mlr3measures (version 0.3.1)

fn: False Negatives

Description

Classification measure counting the false negatives (type 2 error), i.e. the number of predictions indicating a negative class label while in fact it is positive. This is sometimes also called a "false alarm".

Usage

fn(truth, response, positive, ...)

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.

...

(any) Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

Meta Information

• Type: "binary"

• Range: \([0, \infty)\)

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