Binary classification measure defined with $$P$$ as precision() and $$R$$ as recall() 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.

fbeta(truth, response, positive, beta = 1, na_value = NaN, ...)

Arguments

truth :: factor() True (observed) labels. Must have the exactly same two levels and the same length as response. :: factor() Predicted response labels. Must have the exactly same two levels and the same length as truth. :: character(1) Name of the positive class. :: numeric(1) Parameter to give either precision or recall more weight. Default is 1, resulting in balanced weights. :: 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.

Value

Performance value as numeric(1).

Note

This measure is undefined if

• TP = 0

• precision or recall is undefined, i.e. TP + FP = 0 or TP + FN = 0.

Meta Information

• Type: "binary"

• Range: $$[0, 1]$$

• Minimize: FALSE

• Required prediction: response

References

Sasaki Y, others (2007). “The truth of the F-measure.” Teach Tutor mater, 1(5), 1--5. https://www.cs.odu.edu/~mukka/cs795sum10dm/Lecturenotes/Day3/F-measure-YS-26Oct07.pdf.

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

Other Binary Classification Measures: auc(), bbrier(), dor(), fdr(), fnr(), fn(), fomr(), fpr(), fp(), mcc(), npv(), ppv(), tnr(), tn(), tpr(), tp()
set.seed(1)
fbeta(truth, response, positive = "a")#> [1] 0.5