  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.
