# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 5 Discussion of Results
### Graphical Techniques for Data Imputation Verification
By investigating these differences further, two studies were carried out with 60% augmentation, where one of the augmented datasets had very small differences in the change from the original dataset, and the other 60% with larger differences (more extreme with in the standard deviation (SD) limits). The one with more extreme data augmentation gained better SSE and a 20% increase in accuracy for unseen data cases. The distance error, however, was less favourable where it was slightly higher. The distance error metric suggests that the extreme augmented values may score better with an increased network size and iterations to learn the more complex, extreme data patterns. For this study, the network and iterations were all kept constant to make fair comparisons. Another ML technique was used to test the same augmented data as used with the NN--the metrics can be seen in Table 3. The metrics in the case of CART were a lot more favourable with this technique. Similar to NNs, CART is also a supervised technique, however, it provides rules for segregation, which, in a lot of cases, are more robust than producing a boundary curve to segregate data. SSE, however, is not measured here as no iterative input to output mappings occurred with this method in comparison. In short, cumulative SSE is predominately used for large iterative learning techniques such as NNs. It can be seen that a slight increase in data augmentation gives similar results to the NN, where 10% and 20% give 100% accuracy with both the distance error and unseen test cases. Beyond 20% data augmentation, the distance error and accuracy on unseen data all increase and decrease, respectively. Here, there are no real differences for both 60% augmented datasets. However, there is a slight increase in the distance error and a decrease in the accuracy for extreme augmented data, which could be due to the increase in complexity of the data patterns as seen with NNs. Table 4 displays \(R^{2}\) metrics, which provide the results with another view in terms of accuracy from the prediction side of the test data and the best found fit. MAE was also found; however this gives a similar linked metric to \(R^{2}\) and, therefore, a duplicity of results. Looking at Table 4 backs up the results discussed in this section, where 10% and 20% data augmentation give a better fit than without augmentation. However, increasing that data to much greater levels, such as 60% and 100%, creates more noise and diminishes the accuracy. Also, the results reinforce the extreme boundary data augmentation set compared to a normal boundary data augmentation set (for the two different 60% data augmentation sets). In the case of 100% augmentation, there is a higher score for NN and much less for CART, where a \(-1.23\) is reported for \(R^{2}\). The latter is significant to no real best fit and unable to give a sound prediction, whereas the NN is not that good, tending towards 0; however, there is some fit of the predicted data. This can be explained by the nature of the two techniques where the NN produces a boundary condition for the outputs and CART will have hard found rules that may not be able to fit the more complex data where overlaps or near overlaps exist. There are two methods discussed in Section 4.3, where imputation and augmentation can be used. The results displayed in Sections 4.2 and 4.3 display the transparency in producing such results as well as the trade-offs between extra data and extra n-dimensionality. This is very important as such data techniques cannot be applied in such a blind and ad hoc manner as there needs to be an evaluation, especially as this data are expected to be applied within a safety critical environment. If a good understanding and transparency is found within the produced data, then this should serve in reinforcing the machine learning techniques. The key idea behind data augmentation is to promote more accurate machine learning models through giving more extreme cases to allow rules to be more salient and with little to no ambiguity.