I am trying to do a binary classification (0 and 1), and in this case, the end goal is to maximise the True Positives (i.e. maximum no. of 1s should be predicted correctly, even if it gives rise to significant False Positives). I am obtaining a ROC curve, with Area Under Curve (AUC) for Class 0 being 0.55 and Class 1 being 0.75.
From what I have seen over SE and over the Internet, a value of AUC of 0.75 is a fair value, however, many resources mention that a value in the range 0.50-0.60 etc. might indicate a worthless/random classifier, but I definitely want to maximise the correct predictions for Class 1 instead of losing out on predictions for 1s which may actually belong to 1s. I would like to know that in this case, is the AUC I obtain of any significance, or does it show that the classifier is not useful. Any advice in this regard is highly appreciated.