Scikit-tree is a package for modern tree-based algorithms for supervised and unsupervised learning problems. It extends the robust API of scikit-learn for tree algorithms that achieve strong performance in benchmark tasks.
Our package has implemented unsupervised forests (Geodesic Forests [Madhyastha2020]), oblique random forests (SPORF [Tomita2020] and MORF [Li2023]), and honest forests [Perry2021]. In the near future, we also plan to include extended isolation forests and stream decision forests [Xu2022].
We encourage you to use the package for your research and also build on top with relevant Pull Requests. See our examples for walk-throughs of how to use the package. Also, see our contributing guide.
We are licensed under BSD-3 (see License).
- API Documentation
- User Guide
- Release History
- Examples using scikit-tree