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AIF-C01 Practice Question: A machine learning team is working on a…
A machine learning team is working on a multi-label classification problem. They have a highly imbalanced dataset where some labels appear very infrequently. Which evaluation metric is MOST appropriate for assessing model performance across all labels?
⚠ Common exam trap
AWS often tests the distinction between micro and macro averaging in imbalanced multi-label scenarios, where candidates mistakenly choose micro-averaged F1 because it is commonly used in multi-class problems, failing to recognize that it favors majority labels in multi-label settings.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Macro-averaged F1 score
Macro-averaged F1 score is the most appropriate metric for multi-label classification with highly imbalanced data because it computes the F1 score independently for each label and then averages them, giving equal weight to all labels regardless of their frequency. This ensures that the performance on rare labels is not overshadowed by the performance on frequent labels, which is critical when infrequent labels are equally important.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Precision
Why it's wrong here
Precision alone ignores false negatives, so a model can score highly by predicting only confident, frequent labels and missing rare ones. It is useful when false positives are costly, but assessing performance across all labels requires recall combined with precision.
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Micro-averaged F1 score
Why it's wrong here
Micro-averaging pools every label's true positives, false positives and false negatives, so frequent labels dominate the score and rare labels barely influence it. It suits balanced multi-label data; macro-averaged F1 weights each label equally, exposing poor rare-label performance.
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Accuracy
Why it's wrong here
Accuracy counts every label prediction equally, so a model predicting only the dominant labels scores highly while rare labels are never detected. It suits balanced single-label problems; macro-averaged F1 treats each label equally and reveals rare-label failures.
- ✓
Macro-averaged F1 score
Why this is correct
Macro-averaged F1 computes the F1 score for each label independently, then takes the unweighted mean, so every label contributes equally regardless of frequency. This directly satisfies the stem's requirement to assess performance across all labels, preventing rare labels from being masked by the dominant classes in the imbalanced dataset.
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