AUROC too high in image classification I'm dealing with an image classification problem, with a multiclass imbalanced dataset (the bigger class has 4000 samples and the smaller has 110 samples) with 50 classes and 24000 samples.
I'm using a neural network for doing the task. I'm adopting a set o metrics for evaluating the model. Below I provide the metrics with the values achieved by my model:

*

*accuracy: 0.93

*macro-f1: 0.86

*weighted f1: 0.93

*macro precision: 0.87

*weight precision: 0.93

*macro recall: 0.85

*weighted recall: 0.93

*macro AUROC: 0.99

*weighted AUROC: 0.99

These values are averages obtained in a 5-fold cross-validation process. That is, the metrics are obtained by classifying each test fold. After that, the values of each metric are averaged.
However, I'm not confident about the values for AUROC (macro and weighted). I think that these values are "too good to be correct".
What do you think? Can you provide some guidelines for ensuring that this is correct for finding my error? Is AUROC suiable for evaluating the model in this context?

It may be that your minority class(es) have poor performance in the hard classification (obtained by choosing the class with the largest predicted probability), but that their predicted probabilities rank-order well.  The former would explain low macro-averaged hard classification scores compared to high weighted-averaged scores, and the latter would explain why AUROC is not so encumbered.
