## 3 Results and Discussion
### Tree-Based Feature Importance  
Using RF technique, we produced feature importance charts. During this method, importance measures determine the split when growing a decision tree. Figure 9 depicts the relative variable importance of the most influential features for predicting good layers in the DED process for (a) A Single Decision Tree, (b) An Ensemble of Decision Trees, and (c) XGBoost technique. Intuitively comparing DT and RF importance values suggests that the values measured from RF are more reliable. Therefore, we select features from RF to feed into our CNN model. The barchart from XGBoost technique is slightly more nontransparent since there is a clear bias towards contour flag and other features are not considered important. Importance values calculated from RF technique show that the five most influential features are, (1) LP (0.19), (2) MPS (0.18), (3) SLED (0.18), (4) ET (0.13), and (5) PCG (0.12). Figure 10 demonstrates the features upon which the RF selected for splits. During this ensemble we restricted the RF to 3 levels as to not overgrow our model and lose interpretability. The main root begins by splitting upon the LP feature which splits at a value of 362.5 W. From there, two branches are created. Samples less than or equal to 362.5 W split to the left branch and samples with higher LP values split to the right. The left branch splits upon LP again at a value of 286.2 W. Samples with values less than 286.2 W move to a branch (left) dictated by the PDS which splits at a value of 1.1 RPM, while samples with a value greater than or equal to 286.2 W filter to another branch dictated by LP at a value of 289.5 W. Both of these branches lead to leaf nodes. Moving back to the root node, if a sample has an LP value more than 362.5 W it splits to the right branch. This branch is decided by MPS at a value of 1.3 mm. Samples with MPS less than or equal to 1.3 mm are organized to another branch predicated on PDS and move to a leaf node depending on corresponding PDS values. If a sample possesses MPS greater than 1.3 mm they are split to a branch based on SLED. These samples then are split into leaf nodes. Most samples (1180 + 1120) are filtered down the path that splits upon LP \(<=\) 362.5 W \(\rightarrow\) LP \(<=\) 286.2 W\(\rightarrow\) PDS. Samples following that path are all deemed good. When we evaluate XGBoost and the bias it has towards contour flag further with the heat map in Figure 11, we can see a possible explanation. There are several features highly correlated with the contour flag feature. These same features (LP, MPS, and SLED) are measured as highly important from the RF algorithm. Figure 10: RF tree. Figure 9: Feature importance bar plots: (**a**) Decision Tree, (**b**) Random Forest, (**c**) XGBoost. Table 3 showcases the measures of importance from each of the three feature selection techniques. We understand that the RF technique provides more confidence in the splits compared to the decision tree method since RF is an ensemble of trees. Thus, for this reason we do not consider the measures from the decision tree for feature reduction. The XGBoost technique was eliminated from consideration due to poor results. In the XGBoost, the model predicts that most of the importance comes from the CF feature (0.44). The remaining features carry little to no weight which is not useful for this investigation. Figure 11 shows the correlation amongst the 8 most correlated features in the experiment. The features with the most correlation are SLED and LP with a positive relationship of 0.97. This relationship demonstrates almost a direct linear and positive trend between the two features which is expected since SLED is calculated with input from LP. CF has relationships with PDS (0.79), LP (\(-\)0.67), SLED (\(-\)0.65), MPS (0.39), and feed rate (\(-\)0.13). Negative correlations exist between LP/PDS (-0.66), LP/CF (\(-\)0.67), SLED/PDS (\(-\)0.66), and SLED/CF (\(-\)0.65).