What term denotes incorrectly labeling a positive case as negative?

What term denotes incorrectly labeling a positive case as negative?

• A. False Positive (FP)
• B. False Negative (FN)
• C. F1 Score
• D. Area Under the Curve (AUC)

Answer: (B)

In binary classification, when the actual case is positive but the model predicts negative, that mistake is a false negative. It means a real positive case was missed, which hurts recall—the ability to detect positives. The opposite error would be labeling a negative case as positive, which is a false positive. The F1 score and AUC are metrics used to evaluate performance, not the name of the error type itself. So the term for incorrectly labeling a positive case as negative is false negative.

In binary classification, when the actual case is positive but the model predicts negative, that mistake is a false negative. It means a real positive case was missed, which hurts recall—the ability to detect positives. The opposite error would be labeling a negative case as positive, which is a false positive. The F1 score and AUC are metrics used to evaluate performance, not the name of the error type itself. So the term for incorrectly labeling a positive case as negative is false negative.