Specificity
Specificity is a performance metric that measures how effectively a model identifies negative cases. It represents the proportion of actual negative cases that are correctly classified as negative.
What is Specificity?
Specificity, also called the True Negative Rate (TNR), measures how many actual negative cases a model correctly identifies. It is calculated as TN / (TN + FP), where TN represents true negatives and FP represents false positives. Higher specificity means the model produces fewer false positive classifications.
Why is Specificity Important?
Specificity is important when incorrectly flagging a negative case as positive can create unnecessary alerts, costs, or interventions. It is often evaluated alongside sensitivity to understand how well a model distinguishes between positive and negative cases.
Common use cases
Specificity is commonly used in medical diagnosis, fraud detection, security monitoring, spam detection, and classification systems where minimizing false positives is important.