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

提出了一种名为SurvBeX的解释方法，用于解释机器学习生存黑盒模型的预测。该方法的主要思想是使用修改后的Beran估计器作为替代解释模型，并通过最小化黑盒模型与Beran估计器之间的平均距离来找到解释系数。通过使用合成和真实生存数据进行的多个数值实验表明SurvBeX的效率，并与SurvLIME和SurvSHAP进行了比较。

SurvBeX：基于Beran估计的机器学习生存模型的解释方法