Comparison with Alternative Methods

Method	Computational cost	Handles correlated predictors well	Fair attribution
Regression coefficients	Very low	No	Limited
Standardized coefficients	Very low	No	Limited
Correlation analysis	Very low	No	Limited
Sequential \(R^2\) decomposition	Low	Partially	Order dependent
Permutation importance	Moderate	Partially	Moderate
Shapley Value Regression	High	Yes	Strong

Shapley Value Regression is often preferred when understanding variable contribution is more important than computational efficiency.

When Alternative Methods May Be Preferable

Alternative methods may be more appropriate when:

• The dataset contains a large number of predictors.

• Computational resources are limited.

• Approximate importance estimates are sufficient.

• Real-time model interpretation is required.

In such cases, methods such as permutation importance, approximate SHAP algorithms, or other scalable feature attribution techniques may be more practical.

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