Negative Predictive Value (NPV) is defined as a ratio of true negatives to the total number of negatives predicted by a model. This is defined with reference to a special case of the confusion matrix with two classes – one designated the positive class and the other the negative class – as indicated in Table 1.
NPV can then be defined in terms of true negatives and false negatives as follows.
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(2017). Negative Predictive Value. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_582
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