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], and quantile regression by storing the training samples in the leaves of the trees [Meinshausen2006] (Warning: high memory usage will occur in this setting since predicting quantiles stores the training data within the leaves of the tree).

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.

[Meinshausen2006]

Meinshausen, Nicolai, and Greg Ridgeway. “Quantile regression forests.” Journal of machine learning research 7.6 (2006). “Quantile regression forests.”

[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
  • Version 0.9
  • Version 0.10 (Unreleased)
• Installation
  • Dependencies
  • Installing with pip
  • Installing from source with Meson
  • Conda (Recommended)
• Examples using treeple
• Examples
  • Calibrated decision trees via honesty
  • Multi-view learning with Decision-trees
  • Outlier-detection
  • Quantile Predictions with Random Forest
  • Comparing sklearn and treeple decision trees
  • Sparse oblique projections with oblique decision-trees
  • Decision-tree splitters
  • Treeple for Hypothesis Testing

Indices and tables

• Index

• Module Index