## 3 Results and Discussion
### Feature Importance Evaluation  
As seen in Figures 2-4 and Table 16, the most important feature is \"the mental suffering damages that plaintiff claims\", as we expected at first, and it corresponds with the results of feature selection, i.e., \"the mental suffering damages that plaintiff claims\" was selected by all models. In fact, this feature represents the disposition principle that all judges in Taiwan must abide by while assessing mental suffering damages, i.e., the assessment of mental suffering damages must be limited to the mental suffering damages that plaintiff claims. In a word, the final discretionary damages of mental suffering can only be equal to or lower than the mental suffering damages that the plaintiff claims. Therefore, the ML algorithms and permutation feature importance seem to be very clever and useful, because they automatically learn from the dataset and successfully reveal the important disposition principle in Taiwan without being taught by humans. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **MSE** & **RMSE** & \(R^{2}\) \\ \hline KNN & 762.1359 & 27.6068 & 0.7128 \\ \hline CART & 750.0399 & 27.3868 & 0.7173 \\ \hline RF & 728.9268 & 26.9986 & 0.7253 \\ \hline Rankings & & & \\ \hline
1 st & RF & RF & RF \\ \hline
2 nd & CART & CART & CART \\ \hline
3 rd & KNN & KNN & KNN \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The generalization performance and ranking of optimal KNN, CART, and RF models. Figure 3: Feature importance using permutation on the CART model. Figure 2: Feature importance using permutation on the KNN model. Next, we focus on \"the age of the victim\", because it was selected by all models, and the ranking of that feature was high in the KNN and CART models. In practice, some judges indicate that the age of the victim will affect the level of mental suffering, and some studies have ascertained that the older the victim is, the lower the discretionary damages \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Feature** & **KNN** & **CART** & **RF** \\ \hline The mental suffering damages that the plaintiff claims & 0.8105 & 1.1063 & 0.3327 \\ \hline The age of the victim & 0.3685 & 0.2432 & 0.0447 \\ \hline The defendant’s income & 0.3205 & - & 0.0577 \\ \hline The sum of mental suffering damages that all plaintiffs & - & 0.2680 & 0.0712 \\ claim in the same case & - & 0.0647 & 0.0343 \\ \hline The total number of plaintiffs who are the victim’s & - & 0.0793 & 0.1254 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: Comparison of important features. Figure 4: Feature importance using permutation on the RF model. of mental suffering will be [2]. Therefore, we may infer that the judges will consider the level of mental suffering to be slighter if the victim is older. In general, the death of a young person is often considered to be a pity according to public opinion, but the death of an older person, especially at a great age, is often considered to be a \"happy death\" or \"enough\", according to public opinion. Moreover, \"the defendant's income\" was selected and found to be important by the KNN model, and it was also selected by the RF model; thus, we can infer that this feature will have a major effect on the discretionary damages of mental suffering. However, whether the defendant's income or earning capacity should be considered while assessing the mental suffering damages gives rise to much controversy. Some studies have established that the defendant's income or earning capacity is completely irrelevant to mental suffering damages [49], whereas some other studies have discerned that the defendant's income or earning capacity is completely relevant to the mental suffering damages and should be considered, pointing out that the higher the defendant's income or earning capacity, the higher the discretionary damages of mental suffering should be [17]. It is clear that judges in Taiwan may take the view and opinion of the latter, according to the ranking of feature importance we revealed.