One of the important types of data in several applications is censored survival data processed in the framework of survival analysis [1, 2]. This type of data can be found in applications where objects are characterized by times to some events of interest, for example, by times to failure in reliability, times to recovery or times to death in medicine, times to bankruptcy of a bank or times to an economic crisis in economics. The important peculiarity of survival data is that the corresponding event does not necessarily occur during its observation period. In this case, we say about the so-called censored or right-censored data [3]. There are many machine learning models dealing with survival data, including models based on applying and extending the Cox proportional hazard model [4], for example, models presented in [5, 6], models based on a survival modification of random forests and called random survival forests (RSF) [7, 8, 9, 10, 11], models extending the neural networks [6, 12, 13, 14]. These models have gained considerable attention for their ability to analyze time-to-event data and to predict survival outcomes accurately. However, most models are perceived as black boxes, lacking interpretability.