mlr3measures (version 0.3.1)

fomr: False Omission Rate

Description

Binary classification measure defined as $$ \frac{\mathrm{FN}}{\mathrm{FN} + \mathrm{TN}}. $$

Usage

fomr(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(), fpr(), 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)
fomr(truth, response, positive = "a")
# }

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