Pierre Ramond

Machine learning predictions for microbial eukaryotic plankton: implications from unevenly structured data

Christian Marchese, María Laura Zoffoli, Pierre Ramond, Timotej Turk Dermastia, Tinkara Tinta, Ramiro Logares, et al.

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…Read more