Measure to compare true observed labels with predicted labels in binary classification tasks.
fomr(truth, response, positive, na_value = NaN, ...)
Performance value as numeric(1)
.
(factor()
)
True (observed) labels.
Must have the exactly same two levels and the same length as response
.
(factor()
)
Predicted response labels.
Must have the exactly same two levels and the same length as truth
.
(character(1))
Name of the positive class.
(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.
Type: "binary"
Range:
Minimize: TRUE
Required prediction: response
The False Omission Rate is defined as
This measure is undefined if FN + TN = 0.
https://en.wikipedia.org/wiki/Template:DiagnosticTesting_Diagram
Other Binary Classification Measures:
auc()
,
bbrier()
,
dor()
,
fbeta()
,
fdr()
,
fn()
,
fnr()
,
fp()
,
fpr()
,
gmean()
,
gpr()
,
npv()
,
ppv()
,
prauc()
,
tn()
,
tnr()
,
tp()
,
tpr()
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)
fomr(truth, response, positive = "a")
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