Specificity: Computes the Specificity Value.

In D2MCS: Data Driving Multiple Classifier System

Computes the Specificity Value.

Description

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.

Details

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

Super class

D2MCS::MeasureFunction -> Specificity

Methods

Public methods

• Specificity$new()

• Specificity$compute()

• Specificity$clone()

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Method new()

Method for initializing the object arguments during runtime.

Usage

Specificity$new(performance.output = NULL)

Arguments

performance.output

An optional ConfMatrix parameter to define the type of object used as basis to compute the measure.

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Method compute()

The function computes the Specificity achieved by the M.L. model.

Usage

Specificity$compute(performance.output = NULL)

Arguments

performance.output

An optional ConfMatrix parameter to define the type of object used as basis to compute the Specificity measure.

Details

This function is automatically invoke by the ClassificationOutput object.

Returns

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

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Method clone()

The objects of this class are cloneable with this method.

Usage

Specificity$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

MeasureFunction, ClassificationOutput, ConfMatrix

D2MCS documentation built on Aug. 23, 2022, 5:07 p.m.