- post_hoc_ensembles: Improve performance with model combination
- interpretability: Explain TabPFN predictions with SHAP values and feature selection
- many_class: Handle classification with more classes than TabPFN's default limit
- classifier_as_regressor: Use TabPFN's classifier for regression tasks
- hpo: Automatic hyperparameter tuning for TabPFN
- rf_pfn: Combine TabPFN with decision trees and random forests
- unsupervised: Data generation and outlier detection
- embedding: Get TabPFNs internal dense sample embeddings
Detailed documentation for each extension is available in the respective module directories.
# Clone and install the repository
pip install "tabpfn-extensions[all] @ git+https://github.com/PriorLabs/tabpfn-extensions.git"TabPFN Extensions works with two TabPFN implementations:
-
🖥️ TabPFN Package - Full PyTorch implementation for local inference:
pip install tabpfn
-
☁️ TabPFN Client - Lightweight API client for cloud-based inference:
pip install tabpfn-client
Choose the backend that fits your needs - most extensions work with either option!
This project is licensed under the Apache License 2.0 - see the LICENSE file for details.
Interested in adding your own extension? We welcome contributions!
# Clone and set up for development
git clone https://github.com/PriorLabs/tabpfn-extensions.git
cd tabpfn-extensions
# Lightweight dev setup (fast)
pip install -e ".[dev]"
# Test your extension with fast mode
FAST_TEST_MODE=1 pytest tests/test_your_extension.py -vSee our Contribution Guide for more details.
Each extension lives in its own subpackage:
tabpfn-extensions/
├── src/
│ └── tabpfn_extensions/
│ └── your_package/ # Extension implementation
├── examples/
│ └── your_package/ # Usage examples
└── tests/
└── your_package/ # Tests
Built with ❤️ by the TabPFN community