Description Details Author(s) References See Also Examples

Summary and plotting functions for threshold independent performance measures for probabilistic classifiers.

This package includes functions to compute the area under the curve (function `auc`

) of selected measures: The area under
the sensitivity curve (AUSEC) (function `sensitivity`

), the area under the specificity curve
(AUSPC) (function `specificity`

), the area under the accuracy curve (AUACC) (function `accuracy`

), and
the area under the receiver operating characteristic curve (AUROC) (function `roc`

). The curves can also be
visualized using the function `plot`

. Support for partial areas is provided.

Auxiliary code in this package is adapted from the `ROCR`

package. The measures available in this package are not available in the
ROCR package or vice versa (except for the AUROC). As for the AUROC, we adapted the `ROCR`

code to increase computational speed
(so it can be used more effectively in objective functions). As a result less funtionality is offered (e.g., averaging cross validation runs).
Please use the `ROCR`

package for that purposes.

Michel Ballings and Dirk Van den Poel, Maintainer: [email protected]

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

`sensitivity`

, `specificity`

, `accuracy`

, `roc`

, `auc`

, `plot`

1 2 3 4 5 6 7 8 9 10 11 | ```
data(churn)
auc(sensitivity(churn$predictions,churn$labels))
auc(specificity(churn$predictions,churn$labels))
auc(accuracy(churn$predictions,churn$labels))
auc(roc(churn$predictions,churn$labels))
plot(sensitivity(churn$predictions,churn$labels))
plot(specificity(churn$predictions,churn$labels))
plot(accuracy(churn$predictions,churn$labels))
plot(roc(churn$predictions,churn$labels))
``` |

AUC documentation built on May 29, 2017, 2:14 p.m.

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