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Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data
Christian Marchese, Maria Laura Zoffoli, Pierre Ramond, Timotej Turk Dermastia, Tinkara Tinta, Ramiro Logares, Pierre E. Galand, Emanuele Organelli
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Oznake: strojno učenje;metabarkodiranje;fitoplankton;radiometrija;modeliranje;cross-validation;data leakage;eukaryotic plankton;machine learning;marine biodiversity;Mediterranean Sea;omics;shannon diversity index;
Machine learning models provide a scalable approach for predicting the diversity of eukaryotic microbial plankton from environmental predictors. However, the extent to which these models generalize to data outside the training set remains poorly quantified. In this study, XGBoost was used to predict ...
Leto: 2026 Vir: Nacionalni inštitut za biologijo (NIB)
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