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

https://ts.gluon.ai/stable/tutorials/forecasting/quick_start_tutorial.html

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.

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https://docs.aws.amazon.com/forecast/latest/dg/metrics.html#metrics-wQL A more concise doc.