Explainability Methods
Learn about explainable methods like SHAP and LIME.
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There are three broad categories of explainable AI: self-explainable, global, and local.
Self-explainable models
Self-explainable models consist of those algorithms that are inherently explainable (i.e., linear regressions, decision trees, etc.) just by looking at the architecture or formulae involved. Global and local models are more involved and represent only a fraction of the interpretability that self-explainable models provide.
Global explanations
Global explanations are other models constructed to approximate the model in question. They treat the model as a black box that can be repeatedly queried to construct another, more interpretable model that explains the black box.
Shapley Additive Explanations (SHAP)
SHapley Additive exPlanations (SHAP) is one example of global explanations. SHAP is a game theory approach ...