Error Analysis |
Cross-validation refers to the process of removing one of the n
observation points and using the remaining n-1 points to predict its value.
This process is repeated at each data point; for each estimate, n-1
used. The interpolation error at each data point is the
difference between its observed and predicted values.
An option is available to interpolate the error field to a uniform grid
(using a user-specified method). This has the effect of reducing
spatial bias in the estimate of the error.
The output of a cross-validation is an error field that can be
manipulated as any other field. The display of an error field includes
the error field, a table of error summary statistics, a histogram, and
a scatterplot. The overall error is computed either as the mean average
error (MAE) or the root mean square error (RMSE).