DA0-002 Data Analysis Practice Question
A data analyst is building a model to predict customer churn. The dataset has 10,000 records with 500 churned customers. The model predicts churn with 95% accuracy, but only identifies 10% of actual churners. Which metric best highlights this issue?
⚠ Common exam trap
Candidates often choose accuracy because it is a familiar and seemingly high value (95%), failing to recognize that in imbalanced datasets, accuracy can be deceptive and does not reflect poor performance on the minority class.
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 (also known as sensitivity or true positive rate) measures the proportion of actual positives correctly identified. With only 10% of actual churners detected, the model has a recall of 0.1, which directly highlights the failure to capture churners despite high overall accuracy.
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 high (95%) but misleading due to imbalance.
- ✗
F1 score
Why it's wrong here
F1 combines precision and recall, but recall alone directly shows the issue.
- ✓
Recall
Why this is correct
Recall is low (10%), showing the model fails to detect churners.
- ✗
Precision
Why it's wrong here
Precision may be high if false positives are low, but it does not capture missed churners.
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