Predicting interpretability of metabolome models based on behavior, putative identity, and biological relevance of explanatory signals.

Abstract excerpt

Powerful algorithms are required to deal with the dimensionality of metabolomics data. Although many achieve high classification accuracy, the models they generate have limited value unless it can be demonstrated that they are reproducible and statistically relevant to the biological problem under investigation. Random forest (RF) generates models, without any requirement for dimensionality reduction or feature...

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