Fig. 3: RMSE behaviour across GAIN training iterations. | npj Materials Degradation

Fig. 3: RMSE behaviour across GAIN training iterations.

From: Predicting stress corrosion cracking in downhole environments: a Bayesian network approach for duplex stainless steels

Fig. 3

Throughout the training iterative process, the imputation accuracy of GAIN is monitored using RMSE for both training (blue line) and test (red line) subsets, where a proportion of 10% of their data is intermittently obscured to simulate missing data. The trends illustrate the GAIN stabilisation, achieving final RMSE values of 0.11 ± 0.05 for the training set and 0.19 ± 0.02 for the test set.

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