Definition
Precision and recall are the measures used in the information retrieval domain to measure how well an information retrieval system retrieves the relevant documents requested by a user. The measures are defined as follows:
Precision = Total number of documents retrieved that are relevant/total number of documents that are retrieved
Recall = Total number of documents retrieved that are relevant/total number of relevant documents in the database
We can employ the same terminology used in a confusion matrix to define these two measures. Let relevant documents be positive examples and irrelevant documents, negative examples. The two measures can be redefined 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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© 2016 Springer Science+Business Media New York
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Ting, K.M. (2016). Precision and Recall. In: Sammut, C., Webb, G. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7502-7_659-1
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DOI: https://doi.org/10.1007/978-1-4899-7502-7_659-1
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