AI Engineering Degree Practice Exam 2026 - Free AI Engineering Practice Questions and Study Guide

The Relative Absolute Error (RAE) is used to measure the average absolute difference between the predicted values generated by a model and the actual values, relative to the mean of the actual values. This metric provides a way to evaluate the performance of predictive models by expressing the error in relation to the size of the data itself, allowing for a scale-invariant assessment of accuracy across different datasets.

Using the mean of the actual values as a reference point helps provide context for the magnitude of the error, indicating the model's performance in a more interpretable way. For instance, a lower RAE percentage suggests better model performance compared to a higher percentage, as it shows that the errors are smaller relative to the variation in the actual data.

This metric is particularly useful in fields where understanding the relative size of errors is critical, enhancing the interpretability of model accuracy beyond just providing an absolute error figure. It is different from absolute error metrics as it considers the average error relative to the actual data's average, thus giving a more standardized view of performance across different datasets.