My model has an AUROC value of 0.7, and I have a 75:25 class (75% negative, 25% positive) imbalance. From my understanding, AUROC is calculated by using different thresholds for considering the prediction probability as positive. I was wondering if the interpretation of the AUROC score is affected by imbalanced classes (ie. would I interpret it differently if my data was split 50-50)? Essentially, what (if anything) can I say about my model's performance?
Also, I do not not fully understand the straight line on the AUROC curve that represents the random classifier. How do we know that this is a random classifier? By random, does this mean a classifier that essentially guesses and predicts the positive class with 0.5 probability and the negative class with 0.5 probability?