2026-08-13 –, Room 2
Symbolic learning is a branch of machine learning focused on building classifiers that can be translated into logical rules, making them far more readable than neural networks or other statistical models. While training a symbolic model is a necessary first step, it is the post-processing stage that yields the most relevant insights. We present a live walkthrough of SolePostHoc.jl, a SOLE package dedicated to post-processing, allowing for rule extraction, boosting and model simplification.
Symbolic learning studies algorithms that produce models expressible as logical rules. Common examples include decision trees and random forests, whose predictions can be traced back to human-readable conditions on the input data. This interpretability makes symbolic models particularly valuable in domains where understanding why a model makes a decision matters as much as the decision itself.
This session is structured as a hands-on demo on the SolePostHoc.jl package, which serves as the post-hoc analysis layer of the SOLE ecosystem. Starting from training a decision tree on a real dataset, we walk through what SolePostHoc.jl allows from that point onward:
- Rule extraction: deriving explicit logical rules from a symbolic model through a uniform interface, supporting multiple extraction algorithms;
- Rule simplification and manipulation: collapsing redundant or overlapping rules into more succinct, equally expressive theories; for instance, reducing
IF A AND B THEN C; IF A AND NOT B THEN Cto the simpler (but equivalent)IF A THEN C; - Surrogate methods and knowledge distillation: approximating more complex models with interpretable surrogates, transferring knowledge into transparent representations;
- Model boosting and certification: enhancing model capabilities boosting performance on a specified metric or ensuring the satisfiability of a desired feature or constraint.
Arguably, post-hoc analysis is the stage where symbolic learning delivers on its core promise: turning a trained model into usable, understandable knowledge. SolePostHoc.jl aims to give this stage the attention it deserves, providing a reliable and extensible toolkit for anyone interested in interpretability and explainability, from newcomers to experts looking for a fully customizable system.
Hi, my name is Marco Perrotta. I'm a master student in computer science at the University of Ferrara, where I also work as a collaborator at the Applied Computational Logic and Artificial Intelligence Lab. My main interest is how technology can be used to understand and study language.