A new method called the Survival Beran-based Neural Importance Model (SurvBeNIM) is proposed. It aims to explain predictions of machine learning survival models, which are in the form of survival or cumulative hazard functions. The main idea behind SurvBeNIM is to extend the Beran estimator by incorporating the importance functions into its kernels and by implementing these importance functions as a set of neural networks which are jointly trained in an end-to-end manner. Two strategies of using and training the whole neural network implementing SurvBeNIM are proposed. The first one explains a single instance, and the neural network is trained for each explained instance. According to the second strategy, the neural network only learns once on all instances from the dataset and on all generated instances. Then the neural network is used to explain any instance in a dataset domain. Various numerical experiments compare the method with different existing explanation methods. A code implementing the proposed method is publicly available.

翻译：提出了一种名为生存Beran神经重要性模型（SurvBeNIM）的新方法。该方法旨在解释机器学习生存模型的预测结果，这些预测以生存函数或累积风险函数的形式呈现。SurvBeNIM的核心思想是通过将重要性函数融入Beran估计器的核函数中，并将这些重要性函数实现为一组以端到端方式联合训练的神经网络，从而对Beran估计器进行扩展。提出了两种使用和训练实现SurvBeNIM的完整神经网络的策略。第一种策略用于解释单个实例，针对每个被解释实例分别训练该神经网络。第二种策略中，神经网络仅在数据集中的所有实例及所有生成实例上进行一次性学习，随后用于解释数据集域内的任意实例。通过多种数值实验将该方法与现有不同解释方法进行了比较。实现所提方法的代码已公开。