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In this article we introduce the supervised machine learning tool calledFeyn. The simulation engine that powers this tool is called the QLattice. TheQLattice is a supervised machine learning tool inspired by Richard Feynman'spath integral formulation, that explores many potential models that solves agiven problem. It formulates these models as graphs that can be interpreted asmathematical equations, allowing the user to completely decide on the trade-offbetween interpretability, complexity and model performance.  We touch briefly upon the inner workings of the QLattice, and show how toapply the python package, Feyn, to scientific problems. We show how it differsfrom traditional machine learning approaches, what it has in common with them,as well as some of its commonalities with symbolic regression. We describe thebenefits of this approach as opposed to black box models.  To illustrate this, we go through an investigative workflow using a basicdata set and show how the QLattice can help you reason about the relationshipsbetween your features and do data discovery.

An Approach to Symbolic Regression Using Feyn