# Machine-Learning Approach to Determine Surface Quality on a Reactor Pressure Vessel (RPV) [PERSON]
## 1 Introduction
### Data Imputation and Augmentation for Low Data Sets
#### 1.2.3 Decision Trees, Random Forest, and Extra Trees Regression  
One of the regression and classification models being used is called Random Forest, which is based on a bootstrap aggregation of decision trees. Decision trees work much like humans in the sense that the algorithm asks questions about the existing data to see where to classify the data or until it reaches a prediction, which is based on data regression [22]. A decision tree is a flow chart like structure where the interior nodes represent the features, the branches are decision rules with questions that are answered in either True or False, and the stopping criteria is realised based on the impurity of a split--for example, Gini or Entropy. An example of such decision trees can be found in [23], which displays how the rules are found from the root node to the interior and leaf nodes.  
Random Forest fits multiple decision trees--hence the name Random Forest--on subsamples of the data and averages the decision trees to avoid over-fitting and improve the accuracy. The process of taking the mean of all the outputs is known as aggregation [24]. The benefit in using multiple decision trees comes from the 'random' attribute of the Random Forest algorithm. Each node in the decision tree works on a random subset of features and, as a result, each decision tree is individual.  
The Extra Trees Algorithm is related to decision trees and the Random Forest Algorithm. The Extra Trees Algorithm, unlike the Random Forest algorithm, which takes bootstrap samples of the data, fits the algorithm's overall data. Another difference is that it chooses where to split the nodes at random, whereas Random Forest chooses the optimal split for each node [25]. Splitting the nodes at random adds a random attribute to the Extra Trees, but it does increase variance. To work around this, the number of trees is increased [26]. Classification and Regression Trees (CART) work in a similar manner and are used to provide a second classification technique when testing improvements in performance when data augmentation is applied.  
Another technique used to test classification of augmentation is the Neural Network (NN). The NN used in this work is a multi-layered perceptron with a backpropagation learning rule that is very similar to the work carried out [27]. Due to the nature of the problem being non-linear, an input layer of 8 nodes is used to cover the 8 input values. Three hidden layers at 16, 32, and 32 nodes, respectively, were used to segregate the non-linear relationships, followed by a pure linear output layer of 6 nodes. This was to cater to all the different output states.  
Both techniques of CART and NNs are supervised in nature and used to investigate the differences in learning with both increased dimensions and applied augmented data to address such problems when presented with increased data dimensions.  
This paper is divided into the following sections, the experimental setup for investigating the surface quality effects to Barkhausen noise, the Barkhausen noise applied to different surface quality Charpy samples with the effects suppressed using non-magnetic variable thickness spacers, data imputation and augmentation for low datasets, the results section--followed by a discussion of results--and, finally, the conclusions.