MLA-C01 Question 212
Single answerYou are building a binary classification model to predict whether a customer will purchase a product based on their behavior on your e-commerce platform. After training the model, your team evaluates its performance using multiple metrics. The business team has emphasized that minimizing false negatives is the top priority because missing potential customers who would purchase the product has a significant impact on revenue. Which evaluation metric should you prioritize to align with this business requirement?
- A
Accuracy
- B
F1 Score
- C
Precision
- D
Recall
Show answer and explanation
Correct answer: D
Explanation
Recall is the most appropriate metric when the focus is on minimizing false negatives. In this scenario, the business team prioritizes identifying all potential customers who would purchase a product to maximize revenue. While other metrics like accuracy, precision, and F1 score provide value in different contexts, they do not directly address the problem of false negatives as recall does.
- A. Incorrect.
Accuracy measures the overall correctness of the model but does not account for the relative importance of false negatives or false positives. Since the business requirement emphasizes minimizing false negatives, accuracy is not the most suitable metric in this case.
- B. Incorrect.
F1 Score is the harmonic mean of precision and recall, balancing both metrics. While it is useful, it does not directly emphasize minimizing false negatives, which is the specific focus of the business requirement.
- C. Incorrect.
Precision focuses on minimizing false positives (incorrectly predicting a purchase). However, the business case prioritizes minimizing false negatives (missing actual purchasers), making precision less relevant here.
- D. Correct.
Recall measures the model's ability to correctly identify all positive cases (actual purchasers). It directly addresses minimizing false negatives, aligning with the business requirement.