Multivariate random forest prediction of poverty and malnutrition prevalence

Advances in remote sensing and machine learning enable increasingly accurate, inexpensive, and timely estimation of poverty and malnutrition indicators to guide development and humanitarian agencies’ programming. However, state of the art models often rely on proprietary data and/or deep or transfer...

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Detalles Bibliográficos
Autores principales: Browne, Chris, Matteson, David S., McBride, Linden, Hu, Leiqiu, Liu, Yanyan, Sun, Ying, Wen, Jiaming, Barrett, Christopher B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425567/
https://www.ncbi.nlm.nih.gov/pubmed/34495951
http://dx.doi.org/10.1371/journal.pone.0255519

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8425567/
https://www.ncbi.nlm.nih.gov/pubmed/34495951
http://dx.doi.org/10.1371/journal.pone.0255519

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