1Blurb::
2Compute surrogate quality metrics
3
4Description::
5Diagnostic metrics assess the goodness of fit of a global surrogate to
6its training data.
7
8The default diagnostics are:
9\li \c root_mean_squared
10\li \c mean_abs
11\li \c rsquared
12
13Additional available diagnostics include
14\li \c sum_squared
15\li \c mean_squared
16\li \c sum_abs
17\li \c max_abs
18
19The keywords \c press and \c cross_validation further specify
20leave-one-out or k-fold cross validation, respectively, for all of the
21active metrics from above.
22
23<b>Usage Tips</b>
24When specified, the \c metrics keyword must be followed by a list of
25quoted strings, each of which activates a metric.
26
27Topics::	surrogate_models
28
29Examples::
30This example input fragment constructs a quadratic polynomial
31surrogate and computes four metrics on the fit, both with and without
325-fold cross validation. (Also see
33dakota/share/dakota/test/dakota_surrogate_import.in for additional
34examples.)
35
36\verbatim
37model
38  surrogate global
39    polynomial quadratic
40    metrics = "root_mean_squared" "sum_abs" "mean_abs" "max_abs"
41    cross_validation folds = 5
42\endverbatim
43
44Theory::
45
46Most of these diagnostics refer to some operation on the
47residuals (the difference between the surrogate model and the truth
48model at the data points upon which the surrogate is built).
49
50For example, \c sum_squared refers to the sum of the squared
51residuals, and \c mean_abs refers to the mean of the absolute value of
52the residuals. \c rsquared refers to the R-squared value typically
53used in regression analysis (the proportion of the variability in the
54response that can be accounted for by the surrogate model). Care
55should be taken when interpreting metrics, for example, errors may be
56near zero for interpolatory models or rsquared may not be applicable
57for non-polynomial models.
58
59Faq::
60See_Also::
61