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No. The model has a problem with overfitting. That’s my point.

• ska 8y ago
  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)