Binary classification measure defined as $$ \frac{\mathrm{FP}}{\mathrm{FP} + \mathrm{TN}}. $$ Also know as fall out or probability of false alarm.

fpr(truth, response, positive, 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.

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).

Note

This measure is undefined if FP + TN = 0.

Meta Information

  • Type: "binary"

  • Range: \([0, 1]\)

  • Minimize: TRUE

  • Required prediction: response

References

https://en.wikipedia.org/wiki/Template:DiagnosticTesting_Diagram

See also

Other Binary Classification Measures: auc(), bbrier(), dor(), fbeta(), fdr(), fnr(), fn(), fomr(), fp(), mcc(), npv(), ppv(), tnr(), tn(), tpr(), tp()

Examples

set.seed(1) lvls = c("a", "b") truth = factor(sample(lvls, 10, replace = TRUE), levels = lvls) response = factor(sample(lvls, 10, replace = TRUE), levels = lvls) fpr(truth, response, positive = "a")
#> [1] 0.75