Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Compute surrogate quality metrics
This keyword is related to the topics:
Alias: diagnostics
Argument(s): STRINGLIST
Default: No diagnostics
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Optional  cross_validation  Perform kfold cross validation  
Optional  press  Leaveoneout cross validation 
A variety of diagnostic metrics are available to assess the goodness of fit of a global surrogate to its training data.
The default diagnostics are:
root_mean_squared
mean_abs
rsquared
Additional available diagnostics include
sum_squared
mean_squared
sum_abs
max_abs
The keywords press
and cross_validation
further specify leaveoneout or kfold cross validation, respectively, for all of the active metrics from above.
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, sum_squared
refers to the sum of the squared residuals, and mean_abs
refers to the mean of the absolute value of the residuals. rsquared
refers to the Rsquared 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 nonpolynomial models.