# anomalyThreshold

Optimal anomaly threshold for set of anomaly scores and corresponding labels

*Since R2022b*

## Syntax

## Description

calculates the optimal anomaly threshold given per-image anomaly scores and corresponding
ground truth labels. `t`

= anomalyThreshold(`gtLabels`

,`scores`

,`anomalyLabels`

)`anomalyLabels`

indicates which class labels in
`gtLabels`

belong to the anomaly (positive) class. When performing
anomaly detection, images with scores below the calculated threshold are considered normal
images and images with scores above the threshold are considered anomalous images.

**Note**

This functionality requires Deep Learning Toolbox™ and the Computer Vision Toolbox™ Automated Visual Inspection Library. You can install the Computer Vision Toolbox Automated Visual Inspection Library from Add-On Explorer. For more information about installing add-ons, see Get and Manage Add-Ons.

also specifies the optimization method.`t`

= anomalyThreshold(`gtLabels`

,`scores`

,`anomalyLabels`

,`optimMethod`

)

also specifies the maximum false positive rate.`t`

= anomalyThreshold(`gtLabels`

,`scores`

,`anomalyLabels`

,MaxFalsePositiveRate=`maxFPR`

)

also specifies the maximum false negative rate.`t`

= anomalyThreshold(`gtLabels`

,`scores`

,`anomalyLabels`

,MaxFalseNegativeRate=`maxFNR`

)

`[`

also returns the receiver operating characteristic (ROC) curve and performance
metrics.`t`

,`anomalyROC`

] = anomalyThreshold(___)

## Examples

## Input Arguments

## Output Arguments

## Tips

You can plot ROC and PR curves returned by

`anomalyROC`

using the`plot`

(Deep Learning Toolbox) function.

## Version History

**Introduced in R2022b**

## See Also

`fcddAnomalyDetector`

| `rocmetrics`

(Deep Learning Toolbox)

### Topics

- Getting Started with Anomaly Detection Using Deep Learning
- ROC Curve and Performance Metrics (Deep Learning Toolbox)