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

## Usage

```
tnr(truth, response, positive, na_value = NaN, ...)
specificity(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.

## Details

The True Negative Rate is defined as $$ \frac{\mathrm{TN}}{\mathrm{FP} + \mathrm{TN}}. $$ Also know as "specificity".

This measure is undefined if FP + TN = 0.