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Accuracy

What is the approximation error of the result?

The approximation error indicators (predict the metamodel accuracy of the results) can be visualized in the LS-OPT Viewer.

New Plot

  • To view a new plot select the plot button on the task bar. A seperate window of LS-OPT Viewer opens up.

LS-OPT Viewer

  1. Select under Metamodel the item Accuracy
  2. From Entity select Responses → HIC.
  3. We can find RMS Error and R2 (R-sq) above the plot. 

new viewer

mainscreen_acc

→ Despite the coefficient of determination (R2) being high (=0.922), the RMS Error may need further improvement, e.g. more iterations or selection of a more suitable metamodel (e.g. Feedforward Neural Network or Radial Basis Function Network). Nevertheless for a raw estimate the result may be considered as acceptable.

 

→ It is important to note that the metamodel performs poor in cross-validation test using the SPRESS method. A relatively high SPRESS value (=91.4%) justifies a low confidence and a low predictive capability of the fitted metamodel.

HIC_acc

  • With the same procedure you may find the RMS Error, R² and SPRESS Residual of the other responses:

 

Mean Response Value

RMS Error

R2

SPRESS Residual

MASS   

0.7742

0

1

0 (0%)

Disp1   

-159.3827

2.0309 (1.27%)

   0.4783   

6.68 (4.19%)

Disp2   

-694.5841

5.5486 (0.80%)

0.9896

18.2 (2.62%)

HIC       

275.726

76.5620 (27.74%)

0.9225

252.11 (91.43%)