Get Started with Shapley Value Regression
To get started, you can use the ShapleyValue class from the package. Here’s a quick example using a built-in dataset:
Historical Background
Shapley Value Regression is rooted in cooperative game theory, introduced by Lloyd Shapley in 1953. It addresses a fairness question: when outcomes are created jointly, how should credit be shared? The Shapley value answers this by averaging each participant’s contribution across all possible coalitions.
Why Shapley Value Regression?
Shapley Value Regression provides an alternative perspective by allocating model explanatory power among predictors using principles from cooperative game theory. It is especially useful when predictors are correlated and share information, as traditional methods can be misleading in such cases.
The Shapley Value Approach
Shapley Value Regression treats each predictor as a participant in a cooperative game. The total explanatory power of the model is the reward that must be distributed among all predictors.
Comparison with Alternative Methods
Shapley Value Regression is often preferred when understanding variable contribution is more important than computational efficiency. It provides a fair allocation of explanatory power, especially when predictors are correlated and share information.
Summary
Shapley Value Regression does not replace traditional regression analysis. Instead, it provides an additional perspective on how explanatory power should be attributed among predictors.
While the method is computationally more expensive than coefficient-based approaches, it offers a principled and theoretically grounded solution to one of the most challenging problems in regression analysis:
How should explanatory power be fairly distributed when predictors share information?
For models with a manageable number of predictors, Shapley Value Regression can provide insights that are often difficult to obtain using traditional importance measures alone.