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Using Bias and Variance to Choose the Next Improvement
Question: A team is building a classifier for defective parts in a factory. What are the two major sources of error in supervised learning, and how does knowing them help the team decide whether collecting more labeled examples is a worthwhile next step?
Sample answer: The two major sources of error are bias and variance. Bias is the error that comes from a model being too simple or making overly restrictive assumptions, while variance is the error that comes from a model changing too much with different training sets. Recognizing these two sources helps the team choose the most effective improvement strategy. If variance is the main issue, collecting more labeled data may be a good use of time. If bias is the larger problem, then changing the model or features may be more helpful than adding more examples.
Key points:
- Name bias and variance as the two major sources of error.
- Distinguish between a model that is too simple and one that is too sensitive to the training set.
- Explain that the distinction guides the next development step.
- Mention that adding more data is useful in some cases, but not always the best fix.
Rubric: Answers must identify both bias and variance and explain that understanding them helps decide whether collecting more data is the best use of development time.
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