# The Regression Tsetlin Machine: A Tsetlin Machine for Continuous Output Problems

## TL;DR

The paper introduces the Regression Tsetlin Machine (RTM), a novel extension of the Tsetlin Machine that performs continuous output regression by transforming pattern recognition into a regression task with improved accuracy and efficiency.

## Contribution

It presents the RTM, a new Tsetlin Machine variant that maps complex patterns to continuous outputs using a novel voting, normalization, and feedback mechanism, extending TM capabilities beyond classification.

## Key findings

- RTM achieves superior regression accuracy on artificial datasets.
- RTM uses fewer clauses and computational resources than CTM and MTM.
- RTM performs well on noisy and noise-free data, demonstrating robustness.

## Abstract

The recently introduced Tsetlin Machine (TM) has provided competitive pattern classification accuracy in several benchmarks, composing patterns with easy-to-interpret conjunctive clauses in propositional logic. In this paper, we go beyond pattern classification by introducing a new type of TMs, namely, the Regression Tsetlin Machine (RTM). In all brevity, we modify the inner inference mechanism of the TM so that input patterns are transformed into a single continuous output, rather than to distinct categories. We achieve this by: (1) using the conjunctive clauses of the TM to capture arbitrarily complex patterns; (2) mapping these patterns to a continuous output through a novel voting and normalization mechanism; and (3) employing a feedback scheme that updates the TM clauses to minimize the regression error. The feedback scheme uses a new activation probability function that stabilizes the updating of clauses, while the overall system converges towards an accurate input-output mapping. The performance of the RTM is evaluated using six different artificial datasets with and without noise, in comparison with the Classic Tsetlin Machine (CTM) and the Multiclass Tsetlin Machine (MTM). Our empirical results indicate that the RTM obtains the best training and testing results for both noisy and noise-free datasets, with a smaller number of clauses. This, in turn, translates to higher regression accuracy, using significantly less computational resources.