The relative absolute error 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 error E_{i} of an individual program
i is evaluated by the equation:
where P_{(ij)} is the value predicted by
the individual program i for sample case j (out of n
sample cases); T_{j} is the target value for sample case
j; andis
given by the formula:
For a perfect fit, the numerator is equal to 0 and E_{i}
= 0. So, the E_{i} index ranges from 0 to infinity, with 0
corresponding to the ideal.
To evaluate the RAE of your model both on the training and
testing data, you just have to go to the Results
Panel after a run and, although it is not shown there, it is
also evaluated there and kept for your future reference in the Report
Panel.
