Demystifying Bayesian Models: Unveiling Explanability through SHAP Values

Exploring PyMC’s Insights with SHAP Framework via an Engaging Toy Example

The Gap between Bayesian Models and Explainability

SHAP values (SHapley Additive exPlanations) are a game-theory-based method used to increase the transparency and interpretability of machine learning models. However, this method, along with other machine learning explainability frameworks, has rarely been applied to Bayesian models, which provide a posterior distribution capturing uncertainty in parameter estimates instead of point estimates used by classical machine learning models.

While Bayesian models offer a …

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