Measure to compare true observed labels with predicted labels in binary classification tasks.

## Arguments

- truth
(

`factor()`

)

True (observed) labels. Must have the exactly same two levels and the same length as`response`

.- response
(

`factor()`

)

Predicted response labels. Must have the exactly same two levels and the same length as`truth`

.- positive
(

`character(1))`

Name of the positive class.- beta
(

`numeric(1)`

)

Parameter to give either precision or recall more weight. Default is 1, resulting in balanced weights.- na_value
(

`numeric(1)`

)

Value that should be returned if the measure is not defined for the input (as described in the note). Default is`NaN`

.- ...
(

`any`

)

Additional arguments. Currently ignored.

## Details

With \(P\) as `precision()`

and \(R\) as `recall()`

, the F-beta Score is defined as $$
(1 + \beta^2) \frac{P \cdot R}{(\beta^2 P) + R}.
$$
It measures the effectiveness of retrieval with respect to a user who attaches \(\beta\) times
as much importance to recall as precision.
For \(\beta = 1\), this measure is called "F1" score.

This measure is undefined if precision or recall is undefined, i.e. TP + FP = 0 or TP + FN = 0.

## References

Rijsbergen, Van CJ (1979).
*Information Retrieval*, 2nd edition.
Butterworth-Heinemann, Newton, MA, USA.
ISBN 408709294.

Goutte C, Gaussier E (2005).
“A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation.”
In *Lecture Notes in Computer Science*, 345–359.
doi:10.1007/978-3-540-31865-1_25
.