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Model interpretability and explainability: Data Science course | Zoonk

44. Model interpretability and explainability

Covers permutation importance, partial dependence, SHAP-style explanations, surrogate models, and explanation limits. Learners communicate why a model made predictions and where those explanations may fail.

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