Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks

TL;DR

Green Tsetlin (GT) is a user-friendly, production-ready framework for Tsetlin Machines that simplifies training and inference, supporting both research and practical applications with efficient performance and comprehensive features.

Contribution

GT introduces a practical, easy-to-use Tsetlin Machine framework with clear separation of training and inference, supporting hyper-parameter tuning and model export for real-world use.

Findings

Competitive training and inference performance

Supports hyper-parameter search and cross-validation

Provides a production-ready, accessible TM implementation

Abstract

Green Tsetlin (GT) is a Tsetlin Machine (TM) framework developed to solve real-world problems using TMs. Several frameworks already exist that provide access to TM implementations. However, these either lack features or have a research-first focus. GT is an easy-to-use framework that aims to lower the complexity and provide a production-ready TM implementation that is great for experienced practitioners and beginners. To this end, GT establishes a clear separation between training and inference. A C++ backend with a Python interface provides competitive training and inference performance, with the option of running in pure Python. It also integrates support for critical components such as exporting trained models, hyper-parameter search, and cross-validation out-of-the-box.