  Facilitated by the recent advances of Machine Learning (ML), the automated
design of optimization heuristics is currently shaking up evolutionary
computation (EC). Where the design of hand-picked guidelines for choosing a
most suitable heuristic has long dominated research activities in the field,
automatically trained heuristics are now seen to outperform human-derived
choices even for well-researched optimization tasks. ML-based EC is therefore
not any more a futuristic vision, but has become an integral part of our
community.
  A key criticism that ML-based heuristics are often faced with is their
potential lack of explainability, which may hinder future developments. This
applies in particular to supervised learning techniques which extrapolate
algorithms' performance based on exploratory landscape analysis (ELA). In such
applications, it is not uncommon to use dozens of problem features to build the
models underlying the specific algorithm selection or configuration task. Our
goal in this work is to analyze whether this many features are indeed needed.
Using the classification of the BBOB test functions as testbed, we show that a
surprisingly small number of features -- often less than four -- can suffice to
achieve a 98\% accuracy. Interestingly, the number of features required to meet
this threshold is found to decrease with the problem dimension. We show that
the classification accuracy transfers to settings in which several instances
are involved in training and testing. In the leave-one-instance-out setting,
however, classification accuracy drops significantly, and the
transformation-invariance of the features becomes a decisive success factor.
