Analyzing Mismatch Hypotheses via Training and Training Dev Subsets
Question: If both your training and training dev sets contain a specific subset of data representing a mismatch hypothesis (such as car audio), explain how comparing performance on these subsets helps validate the decision to acquire more of that specific subset data.
Sample answer: By double-checking performance on the car audio subset in both sets, we can look for a performance gap. If the model performs well on the car audio in the training set but performs poorly on the car audio in the training dev set, this discrepancy validates the hypothesis that the model needs more car-recorded data to generalize properly. Thus, it justifies the effort to collect more car audio data.
Key points:
- Identify and isolate the subset of interest (e.g., car audio) in both training and training dev sets.
- Compare performance on this subset across both sets to check for a generalization gap.
- Validate that more subset data is needed if the system performs well on the training subset but poorly on the training dev subset.
Rubric: The response must describe comparing subset performance on both the training and training dev sets, and explain that a high training subset performance coupled with low training dev subset performance validates the hypothesis that acquiring more of that data will help.
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What does it indicate when a model performs well on car audio in the training set but poorly on car audio in the training dev set?
If both your training set and training dev set contain car audio, you should evaluate your system's performance specifically on that car-audio subset.
If a model does well on car audio in the training set but poorly on car audio in the training dev set, this validates the hypothesis that getting more _____ data would help.
When training and training dev sets both include car-recorded audio, what action should you take to investigate the data mismatch hypothesis?
If a model performs well on car audio in the training set but poorly on car audio in the training dev set, this further validates the hypothesis that getting more car data would help.
If the system does well on car data in the training set but not on car data in the _____, this further validates the mismatch hypothesis.
Match each observation about car-audio subset performance to its implication for the data mismatch hypothesis.
Order the diagnostic steps for using a shared subset (e.g., car audio) to check the data mismatch hypothesis.
What conclusion should you draw if your model achieves high accuracy on car audio in the training set but low accuracy on car audio in the training dev set?
Checking performance on a shared subset in the training and training dev sets can only refute—never further validate—a data mismatch hypothesis.
Ng recommends double-checking the system's performance on the car-audio _____ when both the training and training dev sets include car-recorded audio.
Match each key term in the mismatch hypothesis checking procedure to its correct description.
Order the reasoning steps for deciding whether to collect more car data, starting from suspecting a mismatch to reaching a validated conclusion.
Analyzing Mismatch Hypotheses via Training and Training Dev Subsets
Diagnosing Speech Recognition Performance in Car Audio Subsets
Hypothesis Validation from Training to Training Dev Subset Performance