In binary classification, which term describes a correctly identified negative case?

In binary classification, which term describes a correctly identified negative case?

• A. False Positive (FP)
• B. True Negative (TN)
• C. False Negative (FN)
• D. True Positive (TP)

Answer: (B)

A true negative arises when the true label is negative and the model also predicts negative. In a confusion matrix, TN is the count of instances where the actual class is negative and the predicted class is negative. This is distinct from false positives (predicted positive when actually negative), false negatives (predicted negative when actually positive), and true positives (predicted positive and actually positive). For example, in disease screening where negative means healthy, a healthy person correctly identified as healthy is a true negative.

A true negative arises when the true label is negative and the model also predicts negative. In a confusion matrix, TN is the count of instances where the actual class is negative and the predicted class is negative. This is distinct from false positives (predicted positive when actually negative), false negatives (predicted negative when actually positive), and true positives (predicted positive and actually positive). For example, in disease screening where negative means healthy, a healthy person correctly identified as healthy is a true negative.