SurvBeX: An explanation method of the machine learning survival models based on the Beran estimator
This is an incremental improvement for researchers and practitioners in survival analysis, offering a new method to explain black-box model predictions in a specific domain.
The authors tackled the problem of interpreting predictions from machine learning survival models by proposing SurvBeX, an explanation method based on the Beran estimator, which demonstrated efficiency in numerical experiments with synthetic and real data, comparing favorably with existing methods like SurvLIME and SurvSHAP.
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