easyMultiple ChoiceObjective-mapped
MLA-C01 Practice Question: A data scientist is training a binary…
A data scientist is training a binary classification model using imbalanced data where the positive class is only 1% of the dataset. The scientist wants to maximize the recall for the positive class while maintaining reasonable precision. Which evaluation metric is most appropriate to tune during model selection?
⚠ Common exam trap
AWS often tests the misconception that AUC-ROC is always the best metric for imbalanced data, but the trap here is that AUC-ROC can remain high even when the model fails to recall the minority class, whereas the F1 score directly penalizes poor recall.
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 the positive class is only 1%. By tuning the F1 score, the data scientist directly balances the trade-off between maximizing recall (capturing true positives) and maintaining reasonable precision (avoiding false positives), which aligns with the stated goal.
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 probability calibration, not classification performance for the minority class directly.
- ✗
Area under the ROC curve (AUC)
Why it's wrong here
AUC measures rank ordering but does not directly optimize recall at a specific threshold.
- ✓
F1 score
Why this is correct
F1 score combines precision and recall, making it suitable for imbalanced classes when both matter.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model predicts all negatives, failing to capture the minority class.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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