Blurb::
Compute surrogate quality metrics

Description::
Diagnostic metrics assess the goodness of fit of a global surrogate to
its training data.

The default diagnostics are:
\li \c root_mean_squared
\li \c mean_abs
\li \c rsquared

Additional available diagnostics include
\li \c sum_squared
\li \c mean_squared
\li \c sum_abs
\li \c max_abs

The keywords \c press and \c cross_validation further specify
leave-one-out or k-fold cross validation, respectively, for all of the
active metrics from above.

<b>Usage Tips</b>
When specified, the \c metrics keyword must be followed by a list of
quoted strings, each of which activates a metric.

Topics::	surrogate_models

Examples::
This example input fragment constructs a quadratic polynomial
surrogate and computes four metrics on the fit, both with and without
5-fold cross validation. (Also see
dakota/share/dakota/test/dakota_surrogate_import.in for additional
examples.)

\verbatim
model
  surrogate global
    polynomial quadratic
    metrics = "root_mean_squared" "sum_abs" "mean_abs" "max_abs"
    cross_validation folds = 5
\endverbatim

Theory::

Most of these diagnostics refer to some operation on the
residuals (the difference between the surrogate model and the truth
model at the data points upon which the surrogate is built).

For example, \c sum_squared refers to the sum of the squared
residuals, and \c mean_abs refers to the mean of the absolute value of
the residuals. \c rsquared refers to the R-squared value typically
used in regression analysis (the proportion of the variability in the
response that can be accounted for by the surrogate model). Care
should be taken when interpreting metrics, for example, errors may be
near zero for interpolatory models or rsquared may not be applicable
for non-polynomial models.

Faq::
See_Also::	
