scikit-tree

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).

References

[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.

Contents

Getting started:

• API Documentation
  • Scikit-learn Tree Estimators
  • Supervised
  • Unsupervised
  • Outlier Detection
  • Distance Metrics
  • Statistical Hypothesis Testing
  • Datasets
  • Experimental Functionality
• User Guide
  • 1. Supervised Decision Trees
  • 2. Unsupervised Decision Trees
  • 3. Oblique Random Forests
  • 4. Feature importance evaluation
• Release History
  • Version 0.1
  • Version 0.2
  • Version 0.3
  • Version 0.4
  • Version 0.5
  • Version 0.6
  • Version 0.7
  • Version 0.8 (Unreleased)
• Installation
  • Dependencies
  • Installing with pip
  • Installing from source with Meson
  • Conda (Recommended)
• Examples using scikit-tree
• Examples
  • Calibrated decision trees via honesty
  • Hypothesis testing with decision trees
  • Multi-view learning with Decision-trees
  • Outlier-detection
  • Quantile Predictions with Random Forest
  • Comparing sklearn and sktree decision trees
  • Sparse oblique projections with oblique decision-trees
  • Decision-tree splitters
  • Treeple for Hypothesis Testing

Indices and tables

• Index

• Module Index