AI Associate AI Fundamentals Practice Question
A data scientist evaluates a churn prediction model. On the test set, the model achieves 99% accuracy, but the business reports that the model rarely flags actual churners. Which metric should the data scientist focus on to improve the model?
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
When classes are imbalanced (few churners), accuracy can be misleading. Recall measures how many actual churners are identified, which is the key business concern.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Recall
Why this is correct
Recall measures the fraction of actual churners correctly identified; improving recall directly addresses the business issue.
- ✗
F1 score
Why it's wrong here
F1 score balances precision and recall, but the primary problem is low recall; focusing on recall first is more direct.
- ✗
Accuracy
Why it's wrong here
Accuracy is high because non-churners dominate; it does not reflect poor churn detection.
- ✗
Precision
Why it's wrong here
Precision measures correctness of churn predictions but not how many churners are caught.
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