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Exam AWS Certified Machine Learning - Specialty topic 1 question 183 discussion

Exam question from Amazon's AWS Certified Machine Learning - Specialty
Question #: 183
Topic #: 1
[All AWS Certified Machine Learning - Specialty Questions]

A data scientist is evaluating a GluonTS on Amazon SageMaker DeepAR model. The evaluation metrics on the test set indicate that the coverage score is 0.489 and 0.889 at the 0.5 and 0.9 quantiles, respectively.
What can the data scientist reasonably conclude about the distributional forecast related to the test set?

  • A. The coverage scores indicate that the distributional forecast is poorly calibrated. These scores should be approximately equal to each other at all quantiles.
  • B. The coverage scores indicate that the distributional forecast is poorly calibrated. These scores should peak at the median and be lower at the tails.
  • C. The coverage scores indicate that the distributional forecast is correctly calibrated. These scores should always fall below the quantile itself.
  • D. The coverage scores indicate that the distributional forecast is correctly calibrated. These scores should be approximately equal to the quantile itself.
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Suggested Answer: D 🗳️
by cognito_22 at May 7, 2022, 8:16 p.m.
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cognito_22
Highly Voted 1 year, 12 months ago
Selected Answer: D
https://ts.gluon.ai/tutorials/forecasting/quick_start_tutorial.html
upvoted 10 times
example_
1 year, 7 months ago
https://ts.gluon.ai/stable/tutorials/forecasting/quick_start_tutorial.html
upvoted 4 times
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backbencher2022
Most Recent 6 months, 2 weeks ago
Selected Answer: C
C is correct based on this blog - https://aws.amazon.com/blogs/machine-learning/training-debugging-and-running-time-series-forecasting-models-with-the-gluonts-toolkit-on-amazon-sagemaker/
upvoted 1 times
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Mickey321
8 months, 1 week ago
Selected Answer: D
D for me
upvoted 1 times
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vbal
9 months, 2 weeks ago
D: A well-calibrated model should have quantile coverage close to the desired coverage level (e.g., 90% quantile coverage should be close to 90%). If the quantile coverage is consistently off from the desired level, it may indicate the need to recalibrate the model or investigate the sources of uncertainty estimation errors.
upvoted 1 times
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CKS1210
10 months, 1 week ago
Selected Answer: C
https://apps.microsoft.com/store/detail/move-mouse/9NQ4QL59XLBF?hl=en-us&gl=us
upvoted 1 times
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WilianCB
1 year ago
Selected Answer: D
I think it is D
upvoted 1 times
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blanco750
1 year, 1 month ago
Selected Answer: C
Thanks to ChatGPT Given the coverage score results, the data scientist can conclude that the distributional forecast related the test set is well calibrated. Specifically, when the model predicts quantiles, around % of the true values should fall within the 0.5 quantile range, and around90% of the true values should fall within the 0.9 quantile range., the GluonTS on Amazon SageMaker DeepAR model performance on the test set was concerning the coverage of the predicted quantiles.
upvoted 1 times
wendaz
6 months, 2 weeks ago
My chatgpt is latest: The coverage of a distributional forecast at a given quantile is the fraction of observations that fall below the predicted quantile. In a well-calibrated forecast, the coverage score should be approximately equal to the quantile itself. Given the information: Coverage score is 0.489 at the 0.5 quantile. Coverage score is 0.889 at the 0.9 quantile. For a well-calibrated forecast: At the 0.5 quantile (or median), the coverage should be approximately 0.5. At the 0.9 quantile, the coverage should be approximately 0.9. The provided coverage scores closely match the quantiles, with slight deviations. Therefore, the correct conclusion is: Option D: The coverage scores indicate that the distributional forecast is correctly calibrated. These scores should be approximately equal to the quantile itself.
upvoted 1 times
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SANDEEP_AWS
1 year, 1 month ago
Selected Answer: C
Scores should always fall below the quantile itself. Ref: https://d1.awsstatic.com/asset-repository/Amazon%20Forecast%20Technical%20Guide%20to%20Time-Series%20Forecasting%20Principles.pdf -- Pg 18
upvoted 2 times
SANDEEP_AWS
1 year, 1 month ago
PDF Pg 23
upvoted 2 times
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Tony_1406
1 year ago
https://docs.aws.amazon.com/forecast/latest/dg/metrics.html#metrics-wQL A more concise doc.
upvoted 1 times
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[Removed]
1 year, 9 months ago
C is correct
upvoted 2 times
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