NCA-AIIO Question 69
Select 4You are tasked with comparing two machine learning models to determine which one should be deployed. Model A has a lower mean squared error (MSE) on the test dataset, while Model B has a higher R² (proportion of explained variance). Both models are trained on the same dataset and evaluated using the same metrics. Which of the following considerations should guide your decision?
- A
Choose Model A because a lower MSE indicates better prediction accuracy.
- B
Choose Model B because a higher R² means the model explains the variance in the data more effectively.
- C
Consider the specific use case and whether minimizing errors or maximizing explained variance is more critical.
- D
Use additional metrics such as precision and recall to make a final decision, as they are always required for regression tasks.
- E
Check for overfitting or underfitting in both models before making a final decision.
Show answer and explanation
Correct answers: A, B, C, E
Explanation
The decision between Model A and Model B should be guided by a combination of performance metrics (MSE and R²) and the specific use case. MSE is more relevant if prediction accuracy is the priority, while R² is important for understanding the variance explained by the model. Additionally, verifying the models for overfitting or underfitting ensures the metrics are valid. Precision and recall are not applicable here as they are classification metrics.
- A. Correct.
A lower MSE indicates that Model A has smaller average prediction errors, which might be desirable depending on the use case.
- B. Correct.
A higher R² shows that Model B captures more variance in the target variable, which could be important for understanding the relationship between features and predictions.
- C. Correct.
The choice of model should depend on the specific application and whether minimizing errors (MSE) or maximizing variance explanation (R²) aligns better with the business or scientific objective.
- D. Incorrect.
Precision and recall are metrics typically used for classification tasks, not regression tasks, making this option irrelevant for the current scenario.
- E. Correct.
Checking for overfitting or underfitting is essential to ensure that the performance metrics (MSE and R²) are reliable and not skewed by these issues.