Evaluating a regression model

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 linear regression sample application, there are five properties that comprise the RegressionMetrics class object. These include the following:

• Loss function
• Mean absolute error
• Mean squared error
• R-squared
• Root mean squared error

In the next sections, we will break down how these values are calculated and ideal values to look for.