## 3 Results
### Descriptive Analysis of the Testing Data  
As the performance comparison relied on the testing dataset, it is important to conduct a descriptive analysis of the adopted data to gain an overview and contrast with the modelling data. Similar to the training data, each earphone design in the testing dataset was characterized by eight variables. The corresponding options and their distribution in the testing dataset can be seen in Table 5. With the intention of facilitating the comparison with the modelling dataset, the distribution of the modelling dataset was also included. From the inspection of the percentage values, although differences can be found among the categories, even distribution and full coverage of each class can be observed in the testing set, which is similar to the training data. Therefore, the testing set was eligible for the evaluation of the performance due to the shared characteristics. Conducting descriptive analysis of the testing dataset allows us to gain a better understanding of the distribution of the data and how it compares to the modelling data. By ensuring that the testing dataset has similar characteristics to the modelling dataset, we can have more confidence in the performance evaluation of the proposed approaches. This is important for ensuring that the evaluation is fair and accurate. On the whole, by conducting a descriptive analysis of the testing dataset, we can gain insights into the distribution of the data and ensure that it is eligible for the evaluation of the performance. This helps to ensure that the results of the evaluation are reliable and valid. \begin{table}
\begin{tabular}{c c c c c c} \hline  & & \multicolumn{2}{c}{**Testing Data**} & \multicolumn{2}{c}{**Training Data**} \\
**Parameters of the Design** & **Possible Options** & **N** & **\%** & **N** & **\%** \\  & **(Total = 168)** & **(Total = 100\%)** & **(Total = 388)** & **(Total = 100\%)** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Type \\ (Primary Unit) \\ \end{tabular} } & Dynamic Unit/Moving & 58 & 34.52\% & 196 & 50.52\% \\  & Coil & 29.76\% & 90 & 23.20\% \\  & Balanced Armature Unit & 60 & 35.71\% & 102 & 26.29\% \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Magnet Type \\ (Primary Unit) \\ \end{tabular} } & N35 Grade & 48 & 28.57\% & 119 & 30.67\% \\  & N40 Grade & 41 & 24.40\% & 114 & 29.38\% \\ \cline{1-1}  & N45 Grade & 45 & 26.79\% & 85 & 21.91\% \\ \cline{1-1}  & N45 Grade & 34 & 20.24\% & 70 & 18.04\% \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Voice Coils \\ (Primary Unit) \\ \end{tabular} } & Copper Wire & 60 & 35.71\% & 161 & 41.49\% \\ \cline{1-1}  & Aluminum Wire with & 56 & 33.33\% & 106 & 27.32\% \\ \cline{1-1}  & Copper Covered & 52 & 30.95\% & 121 & 31.19\% \\ \hline \end{tabular}
\end{table} 
Table 5: Distribution of the cases involved in the testing of the developed predictive models with respect to the eight design parameters and the associated options. Apart from the statistics of the testing dataset, the distribution of the training dataset are included for comparison. The sound quality performance of the earphones in the training dataset are depicted, while the six average scores of the indicators were computed. Along with the average scores of the training data, the performance average of both training and testing sets can be seen in Table 6, along with the visualization in Figure 4. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Sound Quality Indicator**} & **Average Score** & **Average Score** \\  & **(Testing Data)** & **(Training Data)** \\ \hline THD & 4.2024 & 3.9175 \\ Power of Output & 4.1845 & 3.8660 \\ Range of Frequency Response & 4.1488 & 3.8918 \\ SNR & 4.1905 & 3.9459 \\ Impedance of the Speaker & 4.1369 & 4.0052 \\ Headroom & 4.3155 & 4.0335 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Average score of the sound quality performance of the studied earphone design of the testing dataset can be observed in the table. Average values of sound performance in training data are included as well to facilitate comparison.