True Negative Classification
- AKA: TN Outcome.
- See: True Negative Rate; True Positive Rate.
- (Sammut & Webb, 2011) ⇒ Claude Sammut, and Geoffrey I. Webb. (2011). “True Negative.” In: (Sammut & Webb, 2011) p.999
- (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.