F-score

In <a href="/facts/Statistics/DNPsKGYU">statistical</a> analysis of <a href="/facts/Binary_classification/MeYYAMwp">binary classification</a> and <a href="/facts/Information_retrieval/MFSmpeNw">information retrieval</a> systems, the F-score or F-measure is a measure of predictive performance. It is calculated from the <a href="/facts/Precision_(information_retrieval)/Jnmmpl8H">precision</a> and <a href="/facts/Recall_(information_retrieval)/Jnmmpl8H">recall</a> 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 <a href="/facts/Positive_predictive_value/G238SK2V">positive predictive value</a>, and recall is also known as <a href="/facts/Sensitivity_and_specificity/pzc9qfdz">sensitivity</a> in diagnostic binary classification.
The F1 score is the <a href="/facts/Harmonic_mean/ejNdKrvn">harmonic mean</a> of the precision and recall. It thus symmetrically represents both precision and recall in one metric. The more generic 
 
 
 
 
 F
 
 β
 
 
 
 
 {\displaystyle F_{\beta }}
 
 score applies additional weights, valuing one of precision or recall more than the other.
The highest possible value of an F-score is 1.0, indicating perfect precision and recall, and the lowest possible value is 0, if the precision or the recall is zero.

F-score open-in-new

F-score