mlr3measures (version 0.3.0)

prauc: Area Under the Precision-Recall Curve

Description

Computes the area under the Precision-Recall curve (PRC). The PRC can be interpreted as the relationship between precision and recall (sensitivity), and is considered to be a more appropriate measure for unbalanced datasets than the ROC curve. The PRC is computed by integration of the piecewise function.

Usage

prauc(truth, prob, positive, na_value = NaN, ...)

Arguments

truth

:: factor() True (observed) labels. Must have the exactly same two levels and the same length as response.

prob

:: numeric() Predicted probability for positive class. Must have exactly 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).

Meta Information

• Type: "binary"

• Range: \([0, 1]\)

• Minimize: FALSE

• Required prediction: prob

References

Davis J, Goadrich M (2006). “The relationship between precision-recall and ROC curves.” In Proceedings of the 23rd International Conference on Machine Learning. ISBN 9781595933836.

See Also

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

Examples

# NOT RUN {
truth = factor(c("a", "a", "a", "b"))
prob = c(.6, .7, .1, .4)
prauc(truth, prob, "a")
# }