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Hyperparameter optimization (HPO) is a key component of machine learningmodels for achieving peak predictive performance. While numerous methods andalgorithms for HPO have been proposed over the last years, little progress hasbeen made in illuminating and examining the actual structure of these black-boxoptimization problems. Exploratory landscape analysis (ELA) subsumes a set oftechniques that can be used to gain knowledge about properties of unknownoptimization problems. In this paper, we evaluate the performance of fivedifferent black-box optimizers on 30 HPO problems, which consist of two-,three- and five-dimensional continuous search spaces of the XGBoost learnertrained on 10 different data sets. This is contrasted with the performance ofthe same optimizers evaluated on 360 problem instances from the black-boxoptimization benchmark (BBOB). We then compute ELA features on the HPO and BBOBproblems and examine similarities and differences. A cluster analysis of theHPO and BBOB problems in ELA feature space allows us to identify how the HPOproblems compare to the BBOB problems on a structural meta-level. We identify asubset of BBOB problems that are close to the HPO problems in ELA feature spaceand show that optimizer performance is comparably similar on these two sets ofbenchmark problems. We highlight open challenges of ELA for HPO and discusspotential directions of future research and applications.

HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis