I have trained a neural network on a dataset, the test set is very unbalanced, ratio between positive examples and negatives is 1:25000. All positive examples are correctly predicted, instead negatives elements correctly predicted are 99% of total negatives.
Plot of PR and ROC curves are those:
What can be inferred from these curves? Those are my firsts works with classifiers and i'm confused. I think that precision is always low, because the negatives that are wrong predicted as positive have an high score assigned by the classifier (close to 1). ROC instead i think that is high because all positive examples are correctly predicted. These are my suppositions, correct me if I am wrong.