Specificity is defined as the proportion of actual negatives, which got predicted as the negative (or true negative). This implies that there will be another proportion of actual negative, which got predicted as positive and could be termed as false positives.
D2MCS::MeasureFunction -> Specificity
new()Method for initializing the object arguments during runtime.
Specificity$new(performance.output = NULL)performance.outputAn optional ConfMatrix parameter
to define the type of object used as basis to compute the measure.
compute()The function computes the Specificity achieved by the M.L. model.
Specificity$compute(performance.output = NULL)performance.outputAn optional ConfMatrix parameter
to define the type of object used as basis to compute the
Specificity measure.
This function is automatically invoke by the
ClassificationOutput object.
clone()The objects of this class are cloneable with this method.
Specificity$clone(deep = FALSE)deepWhether to make a deep clone.
$$Specificity = True Negative / (True Negative + False Positive)$$
MeasureFunction, ClassificationOutput,
ConfMatrix