MLA-C01 ML Model Development Practice Question
A data scientist needs to evaluate a binary classification model. The dataset is highly imbalanced (5% positive class). Which metric is MOST appropriate for assessing 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
✓
AUC
AUC (Area Under the ROC Curve) is robust to class imbalance as it evaluates the model's ability to rank positive vs negative examples. Precision, recall, and F1 can be misleading if not threshold-optimized.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Precision
Why it's wrong here
Precision may be high but recall low; alone it doesn't capture overall performance.
- ✗
Accuracy
Why it's wrong here
Accuracy can be high even if the model predicts only the majority class, misleading in imbalanced datasets.
- ✗
Recall
Why it's wrong here
Recall may be high but precision low; alone it's not sufficient.
- ✓
AUC
Why this is correct
AUC measures ranking quality and is insensitive to class imbalance.
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