How to interpret PR and ROC Curve for an unbalanced test set 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.
For binary-class classification problems and data being highly unbalanced, 


Go for AUC and f1 score as metrics.
Plot the confusion matrix.
Split the data into train:valid:test::60:20:20 or 80:10:10, and do Cross-validation and hyperparameter tuning on train and valid sets. Then go for test set.
You could also try bootstrap resampling.