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

SurvBeNIM: The Beran-Based Neural Importance Model for Explaining the Survival Models