MLS-C01 Question 218
Single answerYou are building a binary classification model to predict whether an email is spam or not. After training the model, you evaluate it using precision, recall, and F1 score. Your team decides that minimizing false negatives (i.e., incorrectly classifying spam emails as non-spam) is more critical than minimizing false positives. Based on this requirement, which metric should you prioritize to evaluate your model's performance?
- A
Accuracy
- B
Precision
- C
Recall
- D
F1 Score
Show answer and explanation
Correct answer: C
Explanation
In scenarios where minimizing false negatives is prioritized, recall is the most relevant metric. Recall measures the ability of the model to correctly identify all positive instances (e.g., spam emails). A high recall value ensures that the model minimizes the number of false negatives, aligning with the team's objective. While F1 score balances precision and recall, it does not focus solely on false negatives, making recall the better choice in this case.
- A. Incorrect.
Accuracy measures the overall correctness of the model but does not provide insights into false negatives or positives, making it unsuitable for this specific objective.
- B. Incorrect.
Precision focuses on minimizing false positives, which is not the priority in this scenario. The emphasis is on minimizing false negatives instead.
- C. Correct.
Recall measures the proportion of actual positives correctly identified. Since minimizing false negatives is crucial, recall is the most appropriate metric to prioritize.
- D. Incorrect.
F1 Score is the harmonic mean of precision and recall, and while it balances both metrics, it does not focus specifically on minimizing false negatives, which is the primary concern here.