Using interpretable boosting algorithms for modeling environmental and agricultural data

no code implementations • 4 May 2023 • Fabian Obster, Christian Heumann, Heidi Bohle, Paul Pechan

We describe how interpretable boosting algorithms based on ridge-regularized generalized linear models can be used to analyze high-dimensional environmental data.

Factors other than climate change are currently more important in predicting how well fruit farms are doing financially

no code implementations • 11 Jan 2023 • Fabian Obster, Heidi Bohle, Paul M. Pechan

Machine learning and statistical modeling methods were used to analyze the impact of climate change on financial wellbeing of fruit farmers in Tunisia and Chile.