Question 951 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on imbalanced data (95% negative, 5% positive). The model achieves 99% accuracy on the test set but fails to detect any positive cases. Which metric should the scientist focus on to evaluate model 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
✓
Recall
Recall (true positive rate) measures the ability to find all positive samples, which is critical for imbalanced datasets where accuracy can be misleading. Option A is wrong because accuracy is high but misleading in imbalanced data. Option C is wrong because RMSE is a regression metric, not suitable for classification. Option D is wrong because precision focuses on the accuracy of positive predictions but does not capture missed positives; recall is more important for detecting all positive cases.
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 is high but misleading due to class imbalance.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified.
- ✗
RMSE
Why it's wrong here
RMSE is for regression, not classification.
- ✗
Precision
Why it's wrong here
Precision is relevant but does not account for false negatives.
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Last reviewed: Jun 20, 2026
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