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Attributing errors in a cat detector and classifier pipeline with poor bounding boxes.
Case context: You are performing error analysis on a pipeline containing a cat detector and a cat breed classifier. In one instance, the cat detector outputs a poorly cropped bounding box of a Siamese cat. The cat breed classifier receives this poor crop and incorrectly outputs y=0 (indicating no cat is present). However, a highly skilled human looking at the same poorly cropped image can still easily recognize the Siamese cat.
Question: Based on this scenario, explain how you should attribute this error, and describe the condition under which your attribution choice will have a negligible effect on the overall error analysis results.
Sample answer: This error is ambiguous because the cat detector did its job poorly, yet a highly skilled human could still recognize the cat from the poor output, meaning the breed classifier could also be blamed. You can attribute the error to the cat detector, the cat breed classifier, or both. This specific attribution decision will have a negligible effect on the overall error analysis results as long as the total number of such ambiguous cases is small, as any decision will lead to a similar final result.
Key points:
- The cat detector performed poorly, but the breed classifier failed on an image a skilled human could still classify.
- The error can be attributed to the cat detector, the cat breed classifier, or both.
- The specific choice of attribution has a negligible effect if the number of these ambiguous cases is small.
Rubric: The response must: 1) Identify that the error attribution is ambiguous between the cat detector and the cat breed classifier, 2) Mention that the error can be attributed to the detector, classifier, or both, and 3) State that this choice has minimal impact if the total number of such ambiguous cases is small.
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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)
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Related
Perfect Input Test for Pipeline Error Attribution
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When ambiguous pipeline attribution cases are rare, the exact attribution decision (detector vs. classifier) has little impact on the overall error analysis result.
If the number of ambiguous pipeline attribution cases is _____, the exact attribution choice will not significantly change the error analysis outcome.
When does error attribution in a multi-component ML pipeline become ambiguous?
A highly skilled human could arguably still recognize a cat from a poorly cropped image produced by a weak cat detector.
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Match each pipeline error attribution concept to its correct description.
Order the reasoning steps for deciding whether pipeline error attribution ambiguity matters in practice.
In the cat pipeline example, y=0 is output after a poor bounding box crop. According to ML Yearning, this error should be attributed to whom?
If ambiguous pipeline error cases are rare, the specific attribution decision made will significantly change the overall error analysis result.
ML Yearning states that if the number of ambiguous pipeline error cases is _____, you can make whatever attribution decision you want and still get a similar result.
Match each element from the ML Yearning cat pipeline example to its role in the error attribution analysis.
Order the sequence of events in the ML Yearning cat pipeline error example, from input to attribution question.
Explain how ambiguity arises in pipeline error attribution and its significance in error analysis.
Attributing errors in a cat detector and classifier pipeline with poor bounding boxes.
Impact of attribution decisions on rare ambiguous pipeline errors.