roc: Compute the receiver operating characteristic (ROC) curve.

In AUC: Threshold Independent Performance Measures for Probabilistic Classifiers

Compute the receiver operating characteristic (ROC) curve.

Description

This function computes the receiver operating characteristic (ROC) curve required for the auc function and the plot function.

Usage

roc(predictions, labels)

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}.

Value

A list containing the following elements:

cutoffs	A numeric vector of threshold values
fpr	A numeric vector of false positive rates corresponding to the threshold values
tpr	A numeric vector of true positive rates 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)

roc(churn$predictions,churn$labels)

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