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Specificity in machine learning measures the proportion of actual negatives correctly identified by the model (True Negatives) out of all actual negatives (True Negatives + False Positives). It's also known as the True Negative Rate.

Importance of Specificity

Specificity is critical in scenarios where the cost of false positives is high. It is often used alongside sensitivity to provide a comprehensive view of a model's performance in classification tasks.

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