'Proving Kernelshap is trustful
Am working on shap implementation and am trying to prove that kernelshap is trustful , the main concern is that since kernelshap is fitting a linear model to determine shap values there will be some sort of uncertainty since it's an approximation . I tried to look for some metrics but what we have in the library https://github.com/slundberg/shap/blob/master/shap/benchmark/metrics.py are metrics dedicated for the existing methods (benchmark) . I tried comparing shap values of treexplainer since it's an exact calculation for the same data and I assumed at least to find that most important features are the same when calculating with kernelshap but am not getting comparable results .
these are shap values for Boston housing dataset with treexplainer (model : XGBRegressor) :
These are shap values for Boston housing dataset with Kernelshap (model : SVR) :
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