Model evaluation

Once the model is trained, the last step is to evaluate the model. The typical approach to model evaluation is to hold out a portion of your dataset for evaluation. The idea behind this is to take known data, submit it to your trained model, and measure the efficacy of your model. The critical part of this step is to hold out a representative dataset of your data. If your holdout set is swayed one way or the other, then you will more than likely get a false sense of either high performance or low performance. In the next chapter, we will deep dive into the various scoring and evaluation metrics. ML.NET provides a relatively easy interface to evaluate a model; however, each algorithm has unique properties to verify, which we will review as we deep dive into the various algorithms.