Why should the desired performance level be shown on the learning curve?
Question: Answer in one to three sentences: Why is the desired performance level added before visually extrapolating the dev-error curve?
Sample answer: It provides a target against which the extrapolated dev-error curve can be compared. This helps estimate whether adding more data could bring performance close enough to the desired level.
Key points:
- Provides a visible target.
- Enables comparison with extrapolated dev error.
- Supports estimating whether more data could reach the desired level.
Rubric: The answer should identify the desired level as a comparison target and connect it to estimating the benefit of additional data.
0
1
Tags
Machine Learning
Deep Learning
Supervised Learning
Dive into Deep Learning @ D2L
Data Science
Machine Learning Strategy
Machine Learning Yearning @ DeepLearning.AI
Related
What does visually extrapolating the dev-error curve help estimate?
Visual extrapolation gives a guaranteed prediction of performance after adding data.
Add the desired _____ level to the learning curve before extrapolating dev error.
Match each learning-curve element with its role in estimating the value of more data.
Order the reasoning process for estimating whether more data could reach the desired performance.
Explain how a dev-error learning curve can support a decision to collect more data.
A projected dev-error curve reaches the target after doubling the data. What should the team conclude?
Why should the desired performance level be shown on the learning curve?
Which conclusion best reflects the passage's example learning curve?
A dev-error extrapolation may justify saying that a data increase looks plausible, not certain.