Model Explainability – SHAP vs. LIME vs. Permutation Feature Importance

Author(s): Lan Chu

Originally published on Towards AI.

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Interpreting complex models helps us understand how and why a model reaches a decision and which features were important in reaching that conclusion, which will aid in overcoming the trust and ethical concerns of using machine learning in making decisions. Choosing the desired model interpretation will generally depend on the answer to three questions: (i) is the model simple enough to offer an intrinsic explanation, (ii) should the interpretable model be model-specific or model-agnostic? and (iii) do we desire local or global… Read the full blog for free on Medium.

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