auc {mlr3measures} | R Documentation |
Area Under the ROC Curve
Description
Measure to compare true observed labels with predicted probabilities in binary classification tasks.
Usage
auc(truth, prob, positive, na_value = NaN, ...)
Arguments
truth |
( |
prob |
( |
positive |
( |
na_value |
( |
... |
( |
Details
Computes the area under the Receiver Operator Characteristic (ROC) curve. The AUC can be interpreted as the probability that a randomly chosen positive observation has a higher predicted probability than a randomly chosen negative observation.
This measure is undefined if the true values are either all positive or all negative.
Value
Performance value as numeric(1)
.
Meta Information
Type:
"binary"
Range:
[0, 1]
Minimize:
FALSE
Required prediction:
prob
References
Youden WJ (1950). “Index for rating diagnostic tests.” Cancer, 3(1), 32–35. doi:10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3.
See Also
Other Binary Classification Measures:
bbrier()
,
dor()
,
fbeta()
,
fdr()
,
fn()
,
fnr()
,
fomr()
,
fp()
,
fpr()
,
gmean()
,
gpr()
,
npv()
,
ppv()
,
prauc()
,
tn()
,
tnr()
,
tp()
,
tpr()
Examples
truth = factor(c("a", "a", "a", "b"))
prob = c(.6, .7, .1, .4)
auc(truth, prob, "a")