Saying it doesn't make it true. You are claiming there is a generalization problem that causes extra error in practice. Another perfectly viable hypothesis i

Saying it doesn't make it true. You are claiming there is a generalization problem that causes extra error in practice. Another perfectly viable hypothesis is that the classifier is working fine, it's just tuned for true positive rate and accepts a higher false negative rate to get it. Specificity vs. sensitivity is a fundamental trade off, not a training issue (though that can make both worse)