An Approach to Symbolic Regression Using Feyn
This provides a more interpretable alternative to black-box models for researchers and practitioners in scientific domains, though it appears incremental as it builds on symbolic regression concepts.
The paper introduces Feyn, a supervised machine learning tool powered by the QLattice, which explores models as interpretable graphs to balance interpretability, complexity, and performance, and demonstrates its application to scientific problems for data discovery.
In this article we introduce the supervised machine learning tool called Feyn. The simulation engine that powers this tool is called the QLattice. The QLattice is a supervised machine learning tool inspired by Richard Feynman's path integral formulation, that explores many potential models that solves a given problem. It formulates these models as graphs that can be interpreted as mathematical equations, allowing the user to completely decide on the trade-off between interpretability, complexity and model performance. We touch briefly upon the inner workings of the QLattice, and show how to apply the python package, Feyn, to scientific problems. We show how it differs from traditional machine learning approaches, what it has in common with them, as well as some of its commonalities with symbolic regression. We describe the benefits of this approach as opposed to black box models. To illustrate this, we go through an investigative workflow using a basic data set and show how the QLattice can help you reason about the relationships between your features and do data discovery.