Using interpretable boosting algorithms for modeling environmental and
agricultural data
- URL: http://arxiv.org/abs/2305.02699v1
- Date: Thu, 4 May 2023 10:16:11 GMT
- Title: Using interpretable boosting algorithms for modeling environmental and
agricultural data
- Authors: Fabian Obster, Christian Heumann, Heidi Bohle, Paul Pechan
- Abstract summary: We describe how interpretable boosting algorithms can be used to analyze high-dimensional environmental data.
We show how group structures can be considered and how interactions can be found in high-dimensional datasets using a novel 2-step boosting approach.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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 and Tunisia
against climate hazards. We show how group structures can be considered and how
interactions can be found in high-dimensional datasets using a novel 2-step
boosting approach. The advantages and efficacy of the proposed method are shown
and discussed. Results indicate that the presence of interaction effects only
improves predictive power when included in two-step boosting. The most
important variable in predicting all types of vulnerabilities are natural
assets. Other important variables are the type of irrigation, economic assets
and the presence of crop damage of near farms.
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