SurvLIME: A method for explaining machine learning survival models
This addresses the need for interpretability in survival analysis models, which is crucial for domains like healthcare, but it is incremental as it builds directly on the existing LIME method.
The authors tackled the problem of explaining machine learning survival models by proposing SurvLIME, an extension of LIME that uses the Cox proportional hazards model and approximates cumulative hazard functions with perturbed points, resulting in a method reduced to solving an unconstrained convex optimization problem and demonstrated as efficient through numerous numerical experiments.
A new method called SurvLIME for explaining machine learning survival models is proposed. It can be viewed as an extension or modification of the well-known method LIME. The main idea behind the proposed method is to apply the Cox proportional hazards model to approximate the survival model at the local area around a test example. The Cox model is used because it considers a linear combination of the example covariates such that coefficients of the covariates can be regarded as quantitative impacts on the prediction. Another idea is to approximate cumulative hazard functions of the explained model and the Cox model by using a set of perturbed points in a local area around the point of interest. The method is reduced to solving an unconstrained convex optimization problem. A lot of numerical experiments demonstrate the SurvLIME efficiency.