Specificity | R Documentation |

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.

*Specificity = True Negative / (True Negative + False Positive)*

`D2MCS::MeasureFunction`

-> `Specificity`

`new()`

Method for initializing the object arguments during runtime.

Specificity$new(performance.output = NULL)

`performance.output`

An 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.output`

An 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.

A numeric vector of size 1 or NULL if an error occurred.

`clone()`

The objects of this class are cloneable with this method.

Specificity$clone(deep = FALSE)

`deep`

Whether to make a deep clone.

`MeasureFunction`

, `ClassificationOutput`

,
`ConfMatrix`

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