Contents
What do Shapley values mean?
The Shapley value is a solution concept used in game theory that involves fairly distributing both gains and costs to several actors working in coalition. The Shapley value ensures each actor gains as much or more as they would have from acting independently.
What is a shap?
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).
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.
What should I know about Shapley values in Python?
This is a living document, and serves as an introduction to the shap Python package. So if you have feedback or contributions please open an issue or pull request to make this tutorial better! Before using Shapley values to explain complicated models, it is helpful to understand how they work for simple models.
What are the fundemental properties of Shapley values?
One the fundemental properties of Shapley values is that they always sum up to the difference between the game outcome when all players are present and the game outcome when no players are present.
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.