MLS-C01 Question 234
Single answerAn e-commerce company has built a machine learning model to classify whether a product review is 'Positive' or 'Negative'. The following confusion matrix summarizes the model's performance on a test dataset:
| Predicted Positive | Predicted Negative | |
|---|---|---|
| Actual Positive | 450 | 50 |
| Actual Negative | 90 | 410 |
What is the precision of the model?
- A
0.83
- B
0.90
- C
0.82
- D
0.91
Show answer and explanation
Correct answer: B
Explanation
Precision is a metric used to evaluate the quality of positive predictions made by a model. It is calculated using the formula True Positives / (True Positives + False Positives). In this case, the number of True Positives is 450, and the number of False Positives is 90. Using the formula, precision = 450 / (450 + 90) = 0.90. Therefore, the correct answer is 0.90.
- A. Incorrect.
This value is incorrect because it does not align with the formula for precision, which is True Positives / (True Positives + False Positives).
- B. Correct.
This is the correct precision value. Precision is calculated as True Positives / (True Positives + False Positives), which equals 450 / (450 + 90) = 0.90.
- C. Incorrect.
This value is incorrect because it confuses precision with other metrics, such as recall or F1-score.
- D. Incorrect.
This value is incorrect because it miscalculates the number of True Positives and False Positives.