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.

• Paper: Real-Time Explanations for Tabular Foundation Models
• GitHub Repository: kunumi/ShapPFN

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)