Abstract
The discretionary damage of mental suffering in fatal car accident cases in Taiwan is subjective, uncertain, and unpredictable; thus, plaintiffs, defendants, and their lawyers find it difficult to judge whether spending much of their money and time on the lawsuit is worthwhile and which legal factors judges will consider important and dominant when they are assessing the mental suffering damages. To address these problems, we propose k-nearest neighbor, classification and regression trees, and random forests as learning algorithms for regression to build optimal predictive models. In addition, we reveal the importance ranking of legal factors by permutation feature importance. The experimental results show that the random forest model outperformed the other models and achieved good performance, and “the mental suffering damages that plaintiff claims” and “the age of the victim” play important roles in assessments of mental suffering damages in fatal car accident cases in Taiwan. Therefore, litigants and their lawyers can predict the discretionary damages of mental suffering in advance and wisely decide whether they should litigate or not, and then they can focus on the crucial legal factors and develop the best litigation strategy.
Highlights
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 random forests (RFs), to determine whether basic and popular machine learning (ML) algorithms are good enough to address our prediction task and define future work according to the final results
The feature subset found by sequential backward selection (SBS) failed to improve the performance, so we inferred that some relevant features might be eliminated in early iterations, which means that once those features are excluded, they cannot be included later, even though those features might possibly increase the performance [48]
We successfully built an optimal regression model based on RF and achieved good performance, which can serve as a robust and professional tool for litigants and their lawyers, to predict the discretionary damages of mental suffering in advance; they can break the uncertainty of judicial outcome and wisely decide whether they should litigate or not
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. In cases where a victim is killed in a car accident, the father, mother, sons, daughters, and spouse of the deceased may claim for reasonable mental suffering damages in accordance with Article 194 of the Taiwan Civil Code. The mental suffering damages that come with losing someone are often difficult to calculate, and no standard formula exists [1]; judges need to consider numerous legal factors which have been indicated by the Taiwan Supreme Court to assess a specific dollar amount on the mental suffering damages [2]. The assessment of reasonable mental suffering damages is very subjective and unpredictable, and it will cause many serious problems
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