Interpretable Machine Learning for TabPFN
David Rundel Julius Kobialka Constantin von Crailsheim Matthias Feurer Thomas Nagler David Rügamer
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, a major
drawback of the method is its lack of interpretability. Therefore, we propose
several adaptations of popular interpretability methods that we specifically
design for TabPFN. By taking advantage of the unique properties of the model,
our adaptations allow for more efficient computations than existing
implementations. In particular, we show how in-context learning facilitates the
estimation of Shapley values by avoiding approximate retraining and enables the
use of Leave-One-Covariate-Out (LOCO) even when working with large-scale
Transformers. In addition, we demonstrate how data valuation methods can be
used to address scalability challenges of TabPFN. Our proposed methods are
implemented in a package tabpfn_iml and made available at
https://github.com/david-rundel/tabpfn_iml.