mlr3measures (version 0.3.0)

fpr: False Positive Rate

Description

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

Usage

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).

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(), prauc(), tnr(), tn(), tpr(), tp()

Examples

# NOT RUN {
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")
# }