Using interpretable boosting algorithms for modeling environmental and agricultural data

We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data. We illustrate this by using environmental, social, human and biophysical data to predict the financial vulnerability of farmers in Chile...

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Detalles Bibliográficos
Autores principales: Obster, Fabian, Heumann, Christian, Bohle, Heidi, Pechan, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406907/
https://www.ncbi.nlm.nih.gov/pubmed/37550426
http://dx.doi.org/10.1038/s41598-023-39918-5

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10406907/
https://www.ncbi.nlm.nih.gov/pubmed/37550426
http://dx.doi.org/10.1038/s41598-023-39918-5

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