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 asresponse
.- response
(
factor()
)
Predicted response labels. Must have the exactly same two levels and the same length astruth
.- positive
(
character(1))
Name of the positive class.- beta
(
numeric(1)
)
Parameter to give either precision or recall more weight. Default is1
, 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 isNaN
.- ...
(
any
)
Additional arguments. Currently ignored.
Details
With precision()
and recall()
, the F-beta Score is defined as
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 .