MLS-C01 Modeling Practice Question
A data scientist is training a binary classification model on a dataset with a severe class imbalance (95% negative, 5% positive). The model achieves 95% accuracy but only correctly identifies 10% of the positive class. Which metric should the data scientist use to evaluate model performance?
⚠ Common exam trap
The trap here is that candidates see high accuracy and assume the model is good, but AWS tests the understanding that accuracy is meaningless for imbalanced datasets, and that AUC can be misleadingly high even when minority class recall is poor.
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 robust to class imbalance. With 95% accuracy but only 10% recall on the positive class, the model is essentially a trivial classifier that predicts the majority class. F1 score captures both false positives and false negatives, providing a balanced view of performance on the minority class.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Log loss
Why it's wrong here
Log loss measures probabilistic predictions but does not address class imbalance directly.
- ✓
F1 score
Why this is correct
F1 score balances precision and recall, making it suitable for imbalanced datasets where the minority class is important.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading because a model that always predicts the majority class achieves 95% accuracy but fails to identify positives.
- ✗
Area under the ROC curve (AUC)
Why it's wrong here
AUC evaluates overall separability but does not directly measure performance on the minority class.
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