NCA-AIIO Question 68
Single answerYou are tasked with comparing the performance of two machine learning models trained on the same dataset for a regression task. Model A has a Mean Squared Error (MSE) of 4.5, while Model B has an MSE of 3.2. Additionally, Model A has an R-squared (R²) value of 0.78, while Model B has an R² value of 0.81. Based on these metrics, which model performs better and why?
- A
Model A performs better because it has a higher R² value than Model B.
- B
Model B performs better because it has a lower MSE and a higher R² value than Model A.
- C
Model A performs better because its MSE is higher, indicating it generalizes better.
- D
Model B performs better because the R² value is irrelevant, and its MSE is lower.
- E
Both models perform equally well because the difference in metrics is insignificant.
Show answer and explanation
Correct answer: B
Explanation
Model B outperforms Model A as it has a lower Mean Squared Error (MSE), indicating smaller prediction errors, and a higher R-squared (R²) value, which shows that it explains a larger proportion of the variance in the data. Both metrics are crucial for comparing regression models.
- A. Incorrect.
Incorrect: Model A does not have a higher R² value; Model B has a higher R² value (0.81 vs. 0.78). This option misinterprets the metrics.
- B. Correct.
Correct: Model B has a lower MSE (3.2 vs. 4.5) and a higher R² value (0.81 vs. 0.78), indicating that it explains more variance in the data and has less prediction error.
- C. Incorrect.
Incorrect: A higher MSE does not indicate better performance. In regression tasks, lower MSE is preferred as it reflects smaller errors.
- D. Incorrect.
Incorrect: While Model B does have a better MSE, the R² value is not irrelevant. R² provides additional context by measuring the proportion of variance explained by the model.
- E. Incorrect.
Incorrect: The differences in MSE and R² values are significant enough to conclude that Model B performs better.