Assessing the utility of machine learning for predicting food sufficiency: a case study in Malawi
This study explores the potential of applying machine learning (ML) methods to identify and predict areas at risk of food insufficiency using a parsimonious set of publicly available data sources. We combine household survey data that captures monthly reported food insufficiency with remotely sensed...
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Main Authors: | Andrew Tomes, Shahrzad Gholami, Didier Alia, Conor Hennessy, Dafeng Xu, Cecilia Bitz, Rahul Dodhia, Juan Lavista Ferres, C. Leigh Anderson |
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Format: | Article |
Language: | English |
Published: |
Cambridge University Press
2025-01-01
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Series: | Data & Policy |
Subjects: | |
Online Access: | https://www.cambridge.org/core/product/identifier/S2632324925100138/type/journal_article |
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