  Predicting the performance of an optimization algorithm on a new problem
instance is crucial in order to select the most appropriate algorithm for
solving that problem instance. For this purpose, recent studies learn a
supervised machine learning (ML) model using a set of problem landscape
features linked to the performance achieved by the optimization algorithm.
However, these models are black-box with the only goal of achieving good
predictive performance, without providing explanations which landscape features
contribute the most to the prediction of the performance achieved by the
optimization algorithm. In this study, we investigate the expressiveness of
problem landscape features utilized by different supervised ML models in
automated algorithm performance prediction. The experimental results point out
that the selection of the supervised ML method is crucial, since different
supervised ML regression models utilize the problem landscape features
differently and there is no common pattern with regard to which landscape
features are the most informative.
