treeple#

treeple 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], manifold random forests, MORF [Li2023]), honest forests [Perry2021], extended isolation forests [Hariri2019], and more.

For all forests, we also support incremental building of the forests, using the partial_fit API from scikit-learn [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 PolyForm Noncommercial License (see License).

References

[Hariri2019]

Hariri, Sahand, Matias Carrasco Kind, and Robert J. Brunner. “Extended isolation forest.” IEEE transactions on knowledge and data engineering 33.4 (2019): 1479-1489.

[Madhyastha2020]

Madhyastha, Meghana, et al. “Geodesic Forests”, KDD 2020, 513-523, 2020.

[Tomita2020]

Tomita, Tyler M., et al. “Sparse Projection Oblique Randomer Forests”, The Journal of Machine Learning Research, 21(104), 1-39, 2020.

[Li2023]

Li, Adam, et al. “Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks”, SIAM Journal on Mathematics of Data Science, 5(1), 77-96, 2023.

[Perry2021]

Perry, Ronan, et al. “Random Forests for Adaptive Nearest Neighbor Estimation of Information-Theoretic Quantities”, arXiv preprint arXiv:1907.00325, 2021.

[Xu2022]

Xu, Haoyin, et al. “Simplest Streaming Trees”, arXiv preprint arXiv:2110.08483, 2022.