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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...
Autores principales: | Ning, Yilin, Ong, Marcus Eng Hock, Chakraborty, Bibhas, Goldstein, Benjamin Alan, Ting, Daniel Shu Wei, Vaughan, Roger, Liu, Nan |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2022
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Materias: | |
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 |
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