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

🔑 Keyword: Maximize correct predictions of both positive and negative labels ✅ Correct Answer: A. Accuracy Why? Accuracy measures the proportion of correctly classified instances (both positive and negative). It is the most appropriate metric when both false positives and false negatives need to be minimized. Why Others Are Wrong? ❌ B. Precision focuses only on correctly predicted positives, not overall correctness. ❌ C. Recall focuses only on capturing all true positives, ignoring true negatives. ❌ D. Specificity only measures the ability to identify true negatives.

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.

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)

Accuracy formula: (True Positives + True Negatives) / Total Predictions

https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-accuracy-evaluation.html