SurvBeX: an explanation method of the machine learning survival models based on the Beran estimator

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 a 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 predicted, and the survival function of the surrogate model (the Beran estimator) is constructed as a function of the explanation coefficients. To find the explanation coefficients, it is proposed to minimize the mean distance between survival functions of the black-box model and the Beran estimator predicted for generated examples. Many numerical experiments with synthetic and real survival data demonstrate the SurvBeX efficiency and compare the method with explanation methods SurvLIME, SurvNAM and SurvSHAP. The code implementing SurvBeX is publicly available.