## 3 Results
### Comparing ANN and XGB over Varying Filter Fractions  
As mentioned in the explanation of the approach to this work, two different models are used to predict the decored mass. Figure 13 already gives a first hint about their corresponding performance. The XGB models achieved the lowest RMSE and generated the best models for all except two datasets. Figures 21 and 22 allow a second perspective as they show the mean RMSE plotted over the filter fractions for both kinds of models. The values at 100% filter fraction can be used to compare the results of both models. The XGB models demonstrate less variation in the model scores than the ANN models. Furthermore, they indicate a clear preference for the light-colored datasets without extended features. The ANN models achieve the best performance with the datasets without the extended features but show less preference. ANN models demonstrate exceptionally high RMSE for all datasets containing FFT features, especially compared to the XGB models. These score worse values for the FFT features as well but less strongly than the ANN. With increasing feature reduction, the ANN score improves more than the XGB scores. With increasing feature reduction, the ANN score improves more than the XGB scores.  
Figure 19: Mean RMSE of best ANN parameter set. Number of neurons in layer 1 is twice the number of neurons in layer 2.  
Figure 20: Mean RMSE of best ANN parameter set. Layer 1 equals layer 2. Both are twice that of layer 3.  
In summary, the best models of each type perform similarly. Nevertheless, the ANN models have more difficulty handling the data and profit more from the complexity reduction. DS 6 and DS 8 profit the most from the data complexity reduction. Both datasets contain a high number of FFT features. Interestingly, DS 5 and DS 7 do not show this behavior to such a high degree. The explanation is the ratio of features to datapoints. DS 6 and 8 are extended-feature datasets with significantly fewer datapoints than DS 5 and DS 7. Reducing the complexity allowed for a similar mean RMSE for all four datasets, despite the difference in the number of datapoints. All models show a stagnating or decreasing RMSE for reducing data complexity up to a given threshold. Going below this filter fraction leads to a significantly higher mean RMSE. The features of the given dataset at this filter threshold are important for predicting the decoring behavior. Dropping them from the dataset reduces the model quality. Figure 16 shows the features in descending order of importance. The features on top are, thus, the last to be dropped when reducing the  
Figure 21: ANN mean RMSE plotted over varying filter fractions. Light line colors represent datasets without extended features; dark line colors represent datasets with extended features.  
Figure 22: XGB mean RMSE plotted over varying filter fractions. Light line colors represent datasets without extended features; dark line colors represent datasets with extended features.  
filter fraction further. The number of features is always an integer and is rounded up as appropriate. Especially for the datasets with few features, only a single feature remains below single-digit filter fractions.