Question 1,636 of 1,672
MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on an imbalanced dataset where the positive class represents only 1% of the data. The model achieves 99% accuracy but fails to identify most positive cases. Which metric should the data scientist use to evaluate model performance?
⚠ Common exam trap
Many exam-takers default to accuracy as the primary metric, overlooking how imbalanced data can inflate accuracy while hiding poor positive class detection, which the F1 score directly addresses.
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
The F1 score is the harmonic mean of precision and recall, making it ideal for imbalanced datasets where accuracy is misleading. Since the model achieves 99% accuracy by simply predicting the majority class (negative), it fails to capture positive cases; F1 score penalizes this by balancing false positives and false negatives, providing a more truthful performance measure.
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.
- ✓
F1 score
Why this is correct
F1 score balances precision and recall, suitable for imbalanced data.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading for imbalanced datasets.
- ✗
RMSE
Why it's wrong here
RMSE is for regression, not classification.
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Last reviewed: Jun 24, 2026
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