AIF-C01 Question 56
Single answerA company is using a machine learning model for customer churn prediction. The model achieves high accuracy but has a low F1 score. The business team reports that the cost of incorrectly predicting a churned customer as staying (false negative) is significantly higher than incorrectly predicting a staying customer as churned (false positive). Which of the following metrics should the company prioritize to evaluate this model's suitability for their business requirements?
- A
Accuracy
- B
F1 Score
- C
Area Under the ROC Curve (AUC)
- D
Cost per User
Show answer and explanation
Correct answer: B
Explanation
In this scenario, the business impact of false negatives is higher than false positives, and the F1 score is a performance metric that balances precision and recall. This makes it the most suitable metric to evaluate the model's effectiveness given the business requirements.
- A. Incorrect.
Accuracy measures the overall correctness of predictions, but it does not account for the imbalance between false positives and false negatives, which is critical in this business case.
- B. Correct.
F1 Score is the harmonic mean of precision and recall, making it a better metric when the cost of false positives and false negatives is imbalanced, as in this scenario.
- C. Incorrect.
AUC measures the model's ability to distinguish between classes across different thresholds, but it does not directly address the cost imbalance between false negatives and false positives.
- D. Incorrect.
Cost per User is a business metric, but it does not evaluate the performance of the machine learning model itself.