###### Abstract
In contrast, several studies have used features available directly from established datasets rather than textual documents to implement legal judgment prediction; thus, they did not use NLP techniques to extract features. For example, [PERSON] et al. used a dataset from the Supreme Court database and proposed random forests (RFs) to predict the voting behavior of the Court and its Justices [11]. In addition, [PERSON] et al. focused on child custody cases after divorce in Taiwan and used a tabular dataset made by legal experts to propose artificial neural networks (ANNs), decision trees, and gradient boosting to predict whether the father or the mother should receive custody. Moreover, they also demonstrated some specific features that judges considered important [12; 13; 14]. In addition, [PERSON] et al. used a private database provided by an energy supply company and proposed tree-based gradient boosting to predict the outcomes of energy market lawsuits [15]. Despite these efforts in legal judgment prediction based on ML techniques, most previous studies mainly focused on classification tasks instead of regression tasks, and there has been no research using ML techniques to predict the discretionary damages of mental suffering in fatal car accident cases in Taiwan and unveil the importance ranking of legal factors by permutation feature importance. In order to solve the above challenges, in this study, we propose three basic but well-known learning algorithms to predict the discretionary damages of mental suffering in fatal car accident cases in Taiwan, including k-nearest neighbor (KNN), classification and regression trees (CART), and RF, to determine whether basic and popular ML algorithms are good enough to address our prediction task and define future work according to the final results. In addition, we used permutation feature importance to reveal the importance ranking of legal factors. In addition, there was no need to implement NLP techniques, because we used a dataset made by legal experts, which contained features extracted by them. However, we faced two major difficulties in our work. To start with, many judges refuse to unveil the true values of legal factors due to the protection of rights to privacy,which strictly limited the amount of data in our dataset. Moreover, the range of discretionary damages of mental suffering in our dataset was very wide, i.e., from 15 to 500 in TWD ten thousand, which might have affected the model learning and decrease model performance. In addition, we hypothesized that the RF model should outperform other models, because the RF belonged to ensemble learning, which is often considered to be more robust than a single base learner [16]. Furthermore, we infer that \"the mental suffering damages that plaintiff claims\" should have higher importance because it represents the disposition principle that all Taiwanese judges must abide by when they are assessing mental suffering damages. To summarize, the experimental results show that the RF model outperformed other models and achieved good performance; therefore, litigants and their lawyers could predict the discretionary damages of mental suffering in advance, and they can easily break the uncertainty of judicial outcomes and wisely decide whether they should litigate or not. Furthermore, we have successfully revealed the importance ranking of legal factors, so legal actors can focus on the crucial factors and develop the best litigation strategies. For example, they can concentrate on finding evidentiary documents and materials about the most important or dominant legal factors instead of wasting their precious time and money on irrelevant legal factors. On the other hand, this methodology can be applied to other types of mental suffering damages prediction tasks in Taiwan with minor and suitable adjustments, including mental suffering from physical injuries or pain, humiliation, privacy infringements, and divorces.