Interpretable Machine Learning for TabPFN

arXiv:2403.10923v1 Announce Type: new
Abstract: The recently developed Prior-Data Fitted Networks (PFNs) have shown very promising results for applications in low-data regimes. The TabPFN model, a special case of PFNs for tabular data, is able to achieve state-of-the-art performance on a variety of classification tasks while producing posterior predictive distributions in mere seconds by in-context learning without the need for learning parameters or hyperparameter tuning. This makes TabPFN a very attractive option for a wide range of domain applications. However, …

arxiv cs.ai cs.lg machine machine learning stat.co stat.ml type