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 earlier in the sample applications, there are several properties that comprise the CalibratedBinaryClassificationMetrics class object. In Chapter 2, Setting Up the ML.NET Environment, we reviewed some of these properties. However, now that we have a more complex example and have learned how to evaluate regression models, let us dive into the following properties:
- Accuracy
- Area Under ROC Curve
- F1 Score
- Area Under Precision-Recall Curve
In addition, we will also look at the following four metrics returned by the MulticlassClassificationMetrics object...
