Which term denotes incorrectly flagging a negative case as positive?

Which term denotes incorrectly flagging a negative case as positive?

• A. False Positive (FP)
• B. False Negative (FN)
• C. Accuracy
• D. Precision

Answer: (A)

In binary decision making, mislabeling a negative case as positive is a false positive. This error happens when the system or test indicates a positive result even though the true condition is negative, which can inflate the number of positives and affect metrics like the false positive rate and precision. In a confusion matrix, it’s the instance where the predicted positive doesn’t match the actual negative. The other terms don’t describe this specific error: a false negative is when a positive case is incorrectly labeled negative; accuracy is the overall rate of correct predictions, not the nature of the misclassification; precision is the proportion of predicted positives that are truly positive, not the error type itself.

In binary decision making, mislabeling a negative case as positive is a false positive. This error happens when the system or test indicates a positive result even though the true condition is negative, which can inflate the number of positives and affect metrics like the false positive rate and precision. In a confusion matrix, it’s the instance where the predicted positive doesn’t match the actual negative.

The other terms don’t describe this specific error: a false negative is when a positive case is incorrectly labeled negative; accuracy is the overall rate of correct predictions, not the nature of the misclassification; precision is the proportion of predicted positives that are truly positive, not the error type itself.