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.

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).

Contents

Getting started:

• API Documentation
  • Supervised
  • Unsupervised
  • Distance Metrics
  • Experimental Functionality
• User Guide
  • 1. Supervised Decision Trees
  • 2. Unsupervised Decision Trees
  • 3. Oblique Random Forests
  • 4. Feature importance evaluation
• Release History
  • What’s new?
  • Version 0.1
• Installation
  • Dependencies
  • Installing with pip
  • Installing from source with Meson
  • Conda (Recommended)
• Examples using scikit-tree
  • Examples

Indices and tables

• Index

• Module Index