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Counterfactual explanation of machine learning survival models
A method for counterfactual explanation of machine learning survival models is proposed.
A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds
As a result, the robust maximin strategy is used, which aims to minimize the average distance between cumulative hazard functions of the explained black-box model and of the approximating Cox model, and to maximize the distance over all cumulative hazard functions in the interval produced by the Kolmogorov-Smirnov bounds.
SurvLIME-Inf: A simplified modification of SurvLIME for explanation of machine learning survival models
A new modification of the explanation method SurvLIME called SurvLIME-Inf for explaining machine learning survival models is proposed.
SurvLIME: A method for explaining machine learning survival models
A new method called SurvLIME for explaining machine learning survival models is proposed.
An explanation method for Siamese neural networks
A new method for explaining the Siamese neural network is proposed.
