shapemonotone
Shapemonotone is a term used in explainable AI to describe approaches that integrate monotonicity constraints into SHAP-based explanations. It refers to methods that aim to guarantee that the attribution of a feature to a prediction behaves monotonically with respect to the feature, for features known to have a monotone relationship with the target.
Background and motivation: SHAP provides local explanations of model predictions by assigning Shapley values to features,
Methods: Shapemonotone can be realized through several strategies. One route is to train or calibrate models
Applications and limitations: The approach is particularly relevant in regulated or high-stakes domains like finance, healthcare,
See also: SHAP, Shapley values, monotone constraints, interpretable machine learning, explainable AI.