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 tra...
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| Auteurs principaux: | , , , , , , , |
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| Format: | article |
| Langue: | EN |
| Publié: |
Public Library of Science (PLoS)
2021
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| Sujets: | |
| Accès en ligne: | https://doaj.org/article/e90e4f8fad114eea9a676e4cc99a8b9b |
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