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This article explains why post‑hoc interpretation methods such as PDP, ALE, LIME, and SHAP are essential for extracting insights from complex machine‑learning models, demonstrates their mathematical foundations, discusses limitations, and provides a complete Python example using XGBoost on a housing‑price dataset.
This article introduces the importance of model interpretability, reviews common explanation techniques such as model‑specific and model‑agnostic methods, global and local analyses, partial dependence plots, ICE, ALE, and tools like LIME and SHAP, and demonstrates their practical use in anti‑fraud and device‑classification scenarios within a financial‑technology context.
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This article provides a comprehensive survey of model interpretability in machine learning, covering its importance, evaluation criteria, and a wide range of techniques such as permutation importance, partial dependence plots, ICE, LIME, SHAP, RETAIN, and LRP, along with practical code examples and visualizations.