  Understanding the decision-making process of a machine/deep learning model is
crucial, particularly in security-sensitive applications. In this study, we
introduce a neural network framework that combines the global and exact
interpretability properties of rule-based models with the high performance of
deep neural networks.
  Our proposed framework, called $\textit{Truth Table rules}$ (TT-rules), is
built upon $\textit{Truth Table nets}$ (TTnets), a family of deep neural
networks initially developed for formal verification. By extracting the set of
necessary and sufficient rules $\mathcal{R}$ from the trained TTnet model
(global interpretability), yielding the same output as the TTnet (exact
interpretability), TT-rules effectively transforms the neural network into a
rule-based model. This rule-based model supports binary classification,
multi-label classification, and regression tasks for tabular datasets.
Furthermore, our TT-rules framework optimizes the rule set $\mathcal{R}$ into
$\mathcal{R}_{opt}$ by reducing the number and size of the rules. To enhance
model interpretation, we leverage Reduced Ordered Binary Decision Diagrams
(ROBDDs) to visualize these rules effectively.
  After outlining the framework, we evaluate the performance of TT-rules on
seven tabular datasets from finance, healthcare, and justice domains. We also
compare the TT-rules framework to state-of-the-art rule-based methods. Our
results demonstrate that TT-rules achieves equal or higher performance compared
to other interpretable methods while maintaining a balance between performance
and complexity. Notably, TT-rules presents the first accurate rule-based model
capable of fitting large tabular datasets, including two real-life DNA datasets
with over 20K features. Finally, we extensively investigate a rule-based model
derived from TT-rules using the Adult dataset.

Artificial Intelligence;Discrete Mathematics;Machine Learning