Why avoid domain adaptation for specific applications?
Question: According to Machine Learning Yearning, why might a team prioritize choosing dev and test sets from the same distribution over trying to develop domain adaptation algorithms for a specific application?
Sample answer: A team should prioritize same-distribution dev and test sets because it makes them more efficient. Domain adaptation is a complex research problem whose methods are typically applicable only in special cases, making it less suitable for straightforward application progress.
Key points:
- Choosing dev/test sets from the same distribution increases team efficiency.
- Domain adaptation is an important research problem but methods are less widely applicable.
Rubric: The response must mention team efficiency and the specialized or research-oriented nature of domain adaptation.
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Machine Learning
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Supervised Learning
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Data Science
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Machine Learning Yearning @ DeepLearning.AI
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Why avoid domain adaptation for specific applications?