Filling gaps in data sets or identifying outliers—that's the domain of the machine learning algorithm TabPFN, developed by a team led by Prof. Dr. Frank Hutter from the University of Freiburg ...

Machine learning can be supervised, unsupervised, or semi-supervised. In supervised learning, models are trained on labeled data, meaning the input data is paired with the correct output.

Journal reference: Belov V, Erwin-Grabner T, Aghajani M, et al. (2024) Multi-site benchmark classification of major depressive disorder using machine learning on cortical and subcortical measures.