In this module, we will discuss how one can assess the quality of different models, and what aspects of model quality one needs to consider.

- Learn how to determine different aspects of model quality
- Become familiar with different types of model performance metrics
- Become able to choose the right assessment metric for your problem
- Learn what model uncertainty is and how to compute it
- Become familiar with model diagnostics

If we fit any kind of statistical model to data, we need to determine
the quality of a fit somehow. How do we know one model is
*better* than another? How do we know any given model is
*good*? These questions, i.e., what model is *better*
among a group of models, and if a model is *good* (even the best
among a group of awful models can be a bad model!) have multiple parts
to them, only some of which can be answered by statistics. Others are
judgment calls.

There is no single way to define quality. Instead, multiple factors should be considered. Here are some important aspects to think about:

- Model performance: How well do the predictions from the model compare to the actual data?
- Model complexity: How easy or hard is it to understand the model? How fast does it run? How robust is it to violations of the assumptions you made?
- Model uncertainty: How much uncertainty is associated with the predictions made by the model?
- Model misspecification: How much systematic deviation is there between model predictions and actual data?

We will discuss these different ways one can assess model quality in this module.