Green Tsetlin Redefining Efficiency in Tsetlin Machine Frameworks
It addresses the need for an easy-to-use, feature-rich framework for practitioners and beginners in applying Tsetlin Machines to real-world problems, but it is incremental as it builds on existing TM frameworks.
Green Tsetlin is a Tsetlin Machine framework designed to lower complexity and provide a production-ready implementation, featuring a C++ backend with Python interface for competitive performance and built-in support for model export, hyper-parameter search, and cross-validation.
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.