The relative absolute error (RAE) is very similar to the
relative squared error in the sense that it is also relative to a simple
predictor, which is just the average of the actual values. In this case, though, the error is just the total absolute error instead of the total squared error. Thus, the relative absolute error
takes the total absolute error and normalizes it by dividing by the
total absolute error of the simple predictor.
Mathematically, the relative absolute errorEi of an individual model
i is evaluated by the equation:
where P(ij) is the value predicted by
the individual model i for record j (out of n
records); Tj is the target value for record
given by the formula:
For a perfect fit, the numerator is equal to 0 and Ei
= 0. So, the Ei index ranges from 0 to infinity, with 0
corresponding to the ideal.