In a two-class problem, a classification model makes two types of error: false positives and false negatives. A false negative is an example of positive class that has been incorrectly classified as negative. See confusion matrix for a complete range of related terms.
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(2017). False Negative. 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_299
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