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True Negatives

Source: R/binary_tn.R
tn.Rd

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

Usage

tn(truth, response, positive, sample_weights = NULL, ...)

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.

sample_weights

(numeric())
Vector of non-negative and finite sample weights. Must have the same length as truth. The vector gets automatically normalized to sum to one. Defaults to equal sample weights.

...

(any)
Additional arguments. Currently ignored.

Value

Performance value as numeric(1).

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".

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")
#> [1] 1