The correlation coefficient Ci of an individual program
i is evaluated by the equation:
where Cov(T,P) is the covariance of the target and model outputs; and
st and sp are the corresponding
standard deviations, which are given by:
where P(ij) is the value predicted by the individual program
i for sample case j (out of n sample cases);
Tj is the target value for sample case j; andandare given by the
formulas:
The correlation coefficient is confined to the range [-1, 1]. When
Ci = 1, there is a perfect positive linear correlation between
T and P, that is, they vary by the same amount. When Ci = -1, there is a perfect negative linear correlation between
T and P, that is, they vary in opposite ways (when T increases,
P decreases by the same amount). When Ci = 0, there is no correlation between
T and P. Intermediate values describe partial correlations and the closer to 1 or -1 the better the model.
To evaluate the correlation coefficient 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.
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