True Positive Rate

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

Usage

tpr(truth, response, positive, na_value = NaN, ...)

recall(truth, response, positive, na_value = NaN, ...)

sensitivity(truth, response, positive, 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.

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 True Positive Rate is defined as $$ \frac{\mathrm{TP}}{\mathrm{TP} + \mathrm{FN}}. $$ This is also know as "recall", "sensitivity", or "probability of detection".

This measure is undefined if TP + FN = 0.

Meta Information

• Type: "binary"

• Range: \([0, 1]\)

• Minimize: FALSE

• Required prediction: response

References

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

Goutte C, Gaussier E (2005). “A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation.” In Lecture Notes in Computer Science, 345–359. doi:10.1007/978-3-540-31865-1_25 .

See also

Other Binary Classification Measures: auc(), bbrier(), dor(), fbeta(), fdr(), fn(), fnr(), fomr(), fp(), fpr(), gmean(), gpr(), npv(), ppv(), prauc(), tn(), tnr(), tp()

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