# True Negative Classification

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A True Negative Classification is a binary classifier negative prediction that a correct class prediction.

**AKA:**TN Outcome.**Context:**- It can be a member of a True Negative Classification Set (to calculate a true negative error rate).
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**Counter-Example(s):****See:**True Negative Rate; True Positive Rate.

## References

### 2011

- (Sammut & Webb, 2011) ⇒ Claude Sammut, and Geoffrey I. Webb. (2011). “True Negative.” In: (Sammut & Webb, 2011) p.999

### 2006

- (Fawcett, 2006) ⇒ Tom Fawcett. (2006). “An Introduction to ROC Analysis.” In: Pattern Recognition Letters, 27(8). doi:10.1016/j.patrec.2005.10.010
- QUOTE: Given a classifier and an instance, there are four possible outcomes. If the instance is positive and it is classified as positive, it is counted as a
*true positive*; if it is classified as negative, it is counted as a*false negative*. If the instance is negative and it is classified as negative, it is counted as a*true negative*; if it is classified as positive, it is counted as a*false positive*. Given a classifier and a set of instances (the test set), a two-by-two*confusion matrix*(also called a contingency table) can be constructed representing the dispositions of the set of instances.

- QUOTE: Given a classifier and an instance, there are four possible outcomes. If the instance is positive and it is classified as positive, it is counted as a