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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 All Questions

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Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 81 discussion

Exam question from Amazon's AWS Certified Machine Learning Engineer - Associate MLA-C01
Question #: 81
Topic #: 1
[All AWS Certified Machine Learning Engineer - Associate MLA-C01 Questions]

A company has a binary classification model in production. An ML engineer needs to develop a new version of the model.
The new model version must maximize correct predictions of positive labels and negative labels. The ML engineer must use a metric to recalibrate the model to meet these requirements.
Which metric should the ML engineer use for the model recalibration?

  • A. Accuracy
  • B. Precision
  • C. Recall
  • D. Specificity
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Suggested Answer: A 🗳️
by GiorgioGss at Nov. 28, 2024, 5:42 p.m.
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kyo
2 weeks, 4 days ago
Selected Answer: A
For imbalanced data, F1 score is preferred over confusion matrix-derived metrics. Ideally, option A would be "F1 Score". Given the choices, A (Accuracy) is the most reasonable, though not ideal.
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Saransundar
2 months ago
Selected Answer: A
A. Accuracy: Correct choice; maximizes both true positives and true negatives. Formula: (TP + TN) / Total Predictions B. Precision: Focuses only on true positives, not negatives. Formula: TP / (TP + FP) C. Recall: Focuses on capturing all true positives, ignoring negatives. Formula: TP / (TP + FN) D. Specificity: Focuses only on true negatives, ignoring positives. Formula: TN / (TN + FP)
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GiorgioGss
2 months, 1 week ago
Selected Answer: A
Accuracy formula: (True Positives + True Negatives) / Total Predictions
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GiorgioGss
2 months, 1 week ago
Selected Answer: A
https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-accuracy-evaluation.html
upvoted 1 times
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