Decision Tree Python Packages

connected-components-3d

Connected components on discrete and continuous multilabel 3D & 2D images. Handles 26, 18, and 6 connected variants; periodic boundaries (4, 8, & 6)

pyxai

PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).

mljar-supervised

Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation

dectree

A tool to explore and visualize decision trees

obliquetree

obliquetree is an advanced decision tree implementation featuring oblique and axis-aligned splits, optimized performance.

timbertrek

Explore and compare 1K+ accurate decision trees in your browser!

classicml

An easy-to-use ML framework

forestfire-ml

Machine learning from the ground up in Rust

temper-skills

Test suites + deterministic Python for your agent skills' decision logic. Adversarial persona reviewers write the tests; the generated code must keep passing them — zero LLM calls at inference. Try: uvx temper-skills audit <skill.md>

pyxai-experimental

PyXAI (Python eXplainable AI) is a Python library (version 3.6 or later) allowing to bring formal explanations suited to (regression or classification) tree-based ML models (Decision Trees, Random Forests, Boosted Trees, ...).

classicml-python

An easy-to-use ML framework

decision-tree-morfist

Multi-target Random Forest implementation that can mix both classification and regression tasks.

grande

(ICLR 2024) GRANDE: Gradient-Based Decision Tree Ensembles

metasklearn

MetaSklearn: A Metaheuristic-Powered Hyperparameter Optimization Framework for Scikit-Learn Models.

scikit-obliquetree

Oblique Decision Tree in Python

density-forest

Density Forests for Uncertainty, SIE Master Project, EPFL, Spring Semester 2018

classic-id3-decisiontree

ID3 is a Machine Learning Decision Tree Classification Algorithm that uses two methods to build the model. The two methods are Information Gain and Gini Index.

pymep

Python Math Expression Parser

machlearn

A Simple Yet Powerful Machine Learning Python Library

tree-decode

Package for removing the black-box around decision trees

get-oblique

No description available

classicml-lite

An easy-to-use ML framework

stackboost

Stacked decision trees algorithm

treeprompt

Tree prompting: easy-to-use scikit-learn interface for improved prompting.