I have this confusión matrix results:
True Negatives : 725 False Positives : 74 False Negatives : 62 True Positives : 139 precision recall f1-score support False 0.92 0.91 0.91 799 True 0.65 0.69 0.67 201 accuracy 0.86 1000 macro avg 0.79 0.80 0.79 1000 weighted avg 0.87 0.86 0.87 1000
And plotting the ROC curve I got this:
MCC (Matthews correlation coefficient) = 0.6412
Is clear that according to the literature, the classifier is doing perfect, but I'm skeptical considering that I got the same image (with a different confusion matrix) with a threshold of 0.5 and even with 0.2.
I'm not sure if I'm doing well and if AUC ROC is the more appropriated to measure my classifier. Considering this is my first time dealing with AUC ROC curves, what else should I do to resolve my concerns?