# Uncertainty Quantification in the Tsetlin Machine

arXiv: https://arxiv.org/abs/2507.04175 · HTML: https://mlsanity.com/paper/2507.04175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 20 references that are in the map.
- First posted on arXiv 5 Jul 2025.

## Builds on (most cited first, 11 of 11)

- [The Tsetlin Machine -- A Game Theoretic Bandit Driven Approach to Optimal Pattern Recognition with Propositional Logic](https://mlsanity.com/paper/1804.01508/) (2018)
- [The Convolutional Tsetlin Machine](https://mlsanity.com/paper/1905.09688/) (2019)
- [On the Convergence of Tsetlin Machines for the IDENTITY- and NOT Operators](https://mlsanity.com/paper/2007.14268/) (2020)
- [Massively Parallel and Asynchronous Tsetlin Machine Architecture Supporting Almost Constant-Time Scaling](https://mlsanity.com/paper/2009.04861/) (2020)
- [On the Convergence of Tsetlin Machines for the XOR Operator](https://mlsanity.com/paper/2101.02547/) (2021)
- [Tsetlin Machine Embedding: Representing Words Using Logical Expressions](https://mlsanity.com/paper/2301.00709/) (2023)
- [Building Concise Logical Patterns by Constraining Tsetlin Machine Clause Size](https://mlsanity.com/paper/2301.08190/) (2023)
- [Tsetlin Machine for Solving Contextual Bandit Problems](https://mlsanity.com/paper/2202.01914/) (2022)
- [A Relational Tsetlin Machine with Applications to Natural Language Understanding](https://mlsanity.com/paper/2102.10952/) (2021)
- [An Optimized Toolbox for Advanced Image Processing with Tsetlin Machine Composites](https://mlsanity.com/paper/2406.00704/) (2024)
- [The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems](https://mlsanity.com/paper/1905.04206/) (2019)

## Built on by (most cited first, 1 of 1)

- [A Methodology for Transparent Logic-Based Classification Using a Multi-Task Convolutional Tsetlin Machine](https://mlsanity.com/paper/2510.01906/) (2025)

## Abstract (arXiv)

Data modeling using Tsetlin machines (TMs) is all about building logical rules from the data features. The decisions of the model are based on a combination of these logical rules. Hence, the model is fully transparent and it is possible to get explanations of its predictions. In this paper, we present a probability score for TM predictions and develop new techniques for uncertainty quantification to increase the explainability further. The probability score is an inherent property of any TM variant and is derived through an analysis of the TM learning dynamics. Simulated data is used to show a clear connection between the learned TM probability scores and the underlying probabilities of the data. A visualization of the probability scores also reveals that the TM is less confident in its predictions outside the training data domain, which contrasts the typical extrapolation phenomenon found in Artificial Neural Networks. The paper concludes with an application of the uncertainty quantification techniques on an image classification task using the CIFAR-10 dataset, where they provide new insights and suggest possible improvements to current TM image classification models.