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Diagnose a methodology error in a team's use of a split dev set.
Case context: An engineering team splits their dev set into an Eyeball dev set and a Blackbox dev set. After running an automated evaluation, a developer notices that the model performs poorly on the Blackbox dev set. To understand the errors, the developer opens the Blackbox dev set files and manually inspects the misclassified examples to diagnose the bugs.
Question: Identify the methodology error in the developer's actions, explain why this action contradicts the purpose of the Blackbox dev set, and describe what the developer should have done instead to diagnose the model's issues.
Sample answer: The methodology error is that the developer manually inspected (looked at) the examples in the Blackbox dev set. This violates its purpose because the Blackbox dev set is strictly a hands-off subset intended only for automated evaluations (measuring error rates, selecting algorithms, and tuning hyperparameters) without human inspection. By looking at the data, the developer compromises the 'blackbox' nature of the set. To diagnose the bugs, the developer should have manually inspected the misclassified examples in the Eyeball dev set instead.
Key points:
- Manually inspecting the Blackbox dev set is a methodology error.
- The Blackbox dev set must remain off-limits to manual visual inspection to preserve its integrity for automated evaluations.
- Manual error analysis and debugging must be performed using the Eyeball dev set instead.
Rubric: The answer must identify that looking at the Blackbox dev set is the error. It must explain that this violates its purpose as a hands-off, blind evaluation subset. It must recommend using the Eyeball dev set for manual error inspection instead.
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References
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
Machine Learning Yearning (Deeplearning.ai)
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Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
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