NCA-AIIO Question 64
Single answerAn AI research team is evaluating two machine learning models trained to predict housing prices. Model A has a Mean Squared Error (MSE) of 8.5, while Model B has a Mean Squared Error of 7.2. Additionally, Model A has an R-squared value of 0.78, and Model B has an R-squared value of 0.75. Based on these metrics, which of the following conclusions is correct?
- A
Model A performs better because it has a higher R-squared value.
- B
Model B performs better because it has a lower Mean Squared Error.
- C
Model A performs better because it minimizes both MSE and R-squared.
- D
Model B performs worse because R-squared is not as important as MSE.
Show answer and explanation
Correct answer: B
Explanation
When comparing models, Mean Squared Error (MSE) provides a direct measure of prediction error, making it more suitable for evaluating performance in this scenario. While R-squared is a useful metric for understanding the proportion of variance explained by the model, the lower MSE of Model B (7.2 vs. 8.5) indicates that it performs better in predicting housing prices.
- A. Incorrect.
While Model A has a higher R-squared value, it does not necessarily mean it performs better overall, as MSE directly indicates prediction error.
- B. Correct.
Correct. A lower Mean Squared Error (7.2 vs. 8.5) indicates that Model B has better performance, as it predicts closer to actual values.
- C. Incorrect.
This is incorrect because Model A does not minimize both metrics. It has a lower R-squared value but a higher MSE compared to Model B.
- D. Incorrect.
This is incorrect because R-squared is still an important metric, even though MSE provides a direct measure of prediction error.