Tabular data is widely utilized in various fields, including scientific research, finance, and healthcare. Traditionally, machine learning models such as gradient-boosted decision trees have been preferred for analyzing tabular data due to their effectiveness in handling heterogeneous and structured datasets. Despite their popularity, these methods have notable limitations, particularly in terms of performance on unseen data distributions, transferring learned knowledge between datasets, and integration challenges with neural network-based models because of their non-differentiable nature. Researchers from the University of Freiburg, Berlin Institute of Health, Prior Labs, and ELLIS Institute have introduced a novel approach named Tabular Prior-data Fitted Network (TabPFN).…