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False Discovery Rate

Source: R/binary_fdr.R
fdr.Rd

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

Usage

fdr(truth, response, positive, sample_weights = NULL, 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.

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.

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).

Details

The False Discovery Rate is defined as $$ \frac{\mathrm{FP}}{\mathrm{TP} + \mathrm{FP}}. $$

This measure is undefined if TP + FP = 0.

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(), fn(), 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)
fdr(truth, response, positive = "a")
#> [1] 0.5