Opening the Random Forest Black Box of <sup>1</sup>H NMR Metabolomics Data by the Exploitation of Surrogate Variables

Opening the Random Forest Black Box of <sup>1</sup>H NMR Metabolomics Data by the Exploitation of Surrogate Variables

The untargeted metabolomics analysis of biological samples with nuclear magnetic resonance (NMR) provides highly complex data containing various signals from different molecules. To use these data for classification, e.g., in the context of food authentication, machine learning methods are used. The...

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Bibliographic Details
Main Authors: Soeren Wenck, Thorsten Mix, Markus Fischer, Thomas Hackl, Stephan Seifert
Format: Article
Language:English
Published: MDPI AG 2023-10-01
Series:Metabolites
Subjects:
classification
characterization
nuclear magnetic resonance spectroscopy
random forest
variable selection
variable relations
Online Access:https://www.mdpi.com/2218-1989/13/10/1075

Internet

https://www.mdpi.com/2218-1989/13/10/1075

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