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Interpretable Machine Learning for TabPFN

David Rundel, Julius Kobialka, Constantin von Crailsheim, Matthias Feurer, Thomas Nagler, David Rügamer

TL;DR

The paper addresses the interpretability gap of TabPFN, a fast, in-context learning model for tabular data, by tailoring interpretable ML methods to its Transformer-based, context-driven inference. It introduces exact retraining enhancements for Kernel SHAP, LOCO feasible via in-context updates, and data-valued context optimization (Data Shapley), plus adaptations of ICE/PD/ALE and sensitivity analysis, all bundled in the tabpfn_iml toolbox. The approach yields reduced computational cost and improved attribution accuracy, enabling reliable local/global explanations, data-value assessments, and uncertainty tools for TabPFN in low-data regimes. This work has practical impact by making TabPFN more transparent and deployable across domains, while highlighting a path for interpretability in other in-context learning frameworks.

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, 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.

Interpretable Machine Learning for TabPFN

TL;DR

The paper addresses the interpretability gap of TabPFN, a fast, in-context learning model for tabular data, by tailoring interpretable ML methods to its Transformer-based, context-driven inference. It introduces exact retraining enhancements for Kernel SHAP, LOCO feasible via in-context updates, and data-valued context optimization (Data Shapley), plus adaptations of ICE/PD/ALE and sensitivity analysis, all bundled in the tabpfn_iml toolbox. The approach yields reduced computational cost and improved attribution accuracy, enabling reliable local/global explanations, data-value assessments, and uncertainty tools for TabPFN in low-data regimes. This work has practical impact by making TabPFN more transparent and deployable across domains, while highlighting a path for interpretability in other in-context learning frameworks.

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, 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.
Paper Structure (15 sections, 6 equations, 3 figures, 1 table)

This paper contains 15 sections, 6 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Our Contributions:
  3. Supervised Machine Learning & TabPFN
  4. Interpretable Machine Learning for TabPFN
  5. ICE, PD and ALE
  6. LOCO
  7. Kernel SHAP
  8. Data Valuation for Context Optimization
  9. Sensitivity Analysis
  10. Further Methods
  11. Numerical Experiments
  12. ICE & PD
  13. Kernel SHAP
  14. Data valuation for Context Optimization
  15. Discussion & Conclusion

Figures (3)

  • Figure 1: Runtime comparison of the implementations of tabpfn_iml PD vs. scikit-learn partial_dependence().
  • Figure 2: Average errors (points) and corresponding standard deviations (error bars) across all runs plotted against the token connections for various configurations and three datasets (different plots). Exact retraining configurations are red, while approximate retraining configurations are black.
  • Figure 3: Comparison of the ROC AUC of randomly selected TabPFN contexts (black) and contexts optimized through Data Shapley coupled with Kernel SHAP (red) across five runs and three datasets.