Question 580 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on an imbalanced dataset (95% negative class, 5% positive class). The model achieves 95% accuracy but only predicts the negative class for all examples. Which metric should the scientist use to evaluate model performance more appropriately?
⚠ Common exam trap
Candidates often choose F1 score (Option A) thinking it handles imbalance well, but they forget that F1 score requires at least some true positives to be meaningful, and in this extreme case where the model predicts only negatives, F1 score collapses to 0 or undefined, whereas AUC-ROC correctly identifies random performance.
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
✓
AUC-ROC
AUC-ROC is robust to class imbalance because it evaluates the model's ability to discriminate between positive and negative classes across all classification thresholds, rather than relying on a single threshold. In this scenario, the model predicts only the negative class, so its true positive rate is 0 and false positive rate is 0, yielding an AUC-ROC of 0.5 (random performance), which correctly reflects the model's lack of predictive power.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
F1 score
Why it's wrong here
F1 score is useful but may still be high if the model predicts only the majority class; AUC-ROC is more robust.
- ✗
Mean squared error
Why it's wrong here
MSE is for regression tasks, not classification.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model always predicts the majority class, which is misleading.
- ✓
AUC-ROC
Why this is correct
AUC-ROC evaluates the model's ability to distinguish between classes regardless of threshold and is robust to imbalance.
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Last reviewed: Jun 24, 2026
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