)` and its prediction `\(\left( \hat{\color{#FFA500}{\mathbf{y}}} \right)\)`.

The (absolute) distance between a true value and its prediction is often called .b[loss].

The way you choose to use loss to measure model performance is called a **loss function.**

---
name: accuracy-subtlety

## What loss function should we choose?

How do we compare loss across observations? Many questions:

- Which do you prefer?
  1. Lots of little errors and a few really large errors.
  1. Medium-sized errors for everyone.