shogun-toolbox/shogun

Health v2 · maintenance only

Adoption not part of the score

3079 stars · 1034 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Shogun is a C++ machine learning toolbox offering unified, efficient implementations of many ML algorithms with automatically generated bindings for Python, Java, Ruby, R, C#, Octave, Lua, and more. It has been developed since 1999 and is a NumFOCUS-affiliated project released under a BSD-3-Clause license.

Use cases

• train machine learning models from python using a fast c++ backend
• use the same ml library across multiple programming languages
• kernel methods and svm classification in c++
• run machine learning on linux macos and windows
• benchmark ml algorithms against a unified api

When to choose

• you need a mature multi-language ML library with a unified API
• you want efficient C++ implementations callable from Python, R, Java, or Ruby
• you work with kernel methods, SVMs, or classical ML algorithms

When to avoid

• you need deep learning or GPU-accelerated training
• you want a rapidly evolving ecosystem with frequent releases
• you need modern Python-native tooling and integration with the PyData stack

Facets

library · maturity maintenance

machine-learning data-science machine-learning data-science artificial-intelligence windows bsd python cpp cross-platform swig-bindings multi-language kernel-methods cmake scientific-computing linux macos

Member repositories

For agents

markdown · JSON · MCP: product_card(name="shogun-toolbox/shogun")