Hierarchical confounder discovery in the experiment-machine learning cycle

The promise of machine learning (ML) to extract insights from high-dimensional datasets is tempered by confounding variables. It behooves scientists to determine if a model has extracted the desired information or instead fallen prey to bias. Due to features of natural phenomena and experimental des...

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Detalles Bibliográficos
Autores principales: Rogozhnikov, Alex, Ramkumar, Pavan, Bedi, Rishi, Kato, Saul, Escola, G. Sean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024009/
https://www.ncbi.nlm.nih.gov/pubmed/35465234
http://dx.doi.org/10.1016/j.patter.2022.100451

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9024009/
https://www.ncbi.nlm.nih.gov/pubmed/35465234
http://dx.doi.org/10.1016/j.patter.2022.100451

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