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

## Usage

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.

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.

## Value

Performance value as numeric(1).

## 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.

## Meta Information

• Type: "binary"

• Range: $$[0, 1]$$

• Minimize: FALSE

• Required prediction: response

## 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 .

Other Binary Classification Measures: auc(), bbrier(), dor(), fdr(), fnr(), fn(), fomr(), fpr(), fp(), mcc(), npv(), ppv(), prauc(), tnr(), tn(), tpr(), tp()
set.seed(1)