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 dominated by the 9,500 non-churners, so a model predicting "no churn" everywhere scores 95% while catching zero churners; it cannot expose the 10% recall. Accuracy suits balanced classes where false positives and false negatives carry equal cost.
- ✗
F1 score
Why it's wrong here
F1 blends precision and recall into one number, so strong precision can mask the 10% recall and still yield a respectable score. F1 suits balanced trade-offs between false positives and false negatives, not this recall-critical churn scenario.
- ✓
Recall
Why this is correct
Recall measures the proportion of actual churners correctly identified, so 10% recall exposes the model's failure to catch churn despite 95% accuracy. Accuracy is misleading here because the 500 churners are a small minority of 10,000 records.
- ✗
Precision
Why it's wrong here
Precision measures how many predicted churners are genuine, so a model flagging few customers can score highly while missing 90% of actual churners. Precision is the right metric when the cost of false positives dominates, such as scarce retention budgets.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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