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What should you do if perfecting component A's output does not fix the jarring steering scenario?
Question: In the self-driving car error attribution example, what does it mean, and what should you do next, if manually perfecting component A's (detect cars) output does not improve the path planning output?
Sample answer: It means component A is not the source of the error in this scenario, since even with a perfect A output the path is still jarring. The engineer should then move on to Step 2 of the procedure and manually perfect component B's (detect pedestrians) output to test whether B is responsible.
Key points:
- No improvement rules out component A as the cause
- The procedure moves on to testing component B next
- This keeps the analysis systematic across pipeline components
Rubric: Full credit states that A is ruled out as the cause and that the next step is to test component B; partial credit for only stating one of these.
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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)
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
Machine Learning Yearning @ DeepLearning.AI
Related
In the self-driving car jarring-steering scenario, which pipeline component is manually perfected first when attributing the error?
True or False: If perfecting component A's output makes the pipeline plan a much better path, the error is attributed to A.
In the self-driving world, a closed-track case where the car steers more jarringly than a skilled driver would is called a _____.
Match each self-driving car pipeline component to its function.
Order the steps for attributing the jarring-steering error to component A in the self-driving car pipeline.
Why does manually perfecting one component's output help isolate which component caused a pipeline error?
Diagnose the source of a jarring steering decision using error attribution by parts.
What should you do if perfecting component A's output does not fix the jarring steering scenario?
If perfecting component A's output does NOT fix the jarring steering scenario, what should the engineer do next?
True or False: Allowing component C to use A's perfected output is a necessary part of testing whether A caused the error.