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Under what conditions is artificial data synthesis a viable approach to align training data with the development set?
Question: Based on Andrew Ng's Machine Learning Yearning, explain the circumstances under which artificial data synthesis is used, detailing what characteristics the synthesized training set must achieve relative to the dev set.
Sample answer: Artificial data synthesis is a viable approach under circumstances where it allows a practitioner to create a huge dataset that reasonably matches the dev set. The synthesized data helps bridge the gap between the training set and the dev set distributions by simulating real-world dev conditions at scale.
Key points:
- Useful in specific circumstances of training/dev set mismatch
- Enables the creation of a huge dataset
- The synthesized dataset must reasonably match the dev set
Rubric: The answer must specify that synthesis is useful under circumstances where it enables the creation of a huge dataset, and that this synthesized dataset must reasonably match the dev set.
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