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.
As noted earlier in the sample application, for matrix factorization model evaluation in ML.NET, there are five properties that comprise the RegressionMetrics class object. Let us dive into the properties exposed in the RegressionMetrics object here:
- Loss function
- Mean Squared Error (MSE)
- Mean Absolute Error (MAE)
- R-squared
- Root Mean Squared Error (RMSE)
In the next sections, we will break down how these values are calculated, and detail the ideal values to look for.
Loss function
This property uses the loss function set when the matrix factorization trainer was...
