A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

A Perspective on Explainable Artificial Intelligence Methods: SHAP and LIME

eXplainable artificial intelligence (XAI) methods have emerged to convert the black box of machine learning (ML) models into a more digestible form. These methods help to communicate how the model works with the aim of making ML models more transparent and increasing the trust of end‐users in their...

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Bibliographic Details
Main Authors: Ahmed M. Salih, Zahra Raisi‐Estabragh, Ilaria Boscolo Galazzo, Petia Radeva, Steffen E. Petersen, Karim Lekadir, Gloria Menegaz
Format: Article
Language:English
Published: Wiley 2025-01-01
Series:Advanced Intelligent Systems
Subjects:
collinearity
interpretability
Local Interpretable Model Agnostic Explanation
SHapley Additive exPlanations
eXplainable artificial intelligence
Online Access:https://doi.org/10.1002/aisy.202400304
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https://doi.org/10.1002/aisy.202400304

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