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Facilitated by the recent advances of Machine Learning (ML), the automateddesign of optimization heuristics is currently shaking up evolutionarycomputation (EC). Where the design of hand-picked guidelines for choosing amost suitable heuristic has long dominated research activities in the field,automatically trained heuristics are now seen to outperform human-derivedchoices even for well-researched optimization tasks. ML-based EC is thereforenot any more a futuristic vision, but has become an integral part of ourcommunity.  A key criticism that ML-based heuristics are often faced with is theirpotential lack of explainability, which may hinder future developments. Thisapplies in particular to supervised learning techniques which extrapolatealgorithms' performance based on exploratory landscape analysis (ELA). In suchapplications, it is not uncommon to use dozens of problem features to build themodels underlying the specific algorithm selection or configuration task. Ourgoal 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 asurprisingly small number of features -- often less than four -- can suffice toachieve a 98\% accuracy. Interestingly, the number of features required to meetthis threshold is found to decrease with the problem dimension. We show thatthe classification accuracy transfers to settings in which several instancesare involved in training and testing. In the leave-one-instance-out setting,however, classification accuracy drops significantly, and thetransformation-invariance of the features becomes a decisive success factor.

Towards Explainable Exploratory Landscape Analysis: Extreme Feature Selection for Classifying BBOB Functions