DA0-002 Data Analysis Practice Question
A data scientist is building a classification model to predict customer churn. The dataset has 10,000 records with 500 churners. The scientist uses logistic regression and achieves 98% accuracy, but the precision for churn class is only 15%. Which of the following is the most likely cause?
⚠ Common exam trap
CompTIA often tests the misconception that high accuracy always means a good model, hiding the fact that with imbalanced data, accuracy is misleading and metrics like precision, recall, or F1-score for the minority class are critical.
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
✓
Class imbalance
The dataset has only 500 churners out of 10,000 records (5% churn rate), which is a classic class imbalance. Logistic regression can achieve high accuracy by simply predicting the majority class (non-churn) for all records, yielding 95% accuracy even without learning anything about churn. The very low precision (15%) for the churn class indicates that most of the positive predictions are false positives, a direct consequence of the model being biased toward the majority class due to imbalance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Class imbalance
Why this is correct
With only 500 churners out of 10,000, the model predicts most as non-churners, achieving high accuracy but low precision for the minority class.
- ✗
Non‑linear decision boundary
Why it's wrong here
Logistic regression assumes a linear decision boundary; if the boundary is non‑linear, overall accuracy would drop, but precision for the minority class could still be higher if the model captures it.
- ✗
Multicollinearity among predictor variables
Why it's wrong here
Multicollinearity inflates standard errors but does not typically cause low precision for a minority class.
- ✗
Overfitting due to too many features
Why it's wrong here
Overfitting would cause high training accuracy but lower test accuracy; it does not directly cause low precision for a specific class unless combined with imbalance.
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