Summary
In this chapter, we learned about different performance and error metrics for supervised and unsupervised learning models. We discussed the limitations of each metric and the right way of interpreting them. We also reviewed bias and variance analysis and different validation and cross-validation techniques for assessing the generalizability of models. We also presented error analysis as an approach for detecting the components of a model that contribute to model overfitting. We went through Python code examples for these topics to help you practice with them and be able to quickly use them in your projects.
In the next chapter, we will review techniques to improve the generalizability of machine learning models, such as synthetic data addition to training data, removing data inconsistencies, and regularization methods.