Articles | Volume 12, issue 1
Regular research article
27 Jan 2023
Regular research article |  | 27 Jan 2023

Influence of measurement uncertainty on machine learning results demonstrated for a smart gas sensor

Tanja Dorst, Tizian Schneider, Sascha Eichstädt, and Andreas Schütze


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Short summary
A fundamental problem of machine learning (ML) is measurement uncertainty and the influence on ML results. Measurement uncertainty, which is critical in hazardous gas detection, is directly addressed in this paper. A previously published toolbox is extended for regression. One of the benefits of this approach is obtaining a better understanding of where the overall system should be improved. This can be achieved by either improving the trained ML model or using a sensor with higher precision.