Specificity in machine learning refers to which of the following?

Specificity in machine learning is defined as the measure of a model's ability to correctly identify negative cases while minimizing the number of false positives. It evaluates how well a system can discriminate between true negatives (correctly identified negatives) and false positives (incorrectly identified positives). High specificity indicates that there are few false positives, meaning that when the model predicts a negative outcome, it is likely correct.

This concept is crucial in various fields, including healthcare, where distinguishing between positive and negative cases can significantly impact diagnosis and treatment. In the context of AI in dentistry, for instance, a model with high specificity would effectively recognize patients who do not have a certain dental condition, thereby preventing unnecessary anxiety or treatment.

The other options encompass different aspects of machine learning. The measure of true positives relates to sensitivity, the overall accuracy involves both true positive and true negative rates, and the harmonic mean of precision and recall refers to the F1 score, which balances the trade-off between precision and recall but does not directly concern specificity itself.