MLA-C01 Question 210
Single answerYou are building a binary classification model to predict whether a customer will purchase a product (1) or not (0). After training the model, you evaluate its performance and observe the following metrics: Accuracy = 90%, Precision = 75%, Recall = 60%. Based on these metrics, what can you infer about the model's performance?
- A
The model is highly effective at identifying all positive cases.
- B
The model has a high false positive rate compared to true positives.
- C
The model may not be suitable for use cases where identifying all positive cases is critical.
- D
The model's high accuracy indicates it is performing well across all aspects.
Show answer and explanation
Correct answer: C
Explanation
The recall (60%) indicates that the model is missing a significant portion of the positive cases, which is critical in scenarios where identifying positive cases is a priority. While the accuracy is high (90%), it can be misleading and does not fully reflect the model's ability to handle the positive class effectively. Precision (75%) shows the model is moderately effective at identifying true positives among predicted positives but does not compensate for the low recall.
- A. Incorrect.
This is incorrect because the recall value (60%) indicates the model is not highly effective at identifying all positive cases. A high recall is necessary for this conclusion.
- B. Incorrect.
This is incorrect because the precision value (75%) shows the model has a moderate false positive rate, but it does not indicate a particularly high false positive rate.
- C. Correct.
This is correct because the recall (60%) is relatively low, meaning the model is missing many positive cases. For use cases where identifying all positive cases is critical, such as fraud detection or medical diagnostics, this model may not be appropriate.
- D. Incorrect.
This is incorrect because high accuracy alone does not guarantee good performance. Accuracy can be misleading, especially in imbalanced datasets, where the model may correctly predict the majority class while performing poorly on the minority class.