Question: The source notes it is possible to spend years working on improving either the cat detector or the breed classifier. Explain why performing error analysis by parts first is valuable, and how it informs which component to prioritize.

Sample answer: Error analysis by parts lets you attribute each mistake the algorithm makes to the cat detector, the breed classifier, or both, before committing significant effort to improving either component. Since it is possible to spend years working on improving either part, manually examining misclassified examples reveals which component is actually causing most errors. This evidence-based attribution ensures effort is focused on the component most responsible for the pipeline's mistakes rather than guessing which part to improve.

Key points:

• Two pipeline parts exist: cat detector and breed classifier
• It is possible to spend years improving either part
• Error analysis by parts attributes each mistake to one or both parts
• This attribution guides where to focus improvement effort
• Manual examination of individual misclassified examples is the method used

Rubric: Full credit explains that error analysis by parts attributes mistakes to specific components, mentions the risk of spending years on the wrong component, and connects attribution to prioritizing improvement effort.