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Precision is defined as the ratio of true positives (TP) and the total number of positives 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.
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Ting, K.M. (2017). Precision. 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_658
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DOI: https://doi.org/10.1007/978-1-4899-7687-1_658
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Publisher Name: Springer, Boston, MA
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