What is the objective when comparing models using MAPE, MSE, and MAE?

What is the objective when comparing models using MAPE, MSE, and MAE?

• A. Minimize all three measures
• B. Maximize all three measures
• C. Minimize only MAPE
• D. Ignore the measures

Answer: (A)

The objective is to minimize prediction error across the three metrics. MAPE, MSE, and MAE are all measures of how far predictions are from the actual values, and in each case smaller values indicate a better fit. Therefore, when comparing models you aim to minimize these error measures, since that means the model’s predictions are closer to reality. You wouldn’t try to maximize them or ignore them. In practice, you may see trade-offs between metrics, but the underlying goal is to reduce error as much as possible across them.

The objective is to minimize prediction error across the three metrics. MAPE, MSE, and MAE are all measures of how far predictions are from the actual values, and in each case smaller values indicate a better fit. Therefore, when comparing models you aim to minimize these error measures, since that means the model’s predictions are closer to reality. You wouldn’t try to maximize them or ignore them. In practice, you may see trade-offs between metrics, but the underlying goal is to reduce error as much as possible across them.