Hyperparameter optimization (HPO) is a well-studied research field. However,
the effects and interactions of the components in an HPO pipeline are not yet
well investigated. Then, we ask ourselves: can the landscape of HPO be biased
by the pipeline used to evaluate individual configurations? To address this
question, we proposed to analyze the effect of the HPO pipeline on HPO problems
using fitness landscape analysis. Particularly, we studied the DS-2019 HPO
benchmark data set, looking for patterns that could indicate evaluation
pipeline malfunction, and relate them to HPO performance. Our main findings
are: (i) In most instances, large groups of diverse hyperparameters (i.e.,
multiple configurations) yield the same ill performance, most likely associated
with majority class prediction models; (ii) in these cases, a worsened
correlation between the observed fitness and average fitness in the
neighborhood is observed, potentially making harder the deployment of
local-search based HPO strategies. Finally, we concluded that the HPO pipeline
definition might negatively affect the HPO landscape.

论文探讨 HPO 管道对 HPO 问题的影响，通过适应性景观分析研究了 DS-2019 HPO 基准数据集，发现管道定义可能会对 HPO 景观产生负面影响，并确定了大型多样的高维超参数已经形成了一个模式，可能与大多数类预测模型相关联。