# NN2Rules: Extracting Rule List from Neural Networks

arXiv: https://arxiv.org/abs/2207.12271 · HTML: https://mlsanity.com/paper/2207.12271/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 5 of 5)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Distilling a Neural Network Into a Soft Decision Tree](https://mlsanity.com/paper/1711.09784/) (2017)
- [Beyond Sparsity: Tree Regularization of Deep Models for Interpretability](https://mlsanity.com/paper/1711.06178/) (2017)
- [Regional Tree Regularization for Interpretability in Black Box Models](https://mlsanity.com/paper/1908.04494/) (2019)
- [Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization](https://mlsanity.com/paper/1904.05394/) (2019)

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

- [Enabling Regional Explainability by Automatic and Model-agnostic Rule Extraction](https://mlsanity.com/paper/2406.17885/) (2024)

## Abstract (arXiv)

We present an algorithm, NN2Rules, to convert a trained neural network into a rule list. Rule lists are more interpretable since they align better with the way humans make decisions. NN2Rules is a decompositional approach to rule extraction, i.e., it extracts a set of decision rules from the parameters of the trained neural network model. We show that the decision rules extracted have the same prediction as the neural network on any input presented to it, and hence the same accuracy. A key contribution of NN2Rules is that it allows hidden neuron behavior to be either soft-binary (eg. sigmoid activation) or rectified linear (ReLU) as opposed to existing decompositional approaches that were developed with the assumption of soft-binary activation.