



Last update: February 19, 2014






Relative Squared Error
The relative squared error (RSE) is relative to what it would have been if a simple predictor had been used. More specifically, this simple predictor is just the average of the actual values. Thus, the relative squared error takes the total squared error and normalizes it by dividing by the total squared error of the simple predictor.
Mathematically, the relative squared error E_{i} 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); T_{j} is the target value for record
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.
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