Explainability Methods
Explore the main explainability methods used in machine learning to understand model behavior and mitigate risks. Learn about self-explainable models, global explanations such as SHAP and surrogate models, and local methods including LIME and counterfactual analysis. This lesson provides practical insights on interpreting black box models to enhance transparency and trustworthiness in ML pipelines.
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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 ...