Recently, I came across the paper Robust and Stable Black Box Explanations , which discusses a nice framework for global model-agnostic explanations. I was thinking to recreate the experiments performed in the paper, but, unfortunately, the authors haven't provided the code. The summary of the experiments are: use LIME, SHAP and MUSE as baseline models, and compute fidelity score on test data. (All the 3 datasets are used for classification...
Originally asked by user294142 on Stack Exchange (ai) (CC BY-SA 4.0).
