SurvBeX: An explanation method of the machine learning survival models
  based on the Beran estimator
Lev V. Utkin Danila Y. Eremenko Andrei V. Konstantinov
An explanation method called SurvBeX is proposed to interpret predictions of
the machine learning survival black-box models. The main idea behind the method
is to use the modified Beran estimator as the surrogate explanation model.
Coefficients, incorporated into Beran estimator, can be regarded as values of
the feature impacts on the black-box model prediction. Following the well-known
LIME method, many points are generated in a local area around an example of
interest. For every generated example, the survival function of the black-box
model is computed, and the survival function of the surrogate model (the Beran
estimator) is constructed as a function of the explanation coefficients. In
order to find the explanation coefficients, it is proposed to minimize the mean
distance between the survival functions of the black-box model and the Beran
estimator produced by the generated examples. Many numerical experiments with
synthetic and real survival data demonstrate the SurvBeX efficiency and compare
the method with the well-known method SurvLIME. The method is also compared
with the method SurvSHAP. The code implementing SurvBeX is available at:
https://github.com/DanilaEremenko/SurvBeX