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.
prauc(truth, prob, positive, na_value = NaN, ...)
truth | ( |
---|---|
prob | ( |
positive | ( |
na_value | ( |
... | ( |
Performance value as numeric(1)
.
This measure is undefined if the true values are either all positive or all negative.
Type: "binary"
Range: \([0, 1]\)
Minimize: FALSE
Required prediction: prob
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.
Other Binary Classification Measures:
auc()
,
bbrier()
,
dor()
,
fbeta()
,
fdr()
,
fnr()
,
fn()
,
fomr()
,
fpr()
,
fp()
,
mcc()
,
npv()
,
ppv()
,
tnr()
,
tn()
,
tpr()
,
tp()
#> [1] 0.904106