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MLA-C01 Practice Question: Building a binary classifier for credit default…
A company is building a binary classifier for credit default prediction. The dataset is highly imbalanced (98% no default). They want to maximize recall for the minority class while maintaining reasonable precision. Which metric should be optimized during hyperparameter tuning?
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
✓
F1 score
F1 score balances precision and recall, making it suitable for imbalanced datasets when both metrics are important. Other options are less appropriate because accuracy is misleading due to imbalance, precision ignores recall, and AUC-ROC does not directly optimize recall at a decision threshold.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
AUC-ROC
Why it's wrong here
AUC-ROC evaluates overall ranking but does not directly optimize recall at a specific threshold.
- ✓
F1 score
Why this is correct
F1 score is the harmonic mean of precision and recall, addressing both metrics.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleadingly high in imbalanced datasets by predicting the majority class.
- ✗
Precision
Why it's wrong here
Precision alone does not consider recall, so the model might miss many default cases.
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