  The first step in constructing a machine learning model is defining the
features of the data set that can be used for optimal learning. In this work we
discuss feature selection methods, which can be used to build better models, as
well as achieve model interpretability. We applied these methods in the context
of stress hotspot classification problem, to determine what microstructural
characteristics can cause stress to build up in certain grains during uniaxial
tensile deformation. The results show how some feature selection techniques are
biased and demonstrate a preferred technique to get feature rankings for
physical interpretations.
