AUC: roc – R documentation

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roc

Compute the receiver operating characteristic (ROC) curve.

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

roc(predictions, labels)

`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}.

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

Authors: Michel Ballings and Dirk Van den Poel, Maintainer: Michel.Ballings@UGent.be

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

data(churn)

roc(churn$predictions,churn$labels)

Threshold independent performance measures for probabilistic classifiers.

v0.3.0

GPL (>= 2)

Authors

Michel Ballings and Dirk Van den Poel

Initial release

2013-09-30