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 (C) is correct because it measures the proportion of actual positives that were correctly identified (TP / (TP + FN)), which directly reflects how well the model catches the minority class that would otherwise be swamped in an imbalanced dataset. Precision (E) is correct because it measures the proportion of predicted positives that are truly positive (TP / (TP + FP)), exposing the false-positive cost that becomes critical when the positive class is rare. Together, precision and recall (often summarized by F1 or PR-AUC) focus on minority-class performance rather than being dominated by the majority class. Accuracy (A) is not appropriate because a trivial majority-class predictor can score very high accuracy while completely missing the minority class. Mean absolute error (B) and R-squared (D) are regression metrics and do not apply to binary classification evaluation.
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 counts every prediction equally, so a majority-class classifier scores highly while missing the minority class entirely. It is tempting because it is the default metric for balanced data, where class proportions match and overall correctness genuinely reflects performance.
- ✗
Mean absolute error
Why it's wrong here
Mean absolute error measures regression error magnitude on continuous targets, so it cannot express classification performance on skewed class distributions. It would be the right choice for evaluating regression predictions; imbalanced binary classification instead needs metrics such as precision-recall AUC or F1 that reflect minority-class performance.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual positives correctly identified, so on a 99.9:0.1 dataset it exposes how many rare fraudulent or minority cases the model misses. Accuracy would look deceptively high, making recall essential for imbalanced evaluation.
- ✗
R-squared
Why it's wrong here
R-squared quantifies variance explained in continuous regression outputs; class labels carry no such variance structure. It is tempting because it is a familiar goodness-of-fit statistic, and would be appropriate when evaluating a regression model predicting numeric values.
- ✓
Precision
Why this is correct
Precision measures how many predicted positives are genuinely positive, guarding against excessive false alarms when the minority class is rare. Paired with recall, it reveals whether the model's positive predictions on imbalanced data are trustworthy rather than driven by the majority class.
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