AI0-001 AI Models and Data Engineering Practice Question
A data scientist is evaluating a logistic regression model for binary classification on highly imbalanced data. Which TWO metrics are most appropriate to assess model performance? (Choose TWO.)
⚠ Common exam trap
CompTIA often tests the misconception that accuracy is always a valid metric, or that F1 score is a primary metric rather than a derived one, leading candidates to select accuracy or F1 instead of the pair of precision and 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
✓
Recall
Recall (B) is correct because in highly imbalanced binary classification, the minority class (e.g., fraud or disease) is the focus. Recall measures the proportion of actual positives correctly identified, which is critical when missing a positive has high cost. Precision (C) is correct because it measures the proportion of predicted positives that are truly positive, which is essential when false positives are costly or when the model's positive predictions must be trustworthy.
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 can be high even if the model fails on the minority class, making it inappropriate for imbalanced data.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, critical for minority class performance.
- ✓
Precision
Why this is correct
Precision measures the proportion of positive identifications that were actually correct, important for imbalanced data.
- ✗
Mean squared error (MSE)
Why it's wrong here
MSE is a regression metric and not suitable for classification.
- ✗
F1 score
Why it's wrong here
Although F1 is useful, it is a single metric that combines precision and recall; the question explicitly asks for TWO metrics, and precision/recall are the foundational pair.
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