Michael Mayer digs into Kernel SHAP:
In their 2017 paper on SHAP, Scott Lundberg and Su-In Lee presented Kernel SHAP, an algorithm to calculate SHAP values for any model with numeric predictions. Compared to Monte-Carlo sampling (e.g. implemented in R package “fastshap”), Kernel SHAP is much more efficient.
I had one problem with Kernel SHAP: I never really understood how it works!
Needless to say, Michael knows Kernel SHAP a lot better now, considering there’s now a kernelshap package for us.