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Should AUC be low or high?
The Area Under the Curve (AUC) is the measure of the ability of a classifier to distinguish between classes and is used as a summary of the ROC curve. The higher the AUC, the better the performance of the model at distinguishing between the positive and negative classes.
Is ROC AUC sensitive to class imbalance?
The ROC AUC is sensitive to class imbalance in the sense that when there is a minority class, you typically define this as the positive class and it will have a strong impact on the AUC value. This is very much desirable behaviour. Accuracy is for example not sensitive in that way.
How to get low ROC AUC but high accuracy?
Just as an extreme example, if 87% of your labels are 0’s, you can have a 87% accuracy “classifier” simply (and naively) by classifying all samples as 0; in such a case, you would also have a low AUC (fairly close to 0.5, as in your case). For a more general (and much needed, in my opinion) discussion of what exactly AUC is, see my other answer.
What happens when AUC is 0.5 or higher?
When AUC=0.5, then the classifier is not able to distinguish between Positive and Negative class points. Meaning either the classifier is predicting random class or constant class for all the data points. So, the higher the AUC value for a classifier, the better its ability to distinguish between positive and negative classes.
What should the AUC be for a random classifier?
A random classifier will have an AUC close to 0.5. This is easy to understand: for every correct prediction, the next prediction will be incorrect. Usually, the AUC is in the range [0.5, 1] because useful classifiers should perform better than random.
What’s the difference between ROC and AUC curves?
AUC – ROC curve is a performance measurement for classification problem at various thresholds settings. ROC is a probability curve and AUC represents degree or measure of separability. It tells how much model is capable of distinguishing between classes. Higher the AUC, better the model is at predicting 0s as 0s and 1s as 1s.