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Tabular Classification
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pipeline_tag: tabular-classification

ShapPFN

ShapPFN is a foundation model for tabular data that integrates Shapley value regression directly into its architecture, producing both predictions and explanations in a single forward pass. On standard benchmarks, ShapPFN achieves competitive performance while producing high-fidelity explanations ($R^2 \approx 0.96$, cosine $\approx 0.99$) over 1000x faster than KernelSHAP.

Architecture

ShapPFN combines several key components:

  • PFN-style tabular transformers: Uses alternating attention over rows and features (similar to TabPFN / nanoTabPFN).
  • Additive decomposition: Separate decoder heads for base and per-feature contributions.
  • Shapley value regression: Training explanations as part of the model output (ViaSHAP-style) rather than as a post-hoc procedure.

Setup

To use the code and the model from the official repository, you can install it via:

pip install -e .

Quickstart

The official repository provides scripts for various tasks:

  • Data Generation: bash scripts/generate_data.sh
  • Training: bash scripts/train_shappfn.sh
  • Evaluation: bash scripts/eval_openml.sh (OpenML suite) and bash scripts/eval_shap.sh (KernelExplainer comparison)