## 3 Results
### Feature Importance Values  
Figure 16 shows SHAP plots of the best XGB model for six datasets. The meaning of the shortened feature names can be found in Tables A1-A3 in Appendix A. DS 1 in Figure 16a contains only the base features. DS 3 Figure 16b contains the aggregated features describing the composition of the sand-binder system. DS 7 in Figure 16c comprises the base data and FFT features. DS 9 in Figure 16d uses the mfcc features, and DS 11 in Figure 16e uses only the time-based signal features. Interestingly, the XGB model for DS 11 achieving the lowest mean RMSE filters all signal features and equals DS 1, which explains the same model scores for both datasets a shown in Figure 13. The sieving percentage of the used sand is the most important feature in all four plots. The sand mass, absolute and relative at the beginning of the interval, is also an important factor. The flexural strength, cohesion, phi, and compressive strength are also influential for all four models. The dataset using the mfcc features scores the lowest mean RMSE. Accordingly, 5 of the 25 mfcc features are listed in the most important feature plot. Figure 16b includes the sand-binder system names. In most cases, these aggregated composition features influence only its corresponding datapoints. For example, feature \"system_N\" has nearly no effect except on the datapoints belonging to system N, colored red in the plot. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & & **All 507 Parameter Sets** & \multicolumn{4}{c}{**20 Best Parameter Sets of Each Dataset**} \\  & & & & & **(According to Mean RMSE)** \\ \hline
**Datasets** & **Average** & \
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \(p\)**-Value** & **Average** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & \(p\)**-Value** \\ \hline DS 9 & 0.128 & \(6.07\times 10^{-4}\) & - & 0.096 & \(1.24\times 10^{-6}\) & - \\ DS 1 & 0.138 & \(6.12\times 10^{-4}\) & \(3.08\times 10^{-10}\) & 0.100 & \(2.76\times 10^{-7}\) & \(1.97\times 10^{-14}\) \\ DS 11 & 0.138 & \(9.57\times 10^{-4}\) & \(1.82\times 10^{-8}\) & 0.101 & \(4.98\times 10^{-7}\) & \(4.31\times 10^{-18}\) \\ DS 3 & 0.134 & \(8.85\times 10^{-4}\) & \(6.63\times 10^{-4}\) & 0.100 & \(1.49\times 10^{-7}\) & \(2.15\times 10^{-14}\) \\ DS 7 & 0.161 & \(5.84\times 10^{-4}\) & \(2.43\times 10^{-86}\) & 0.117 & \(1.68\times 10^{-6}\) & \(1.43\times 10^{-37}\) \\ DS 5 & 0.161 & \(5.50\times 10^{-4}\) & \(1.67\times 10^{-84}\) & 0.119 & \(1.23\times 10^{-6}\) & \(7.83\times 10^{-41}\) \\ DS 12 & 0.174 & \(8.28\times 10^{-4}\) & \(9.63\times 10^{-123}\) & 0.132 & \(8.68\times 10^{-6}\) & \(3.11\times 10^{-26}\) \\ DS 10 & 0.179 & \(6.70\times 10^{-4}\) & \(2.97\times 10^{-157}\) & 0.137 & \(5.05\times 10^{-6}\) & \(8.84\times 10^{-34}\) \\ DS 4 & 0.176 & \(6.40\times 10^{-4}\) & \(4.81\times 10^{-147}\) & 0.147 & \(6.46\times 10^{-6}\) & \(5.84\times 10^{-33}\) \\ DS 2 & 0.177 & \(5.92\times 10^{-4}\) & \(3.43\times 10^{-152}\) & 0.148 & \(3.77\times 10^{-6}\) & \(2.65\times 10^{-40}\) \\ DS 8 & 0.218 & \(1.73\times 10^{-3}\) & \(2.57\times 10^{-206}\) & 0.152 & \(1.20\times 10^{-5}\) & \(3.85\times 10^{-28}\) \\ DS 6 & 0.220 & \(1.56\times 10^{-3}\) & \(1.61\times 10^{-222}\) & 0.156 & \(3.29\times 10^{-6}\) & \(6.30\times 10^{-45}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical evaluation of the twelve parameter sets. The \(p\)-value of a two-tailed [PERSON]’s \(t\)-test with a 0.05 significance level was calculated between each dataset and DS 9, which yielded the overall best RMSE. The first three columns represent the evaluation of the mean RMSE of all 507 parameter sets of each dataset. The last three columns show the evaluation of the mean RMSE of the best 20 parameter sets, which scored the best mean RMSE for each dataset. Figure 16: _Cont._  
A further aspect is whether the most important features change with varying the filter fraction, which is investigated by comparing DS 1, DS 3, and DS 9.