  Drive towards improved performance of machine learning models has led to the
creation of complex features representing a database of condensed matter
systems. The complex features, however, do not offer an intuitive explanation
on which physical attributes do improve the performance. The effect of the
database on the performance of the trained model is often neglected. In this
work we seek to understand in depth the effect that the choice of features and
the properties of the database have on a machine learning application. In our
experiments, we consider the complex phase space of carbon as a test case, for
which we use a set of simple, human understandable and cheaply computable
features for the aim of predicting the total energy of the crystal structure.
Our study shows that (i) the performance of the machine learning model varies
depending on the set of features and the database, (ii) is not transferable to
every structure in the phase space and (iii) depends on how well structures are
represented in the database.
