Decision Trees Python Packages

lightgbm

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

catboost

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

tensorflow-decision-forests

A collection of state-of-the-art algorithms for the training, serving and interpretation of Decision Forest models in Keras.

ydf

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

orange3

🍊 :bar_chart: :bulb: Orange: Interactive data analysis

dtreeviz

A python library for decision tree visualization and model interpretation.

catboost-dev

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

linear-tree

A python library to build Model Trees with Linear Models at the leaves.

lleaves

Compiler for LightGBM gradient-boosted trees, based on LLVM. Speeds up prediction by ≥10x.

adaxt

adaXT: tree-based machine learning in Python

rgf-python

Home repository for the Regularized Greedy Forest (RGF) library. It includes original implementation from the paper and multithreaded one written in C++, along with various language-specific wrappers.

stochtree

Stochastic tree ensembles (BART / XBART) for supervised learning and causal inference

pynets

A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

gosdt

Generalized Optimal Sparse Decision Trees

ydf-tf

A library to train, evaluate, interpret, and productionize decision forest models such as Random Forest and Gradient Boosted Decision Trees.

fukuml

Simple machine learning library / 簡單易用的機器學習套件

supertree

Visualize decision trees in Python

chefboost

A Lightweight Decision Tree Framework supporting regular algorithms: ID3, C4.5, CART, CHAID and Regression Trees; some advanced techniques: Gradient Boosting, Random Forest and Adaboost w/categorical features support for Python

pyscipopt-ml

Python interface to automatically formulate Machine Learning models into Mixed-Integer Programs

spkit

Signal Processing Toolkit, including ML models with visualization

dectree

A tool to explore and visualize decision trees

neural-trees

sklearn-compatible PyTorch implementations of Soft Decision Trees, HMoE, and classifier comparison tests (5×2cv F-test). pip install neural-trees

grande

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

timber-compiler

Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference.