Encyclopedia of Machine Learning

2010 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Precision and Recall

  • Kai Ming Ting
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_652


 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 use 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.
Precision and Recall. Table 1

The outcomes of classification into positive and negative classes


Assigned Class





Actual Class


This is a preview of subscription content, log in to check access.

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Kai Ming Ting
    • 1
  1. 1.Monash UniversityVicAustralia