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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Bibliographic Details
Published in:	Scientific reports Vol. 13; no. 1; pp. 12767 - 16
Main Authors:	Obster, Fabian, Heumann, Christian, Bohle, Heidi, Pechan, Paul
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 07.08.2023 Nature Publishing Group Nature Portfolio
Subjects:	631/114 631/158 631/158/2445 704/106 704/172/4081 704/4111 704/844 Agricultural economics Agriculture Algorithms Altitude Climate change Crop damage Drought Environmental hazards Farmers Farms Generalized linear models Human subjects Humanities and Social Sciences multidisciplinary Science Science (multidisciplinary) Sparsity Variables
ISSN:	2045-2322, 2045-2322
Online Access:	Get full text
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