As discussed in previous chapters, evaluating a model is a critical part of the overall model-building process. A poorly trained model will only provide inaccurate predictions. Fortunately, ML.NET provides many popular attributes to calculate model accuracy based on a test set at the time of training to give you an idea of how well your model will perform in a production environment.
In ML.NET, as noted in the example application, there are two properties that comprise the AnomalyDetectionMetrics class object. Let's dive into the properties exposed in the AnomalyDetectionMetrics object:
- Area under the ROC curve
- Detection rate at false positive count
In the next sections, we will break down how these values are calculated and ideal values to look for.
Area under the ROC curve
The area under the ROC curve, as mentioned in Chapter 3, Regression Model, is, as the name implies, the area under the Receiver Operating Characteristic (ROC) curve. One question...
