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A new modification of the explanation method SurvLIME called SurvLIME-Inf forexplaining machine learning survival models is proposed. The basic idea behindSurvLIME as well as SurvLIME-Inf is to apply the Cox proportional hazards modelto approximate the black-box survival model at the local area around a testexample. The Cox model is used due to the linear relationship of covariates. Incontrast to SurvLIME, the proposed modification uses $L_{\infty }$-norm fordefining distances between approximating and approximated cumulative hazardfunctions. This leads to a simple linear programming problem for determiningimportant features and for explaining the black-box model prediction. Moreover,SurvLIME-Inf outperforms SurvLIME when the training set is very small.Numerical experiments with synthetic and real datasets demonstrate theSurvLIME-Inf efficiency.

SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models