Which metric penalizes larger errors more heavily?

Which metric penalizes larger errors more heavily?

• A. Mean Absolute Error
• B. Mean Squared Error
• C. Mean Absolute Percentage Error
• D. R-squared

Answer: (B)

Larger errors are penalized more heavily when the metric squares the errors. Mean Squared Error takes each prediction error, squares it, and then averages. Squaring makes bigger errors grow much faster than smaller ones, so a few large mistakes drag the score up more than many small ones. This sensitivity to outliers is exactly why it punishes big deviations more than other metrics. In contrast, Mean Absolute Error uses the absolute value of errors, so all mistakes contribute linearly and large errors aren’t amplified. Mean Absolute Percentage Error expresses errors as a percentage of the true values, which can distort the penalty when true values are small. R-squared isn’t a direct penalty on error magnitude; it measures the proportion of variance explained by the model, not how large the residuals are.

Larger errors are penalized more heavily when the metric squares the errors. Mean Squared Error takes each prediction error, squares it, and then averages. Squaring makes bigger errors grow much faster than smaller ones, so a few large mistakes drag the score up more than many small ones. This sensitivity to outliers is exactly why it punishes big deviations more than other metrics.

In contrast, Mean Absolute Error uses the absolute value of errors, so all mistakes contribute linearly and large errors aren’t amplified. Mean Absolute Percentage Error expresses errors as a percentage of the true values, which can distort the penalty when true values are small. R-squared isn’t a direct penalty on error magnitude; it measures the proportion of variance explained by the model, not how large the residuals are.