LinearSHAP

LinearSHAP is a method used in machine learning to explain the predictions of linear models, particularly those based on Shapley values, a concept from cooperative game theory. Shapley values provide a way to fairly distribute the contribution of each feature to the model’s output by considering all possible feature subsets. In traditional Shapley value calculations, the complexity grows factorially with the number of features, making it impractical for models with many features.

LinearSHAP addresses this challenge by leveraging the linearity of the model. For a linear model, the Shapley value for each feature can be computed analytically without exhaustive subset enumeration. This approach simplifies the calculation while preserving the interpretability and fairness of Shapley values. The method assumes that the model’s output is a linear combination of feature contributions, which is inherently true for linear models but can also be approximated for certain non-linear models.

The key advantage of LinearSHAP is its computational efficiency, especially for high-dimensional datasets. It provides a clear, additive breakdown of how each feature influences the model’s prediction, making it easier to interpret compared to other explanation methods like partial dependence plots or permutation importance. However, it is limited to linear models or cases where linearity can be approximated, restricting its applicability to more complex architectures.

LinearSHAP is particularly useful in domains where interpretability is critical, such as healthcare, finance, or regulatory compliance, where understanding feature contributions is essential for trust and decision-making. By offering a straightforward and efficient way to compute Shapley values, it bridges the gap between model complexity and explainability.