Question 905 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). Which metric is most appropriate for evaluating model performance?
⚠ Common exam trap
It's easy for candidates to default to accuracy as the primary metric, not realizing that with severe class imbalance, accuracy can be artificially high and completely mask poor performance on the minority class.
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
✓
Area Under the ROC Curve (AUC-ROC)
AUC-ROC is the most appropriate metric for imbalanced binary classification because it evaluates the model's ability to distinguish between positive and negative classes across all classification thresholds, without being biased by the 95% negative majority. It measures the trade-off between true positive rate and false positive rate, making it robust to class imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
R-squared
Why it's wrong here
R-squared is for regression, not classification.
- ✗
Mean Squared Error (MSE)
Why it's wrong here
MSE is a regression metric, not suitable for classification.
- ✓
Area Under the ROC Curve (AUC-ROC)
Why this is correct
AUC-ROC measures the model's ability to distinguish between classes regardless of threshold, suitable for imbalanced data.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets, as a model that predicts all negatives would achieve 95% accuracy.
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Last reviewed: Jun 24, 2026
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