Shapelinear

Shapelinear is a term encountered in discussions of model interpretability that refers to the use of Shapley-based explanations in linear or locally linear models. The concept builds on SHAP values, which attribute a model’s prediction to its input features in a way that is additive and locally accurate.

Definition and scope: Shapelinear covers methods that either apply SHAP to models that are linear in their inputs, such as linear regression, logistic regression, or generalized linear models, or approximate a nonlinear model by a local linear surrogate for the purpose of explanation. In both cases, the aim is to produce straightforward, additive attributions for each feature.

Computation: For a strictly linear model, SHAP values can be computed in closed form, enabling exact attributions. In practice, tools like LinearExplainer in the SHAP library implement these computations efficiently, exploiting linearity to avoid sampling-based methods used for more complex models.

Interpretation and limitations: SHAP values provide consistent and locally accurate attributions, allowing practitioners to understand how features contribute to a prediction. However, shapelinear explanations for nonlinear models rely on local linearization and may not capture intricate interactions or nonlinear effects; results can be sensitive to feature preprocessing and encoding.

Usage and relevance: Shapelinear is commonly used in model debugging, feature attribution communication, and contexts requiring transparent explanations for regulatory or stakeholder purposes.

See also: SHAP, LinearExplainer, Shapley values, model interpretability.