F1 score
Sign in to savethumb|350px|Precision and recall In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly, and the recall is the number of true positive results divided by the number of all samples that should have been identified as positive. Precision is also known as positive predictive value, and
Article
14 sectionsContents
- Etymology
- Definition
- F<sub>β</sub> score
- Diagnostic testing
- Dependence of the F-score on class imbalance
- Applications
- Properties
- Criticism
- Difference from Fowlkes–Mallows index
- Extension to multi-class classification
- Macro F1
- Micro F1
- See also
- References
thumb|350px|Precision and recall In statistical analysis of binary classification and information retrieval systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the precision and recall of the test, where the precision is the number of true positive results divided by the number of all samples predicted to be positive, including those not identified correctly, and the recall is the number of true positive results divided by the number of all samples that should have been identified as positive. Precision is also known as positive predictive value, and recall is also known as sensitivity in diagnostic binary classification.
The F1 score is the harmonic mean of the precision and recall. It thus symmetrically represents both precision and recall in one metric. The more generic F_\beta score applies additional weights, valuing one of precision or recall more than the other.