## 3 Results
### Basic Model Scores for Unfiltered and Filtered Datasets  
This chapter summarizes the model scores for all datasets, randomly initialized runs, and validation data. All shown ranks are calculated according to the mean RMSE over \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Datasets** & **Base Features** & **Extended Features** & **Aggregated Composition Features** & **Signal Features** & **No. of Features** & **Number of Datapoints** \\ \hline DS 1 & X & & & none & 10 & 280 \\ DS 2 & X & X & & none & 24 & 81 \\ \hline DS 3 & X & & X & none & 24 & 280 \\ DS 4 & X & X & X & none & 32 & 81 \\ \hline DS 5 & X & & & all & 4842 & 280 \\ DS 6 & X & X & & all & 4856 & 81 \\ \hline DS 7 & X & & & FFT & 4810 & 280 \\ DS 8 & X & X & & FFT & 4824 & 81 \\ \hline DS 9 & X & & & mfcc & 35 & 280 \\ DS 10 & X & X & & mfcc & 49 & 81 \\ \hline DS 11 & X & & & time-based & 17 & 280 \\ DS 12 & X & X & & time-based & 31 & 81 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: All datasets with their composition, feature, and data count. “X” means these features are included in the dataset. Figure 12: Superposition of all 10,988 impacts. the ten random initializations of one parameter set. The values are calculated based on that dataset's randomly selected 10% test data or the fixed-selected validation data. A filter fraction of less than 100% means that a reduced feature set achieved the lowest RMSE for that dataset. Due to the volume of results, the tabular overview can be found in Appendix A in Tables A6 and A7. As a point graph, Figure 13 shows the results for the three core metrics, mean RMSE, mean MAE rescaled, and relative mean MAE. A linear connection line was added to indicate the trend and connection of the points. The datasets are sorted by the achieved mean RMSE of their best model in ascending order meaning the overall best model is on the very left and the overall worst model on the very right. The integrated table depicts the composition of each dataset. It also includes the filter fraction and model type of the best model for each dataset. Figure 13: Main results of the model training by showing the best model of each dataset (DS). The datasets are sorted by the achieved mean RMSE of their best model in ascending order meaning the overall best model is on the very left and the overall worst model on the very right. The integrated table depicts the composition of each dataset. “X” means these features are included in the dataset. It also includes the filter fraction and model type of the best model for each dataset. The graph shows the main model metric, the mean RMSE (circle). Additionally, it shows the best RMSE achieved for the same parameter (triangle) and the best RMSE over all parameter sets and randomly initialized runs (square). Furthermore, it shows the mean MAE (circle), rescaled and relative, as well as the corresponding best-achieved value in the ten randomly initialized runs (triangle). All values are calculated using the test data. The range of the mean MAE is between 5.9 g and 10 g, corresponding to a relative mean MAE of 19% to 33%. The difference between the mean MAE and the best-achieved MAE of the model depicts the variation and range of the MAE for the ten runs of a parameter set. For the best-ranking models, this variation diminishes, which can be interpreted as a parameter set resulting in a robust model. The figure also includes the best run value for all three core metrics. The best run out of the ten randomly initialized runs is defined as the one with the lowest RMSE. Furthermore, for each dataset, the lowest achieved RMSE overall runs of all models was added to illustrate the absolute minimum achieved for all datasets. An evident diminishing variation for the best-ranking models can be seen. Additionally, it depicts the less robust behavior of models for datasets with fewer datapoints, DS 2, 4, 6, 8, 10, and 12. Therefore, some models achieved lower RMSE with the extended features of these datasets than the best-ranking models according to the mean RMSE.