What is indicated by the term ‘absolute value’ in error measures?

The term ‘absolute value’ in error measures refers specifically to the difference between predicted and actual values without considering the direction of that difference. This means that it focuses solely on how far off a prediction is from the actual result, regardless of whether the prediction was above or below the actual value. By taking the absolute value, negative errors (where the prediction was higher than the actual) are treated the same as positive errors (where the prediction was lower than the actual). This is particularly useful in contexts where the magnitude of error is of primary concern, allowing for a straightforward assessment of performance without the complications introduced by negative values.

While other concepts related to error measures address specific aspects like percentage errors, maximum errors, or the square root of sum of errors, these do not encapsulate the core idea of ‘absolute value,’ which specifically emphasizes the non-directional nature of the differences being evaluated.