MLS-C01 Modeling Practice Question
A company is building a binary classifier to detect fraudulent transactions. The dataset is highly imbalanced (99% legitimate, 1% fraudulent). Which metric is most appropriate for evaluating the model?
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
Precision and recall (or F1-score) are more informative for imbalanced datasets than accuracy, because a model predicting all legitimate would achieve 99% accuracy but be useless. F1-score balances precision and recall.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Accuracy
Why it's wrong here
Accuracy can be misleading due to class imbalance.
- ✗
Mean Squared Error
Why it's wrong here
MSE is for regression, not classification.
- ✓
F1-score
Why this is correct
F1-score considers both precision and recall, suitable for imbalanced data.
- ✗
Area Under the ROC Curve (AUC-ROC)
Why it's wrong here
AUC-ROC is useful but can be optimistic with severe imbalance; precision-recall curve is often better.
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