cair/pyTsetlinMachine

Implements the Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, Weighted Tsetlin Machine, and Embedding Tsetlin Machine, with support for continuous features, multigranularity, clause indexing, and literal budget

46
/ 100
Emerging

This tool helps researchers and data scientists build clear and interpretable classification and regression models. You input numerical or categorical data, and it outputs predictions along with logical rules that explain how those predictions were made. It's designed for anyone who needs to understand the 'why' behind their model's decisions, not just the 'what'.

149 stars. No commits in the last 6 months.

Use this if you need transparent, explainable AI models for tasks like image recognition, text classification, or predicting outcomes from tabular data.

Not ideal if you prioritize raw predictive power over model interpretability, or if you are working with extremely large, high-dimensional datasets where speed is the absolute top priority.

interpretable-AI pattern-recognition classification regression-modeling explainable-AI
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 10 / 25
Maturity 16 / 25
Community 20 / 25

How are scores calculated?

Stars

149

Forks

32

Language

C

License

MIT

Last pushed

Apr 01, 2025

Commits (30d)

0

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