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False Negatives

Source: R/binary_fn.R
fn.Rd

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

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).

Details

This measure counts 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 "miss" or an "underestimation".

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(), fp(), fpr(), gmean(), gpr(), npv(), ppv(), prauc(), tn(), tnr(), tp(), tpr()

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)
fn(truth, response, positive = "a")
#> [1] 3