What training data strategy helps resolve data mismatch?
Question: When a model suffers from data mismatch and struggles with specific types of examples in the dev set, what is one recommended option regarding the training data to improve performance?
Sample answer: One recommended option is to find and acquire more training data that better matches the specific dev-set examples the algorithm has trouble with.
Key points:
- Acquire more training data.
- Ensure the new data matches the specific dev-set examples the algorithm struggles with.
Rubric: The answer must state that the practitioner should acquire more training data that resembles the difficult dev-set examples.
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Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
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When a model performs well on training data but poorly on the dev set due to distribution differences, what is one recommended strategy?
A data mismatch problem exists when a model performs well on its training set but poorly on a dev set drawn from a different distribution.
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Match each concept related to data mismatch to its correct description from ML Yearning.
Order the steps for diagnosing and addressing a data mismatch problem in a speech recognition system.
In ML Yearning's speech recognition example, what is the primary cause of the model's poor performance on the dev set?
According to ML Yearning, acquiring training data that matches the dev set distribution is guaranteed to resolve a data mismatch problem.
To address a data mismatch problem, one recommended option is to find more training data that better _____ the dev-set examples the algorithm has trouble with.
Match each component of the speech recognition scenario to its role in ML Yearning's data mismatch framework.
Order the reasoning steps a practitioner should follow when deciding to seek targeted training data to address a data mismatch.
How does acquiring targeted training data address a data mismatch problem?
Diagnose and resolve the speech recognition mismatch
What training data strategy helps resolve data mismatch?