Determine how good an AUC is (Area under the Curve of ROC) I'm currently working on a project involving using different sets of data as a predictor to predict the outcome of out-sample data. I use AUC (Area under the Curve of ROC) to compare the performances of each set of data.
I am familiar with the theory behind AUC and ROC, but I'm wondering is there a precise standard for assessing AUC, for example, if an AUC outcome is over 0.75, it will be classified as a 'GOOD AUC', or below 0.55, it will be classified as a 'BAD AUC'.
Is there such a standard, or AUC is always for comparing only?
 A: It isn't possible to say because it really depends on the task and the data. For some simple tasks AUC can be 90+, for others ~0.5-0.6.
A: Generally, I would not say so. It all depends on the task, your data set, and objectives. There is no rule of thumb that an AUC value of x.x is defined as a good predicting model.
That being said, you want to achieve as high an AUC value as possible. In cases where you get an AUC of 1, your model is essentially a perfect predictor for your outcome. In cases of 0.5, your model is not really valuable. An AUC of 0.5 just means the model is just randomly predicting the outcome no better than a monkey would do (in theory). I can only recommend you to read more about it if you have not so. This is realtively straightforward. And, here.
A: From the comments:

Calimo: If you are a trader and you can get an AUC of 0.501 in predicting future financial transactions, you're the richest man in the world. If you are a CPU engineer and your design gets an AUC of 0.999 at telling if a bit is 0 or 1, you have a useless piece of silicon.

A: This is a complementary to Andrey's answer (+1).
When looking for a generally accepted reference on AUC-ROC values, I came across Hosmer's "Applied Logistic Regression". In Chapt. 5 "Assessing the Fit of the Model", it emphasised that "there is no “magic” number, only general guidelines". Therein, the following values are given:


*

*ROC = 0.5 This suggests no discrimination, (...).

*0.5 < ROC < 0.7 We consider this poor discrimination, (...).

*0.7 $\leq$ ROC < 0.8 We consider this acceptable discrimination.

*0.8 $\leq$ ROC < 0.9 We consider this excellent discrimination.

*ROC $\geq$ 0.9 We consider this outstanding discrimination.

These values are by no means set-to-stone and they are given without any context. As Star Trek teaches us: "Universal law is for lackeys, context is for kings", i.e. (and more seriously) we need to understand what we are making a particular decision and what our metrics reflect. My guidelines would be:

*

*For any new task we should actively look at existing literature to see what is considered competitive performance. (e.g. detection of lung cancer from X-ray images) This is practically a literature review.

*If our tasks is not present in literature, we should aim to provide an improvement over a reasonable baseline model. That baseline model might be some simple rules of thumb, other existing solutions and/or predictions provided by human rater(s).

*If we have a task with no existing literature and no simple baseline model available, we should stop trying to make a "better/worse" model performance comparison. At this point, saying "AUC-R0C 0.75 is bad" or "AUC-ROC 0.75 is good" is a matter of opinion.

