True Negative Classification

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).
  • …
• Counter-Example(s):
  • True Positive Classification.
  • False Negative Classification.
• See: True Negative Rate; True Positive Rate.

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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.

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