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SurvSHAP(t): Time-dependent explanations of machine learning survival models. (arXiv:2208.11080v2 [cs.LG] UPDATED)
Sept. 8, 2022, 1:12 a.m. | Mateusz Krzyziński, Mikołaj Spytek, Hubert Baniecki, Przemysław Biecek
cs.LG updates on arXiv.org arxiv.org
Machine and deep learning survival models demonstrate similar or even
improved time-to-event prediction capabilities compared to classical
statistical learning methods yet are too complex to be interpreted by humans.
Several model-agnostic explanations are available to overcome this issue;
however, none directly explain the survival function prediction. In this paper,
we introduce SurvSHAP(t), the first time-dependent explanation that allows for
interpreting survival black-box models. It is based on SHapley Additive
exPlanations with solid theoretical foundations and a broad adoption among
machine …
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