AI Associate AI Fundamentals Practice Question
A model predicts customer churn with 95% accuracy, but most customers who actually churn are not flagged by the model. Which metric should the team improve?
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 (true positive rate) measures how many actual churners are correctly identified. High accuracy with low recall indicates the model is biased toward the majority class (non-churners). Precision focuses on how many flagged churners actually churn, F1 is a balance, and accuracy is already high.
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 would improve if fewer false positives occur, but the issue is missing true churners (false negatives).
- ✓
Recall
Why this is correct
Recall measures the proportion of actual churners correctly predicted. Low recall indicates many churners are missed.
- ✗
Accuracy
Why it's wrong here
Accuracy is already high but misleading because the model fails on the minority class.
- ✗
F1 score
Why it's wrong here
F1 is a harmonic mean of precision and recall; while it might improve, the direct issue is low recall.
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