Abstract
The use of the direct evaluation of the Gaussian
Process, using the square exponential function kernel prediction
at the given data points is often misleading towards evaluation
of the fit, given by the coefficient of determination. The
predicted value at the data points when using the Gaussian
Process, is almost at all cases equal to the original value. As such,
interpretation problems arise when coefficient of determination
suggest the model to be a good fit, but visual representations
suggest otherwise. We illustrate the difficulties in presenting the
coefficient of determination for the Gaussian Process and
recommend the use of alternative methods for the evaluation of
the predicted value, thus realizing the true function of the
coefficient of determination.