Summary

In this chapter, we briefly explored an overview of different model evaluation methods and how they can be used to measure the performance of a deep learning model. We started with the topic of metric engineering among all the introduced methods. We introduced common base model evaluation metrics. On top of this, we discussed the limitations of using base model evaluation metrics and introduced the concept of engineering a model evaluation metric tailored to the specific problem at hand. We also explored the idea of optimizing directly against the evaluation metric by using it as a loss function. While this approach can be beneficial, it is important to consider the potential pitfalls and limitations, as well as the specific use case for which this approach may be appropriate.

The evaluation of deep learning models requires careful consideration of appropriate evaluation methods, metrics, and statistical tests. Hopefully, after reading through this chapter, I have helped...