# Receiver Operating Characteristics Graph

The Receiver Operating Characteristic (ROC The Receiver Operating Characteristic (ROC) is a graphical tool that allows you to visualize and analyze the tradeoff between precision and recall exhibited by a particular set of training parameters when applied to a particular training image set.) graph is a graphical tool that allows you to visualize and analyze the tradeoff between precision and recall exhibited by a particular set of training Training is the process that your tool, which is a neural network, is learning about the features (pixels) based on the labels you made. For example, a tool will learn the defect/normal pixels in each image based on the defect/normal labels you drew. The goal of the tool Training is learning enough to give the correct inspection results of whether an unseen image is defective or not. The key to training is to ensure that you include all possible variations within your training set, and that your images are accurately labeled. Training times vary by the application, tool setup and the GPU in the PC being used to train the network. parameters when applied to a particular training image set A collection of images of your specific application. A training image set represents images of a specific part or process acquired in a consistent way using the lighting, optical, and mechanical characteristics of your runtime system. The training image set includes images that represent the range of image appearances that you expect to see in normal operation.. The graph measures the sensitivity over the specificity, given a specific threshold.

- Sensitivity is the ability to detect a positive result (has a defect).
- Specificity is the ability to detect a negative result (no defect).

This is performed by evaluating the results between the two thresholds.

## Area Under the Curve (AUC)

The Area Under the Curve (AUC) metric represents how well a result is being obtained. Generally, with the AUC, a 1 being a perfect result; anything above .8 being a good result (generally speaking); and .5 being completely random.

The AUC is independent from any threshold, and provides a statistical test based on the ranking of scores only, with no assumptions on the score distribution. The AUC is a robust measure of the classifier's separation power.

The AUC is:

- 0.5 for a random classifier
- 1 for perfectly discriminating classifier
- not a percentage
- independent of any threshold