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Dakota Reference Manual
Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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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 k-fold cross validation | ||
| Optional | press | Leave-one-out 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 leave-one-out or k-fold 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 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.
1.8.4