Encyclopedia of Machine Learning

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


Reference work entry
DOI: https://doi.org/10.1007/978-0-387-30164-8_298
The F 1-measure is used to evaluate the accuracy of predictions in two-class (binary)  classification problems. It originates in the field of information retrieval and is often used to evaluate  document classification models and algorithms. It is defined as the harmonic mean of  precision (i.e., the ratio of  true positives to all instances predicted as positive) and  recall (i.e., the ratio of true positives to all instances that are actually positive). As such, it lies between precision and recall, but is closer to the smaller of these two values. Therefore a system with high F 1 has both good precision and good recall. The F 1-measure is a special case of the more general family of evaluation measures:
$${F}_{\beta } = ({\beta }^{2} + 1)precisionrecall/({\beta }^{2}precision + recall)$$
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© Springer Science+Business Media, LLC 2011