NCA-AIIO Question 63
Single answerYou are tasked with evaluating two trained machine learning models for deployment in an AI-powered recommendation system. Model A has a Mean Squared Error (MSE) of 0.02, while Model B has an MSE of 0.04. Additionally, Model A explains 92% of the variance in the test dataset, whereas Model B explains 88%. Based on these statistical performance metrics, which model would you choose for deployment, and why?
- A
Model A, because it has a lower Mean Squared Error (MSE) and explains more variance.
- B
Model B, because it has a higher Mean Squared Error (MSE), which indicates better generalization.
- C
Model A, because a higher MSE is preferred in regression tasks.
- D
Model B, because even though its MSE is higher, its proportion of explained variance is more important.
Show answer and explanation
Correct answer: A
Explanation
When comparing models using statistical performance metrics, lower Mean Squared Error (MSE) indicates better prediction accuracy, and a higher proportion of explained variance suggests the model captures more variability in the data. Model A outperforms Model B on both metrics, making it the optimal choice for deployment.
- A. Correct.
Correct. Model A has a lower MSE, indicating it produces smaller errors on average, and explains more variance, which shows it fits the data better. These are key indicators of superior performance.
- B. Incorrect.
Incorrect. A higher Mean Squared Error (MSE) does not indicate better generalization. In fact, lower MSE is preferred as it reflects lower average prediction error.
- C. Incorrect.
Incorrect. A lower MSE is preferred in regression tasks, not a higher one. This statement is contradictory to standard practices in evaluating model performance.
- D. Incorrect.
Incorrect. While explained variance is an important metric, Model B does not outperform Model A in this regard. Model A explains more variance and also has a lower MSE, making it the superior choice.