AI0-001 AI Concepts and Foundations Practice Question
When evaluating a binary classification model, which two metrics are most appropriate for imbalanced datasets? (Choose two.)
⚠ Common exam trap
CompTIA often tests the misconception that accuracy is always the best metric, but the trap here is that accuracy fails on imbalanced datasets, and candidates must recognize that recall and precision are the appropriate pair for evaluating minority class 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
✓
Recall
Recall (Option C) is correct because it measures the proportion of actual positive cases correctly identified, which is critical in imbalanced datasets where the minority class is of primary interest. Precision (Option E) is correct because it measures the accuracy of positive predictions, helping to avoid false positives when the positive class is rare. Together, recall and precision provide a balanced view of model performance on the minority class, unlike accuracy which can be misleadingly high by simply predicting the majority 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.
- ✗
Accuracy
Why it's wrong here
Accuracy is misleading in imbalanced datasets because a model predicting only the majority class can achieve high accuracy.
- ✗
Mean absolute error
Why it's wrong here
MAE is a regression metric, not used for classification evaluation.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, essential for capturing minority class.
- ✗
R-squared
Why it's wrong here
R-squared is a regression metric, not applicable to classification.
- ✓
Precision
Why this is correct
Precision measures the proportion of true positives among predicted positives, important for imbalanced data.
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