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...
| Main Authors: | , , , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
Public Library of Science
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/142847 |
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