition used AUC.

Or is this a bad question to ask?

To paraphrase my question – What can be instances in which a classifier is deemed as “good” by the AUC measure and “not so good” by the RMSE measure. What would be the exact reason for such a different “opinion”? And in what situations should I use AUC and in what situations should I use RMSE?

Some Background : If they are equivalent, then you would expect a strong linear relationship (with a negative correlation). That means that for a perfect classifier RMSE would be zero and the AUC 1.

I always use both for all purposes. Here is a sample graph.

RMSE versus AUC for a classifier on some Intelligent Tutoring Data

This is actually a very typical graph, and there are no surprises with it. If you leave out some “bad examples” such as those at (0.4, 0.65) and (0.38, 0.7), the graph has a good negative correlation (as measured by the line fit).

So, the question remains for me. What are the advantages and disadvantages of both?

Recommendations :

1. ROC Graphs : Notes and Practical Considerations for Researchers – Tom Fawcett


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