MLA-C01 Question 211
Single answerYou are developing a binary classification model to predict whether an email is spam or not spam. After training, you evaluate the model using a confusion matrix. The matrix shows the following results:
True Positives (TP): 80 False Positives (FP): 20 True Negatives (TN): 70 False Negatives (FN): 30
You want to select an evaluation metric that balances the trade-off between precision and recall. Which metric is the most appropriate in this scenario?
- A
Accuracy
- B
Precision
- C
Recall
- D
F1 Score
Show answer and explanation
Correct answer: D
Explanation
The F1 Score is specifically designed to balance the trade-off between precision and recall, making it the best metric to use when both are equally important. In scenarios like spam detection, where false positives (non-spam emails flagged as spam) and false negatives (spam emails not flagged as spam) can have different consequences, the F1 Score provides a more balanced evaluation of the model's performance.
- A. Incorrect.
Accuracy measures the overall correctness of the model by considering all predictions, but it does not specifically address the trade-off between precision and recall. It is not the best choice when balancing these metrics is critical.
- B. Incorrect.
Precision measures the proportion of correctly identified spam emails out of all emails labeled as spam. While useful, it does not address the trade-off with recall, which measures the ability to identify all actual spam emails.
- C. Incorrect.
Recall measures the proportion of actual spam emails correctly identified by the model. However, it does not consider the trade-off with precision, which accounts for false positives.
- D. Correct.
F1 Score is the harmonic mean of precision and recall, effectively balancing the trade-off between these two metrics. It is the most appropriate choice when both precision and recall are important.