Contents
What is Shap Shapley additive exPlanations?
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations).
What is kernel Shap?
Kernel SHAP is a method that uses the LIME framework to compute Shapley Values. Setting the loss function, weighting kernel and regularization terms appropriately in the LIME framework allows theoretically obtaining Shapley Values more efficiently than directly computing Shapley Values.
What is kernel explainer?
Uses the Kernel SHAP method to explain the output of any function. Kernel SHAP is a method that uses a special weighted linear regression to compute the importance of each feature. The computed importance values are Shapley values from game theory and also coefficents from a local linear regression.
Is the Shap method based on optimal Shapley values?
This chapter is currently only available in this web version. ebook and print will follow. SHAP (SHapley Additive exPlanations) by Lundberg and Lee (2016) 48 is a method to explain individual predictions. SHAP is based on the game theoretically optimal Shapley Values.
Why is kernelshap not an interpretable interpretation method?
Sampling from the marginal distribution means ignoring the dependence structure between present and absent features. KernelSHAP therefore suffers from the same problem as all permutation-based interpretation methods. The estimation puts too much weight on unlikely instances. Results can become unreliable.
Which is the best way to estimate Shapley values?
First, the SHAP authors proposed KernelSHAP, an alternative, kernel-based estimation approach for Shapley values inspired by local surrogate models. And they proposed TreeSHAP, an efficient estimation approach for tree-based models.
How to calculate the importance of a Shap feature?
The idea behind SHAP feature importance is simple: Features with large absolute Shapley values are important. Since we want the global importance, we sum the absolute Shapley values per feature across the data: I j = n ∑ i=1|ϕ(i) j | I j = ∑ i = 1 n | ϕ j (i) | Next, we sort the features by decreasing importance and plot them.