specificity: Compute the specificity curve.

In AUC: Threshold Independent Performance Measures for Probabilistic Classifiers

Compute the specificity curve.

Description

This function computes the specificity curve required for the auc function and the plot function.

Usage

specificity(predictions, labels, perc.rank = TRUE)

Arguments

predictions	A numeric vector of classification probabilities (confidences, scores) of the positive event.
labels	A factor of observed class labels (responses) with the only allowed values {0,1}.
perc.rank	A logical. If TRUE (default) the percentile rank of the predictions is used.

Value

A list containing the following elements:

cutoffs	A numeric vector of threshold values
measure	A numeric vector of specificity values corresponding to the threshold values

Author(s)

Authors: Michel Ballings and Dirk Van den Poel, Maintainer: [e-mail]

References

Ballings, M., Van den Poel, D., Threshold Independent Performance Measures for Probabilistic Classifcation Algorithms, Forthcoming.

See Also

sensitivity, specificity, accuracy, roc, auc, plot

Examples

data(churn)

specificity(churn$predictions,churn$labels)

AUC documentation built on April 5, 2022, 1:15 a.m.