Which evaluation metric is most sensitive to large prediction errors in numeric prediction?

Question

BlackTom AI · Accepted Answer

When evaluating numeric predictions, different metrics respond differently to the size of errors.
Option 1: MAE (Mean Absolute Error) averages the absolute residuals. Large errors increase this metric, but their impact grows linearly, so it is not as sensitive to very large errors as quadratic measures.
Option 2: MAPE (Mean Absolute Percentage Error) scales errors by the true values, which can underweight or overweight errors depending on the scale of the target and can be undefined or inflated for small true values. Its sensitivity to outliers is not as direct or consistent as a squared-error metric.
Option 3: RMSE (Root Mean Squared Error) squares each residual, which disproportionately amplifies larger errors, then takes the square root. This makes RMSE highly sensitive to large prediction mistakes because a single large error can dominate the metric.
Option 4: ME (Mean Error) captures the average signed error, indicating bias but not the magnitude distribution. It can mask large individual errors by canceling positive and negative residuals, so it does not reflect the presence of large mistakes.
In summary, the squaring step in RMSE causes large errors to have a disproportionately large influence, relative to MAE, MAPE, and ME, making RMSE the most sensitive to substantial prediction mistakes.

Which evaluation metric is most sensitive to large prediction errors in numeric prediction?单项选择题

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