NCA-AIIO Question 67
Select 3You are training a deep learning model for image classification and need to compare its performance with an alternative model. Which of the following metrics would be appropriate for evaluating and comparing the models' performance based on their predictive accuracy and error minimization?
- A
Mean Squared Error (MSE)
- B
Proportion of Explained Variance (R-squared)
- C
Confusion Matrix
- D
Loss Function Value
- E
GPU Utilization Rate
Show answer and explanation
Correct answers: A, B, D
Explanation
Evaluating and comparing models involves statistical performance metrics that reflect the predictive accuracy or error minimization, such as MSE, R-squared, and Loss Function Value. These metrics provide quantifiable insights into the models' performance. While a confusion matrix and GPU utilization are important in other contexts, they do not directly serve as statistical comparisons of model performance.
- A. Correct.
Mean Squared Error (MSE) is a common statistical metric used to measure the average squared difference between predicted and actual values, making it a useful option for evaluating model performance.
- B. Correct.
Proportion of Explained Variance (R-squared) evaluates how well the model explains the variability of the target variable, making it a relevant metric for comparison.
- C. Incorrect.
Confusion Matrix provides detailed insights about classification errors but is not a direct statistical performance metric for model evaluation across different scenarios.
- D. Correct.
Loss Function Value represents the optimization target for the model and reflects how well the model minimizes error during training, making it a critical metric for comparison.
- E. Incorrect.
GPU Utilization Rate measures hardware resource usage and is not a relevant statistical performance metric for comparing model performance.