Comparing SHAP with LIME
As you will have noticed by now, both SHAP and LIME have limitations, but they also have strengths. SHAP is grounded in game theory and approximate Shapley values, so its SHAP values mean something. These have great properties such as additivity, efficiency, and substitutability that make it consistent but violate the dummy property. It always adds up and doesn't need parameter tuning to accomplish this. However, it's more suited for global interpretations, and one of its most model-agnostic explainers, KernelExplainer, is painfully slow. KernelExplainer also deals with missing values by using random ones, which can put too much weight on unlikely observations.
LIME is speedy, very model-agnostic, and adaptable to all kinds of data. However, it's not grounded on strict and consistent principles but has the intuition that neighbors are alike. Because of this, it can require tricky parameter tuning to define the neighborhood size optimally,...
