2023 review

Compositionality, sparsity, spurious heterogeneity, and other data-driven challenges for machine learning algorithms within plant microbiome studies


By: S. Busato n, M. Gordon n, M. Chaudhari n, I. Jensen*, T. Akyol*, S. Andersen*, C. Williams n

author keywords: Machine learning; Deep learning; Plant-associated microbiome; Compositional data analysis
MeSH headings : Microbiota; Algorithms; Machine Learning; Plants
TL;DR: The analysis is expanded to other fields to quantify the degree to which mitigation approaches improve the performance of ML and describe the mathematical basis for this. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: January 30, 2023

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