Shapley variable importance cloud for interpretable machine learning

Interpretable machine learning has been focusing on explaining final models that optimize performance. The state-of-the-art Shapley additive explanations (SHAP) locally explains the variable impact on individual predictions and has recently been extended to provide global assessments across the data...

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Detalles Bibliográficos
Autores principales: Ning, Yilin, Ong, Marcus Eng Hock, Chakraborty, Bibhas, Goldstein, Benjamin Alan, Ting, Daniel Shu Wei, Vaughan, Roger, Liu, Nan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023900/
https://www.ncbi.nlm.nih.gov/pubmed/35465224
http://dx.doi.org/10.1016/j.patter.2022.100452

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9023900/
https://www.ncbi.nlm.nih.gov/pubmed/35465224
http://dx.doi.org/10.1016/j.patter.2022.100452

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