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Last update: February 19, 2014

 

Negative Predictive Value

The negative predictive value is widely used in medicine and consists of the percentage of people with a negative diagnostic test who do not have the disease. More formally, the negative predictive value NPVi of an individual model i is evaluated by the equation:

where TNi and FNi represent, respectively, the number of true negatives and false negatives.

True positives (TP), true negatives (TN), false positives (FP), and false negatives (FN), are the four different possible outcomes of a single prediction for a binomial classification task with classes “1” (“yes”) and “0” (“no”). A false positive is when the outcome is incorrectly classified as “yes” (or “positive”), when it is in fact “no” (or “negative”). A false negative is when the outcome is incorrectly classified as negative when it is in fact positive. True positives and true negatives are obviously correct classifications. These four types of classifications are usually shown in a two-way table called the confusion matrix.


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