An Efficient Model-Agnostic Approach for Uncertainty Estimation in Data-Restricted Pedometric Applications
This addresses data scarcity in pedometrics for soil scientists, but it is incremental as it adapts existing methods to a new domain.
The paper tackles uncertainty estimation in soil property prediction by transforming regression into classification, showing potential for better uncertainty estimates than common pedometric models in data-scarce scenarios.
This paper introduces a model-agnostic approach designed to enhance uncertainty estimation in the predictive modeling of soil properties, a crucial factor for advancing pedometrics and the practice of digital soil mapping. For addressing the typical challenge of data scarcity in soil studies, we present an improved technique for uncertainty estimation. This method is based on the transformation of regression tasks into classification problems, which not only allows for the production of reliable uncertainty estimates but also enables the application of established machine learning algorithms with competitive performance that have not yet been utilized in pedometrics. Empirical results from datasets collected from two German agricultural fields showcase the practical application of the proposed methodology. Our results and findings suggest that the proposed approach has the potential to provide better uncertainty estimation than the models commonly used in pedometrics.