tn: True Negatives

In mlr3measures: Performance Measures for 'mlr3'

True Negatives

Description

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

Usage

tn(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.

Details

This measure counts the true negatives, i.e. the number of predictions correctly indicating a negative class label. This is sometimes also called a "correct rejection".

Value

Performance value as numeric(1).

Meta Information

• Type: "binary"

• Range: [0, \infty)

• Minimize: FALSE

• Required prediction: response

References

https://en.wikipedia.org/wiki/Template:DiagnosticTesting_Diagram

See Also

Other Binary Classification Measures: auc(), bbrier(), dor(), fbeta(), fdr(), fn(), fnr(), fomr(), fp(), fpr(), gmean(), gpr(), npv(), ppv(), prauc(), 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)
tn(truth, response, positive = "a")

mlr3measures documentation built on Sept. 12, 2024, 7:20 a.m.