Using Error Diagnosis to Set the Next Priority
Question: How does identifying the main type of error in a model help a team decide what to improve next?
Sample answer: It tells the team whether the next effort should target bias, variance, or a mismatch between the training data and the data the model will face in practice.
Key points:
- It narrows the problem to the most important error source.
- It helps the team choose the most appropriate fix instead of using a random improvement.
Rubric: The answer should explain that recognizing the dominant error source helps decide whether to focus on reducing bias, reducing variance, or addressing data mismatch.
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