Automated machine learning (AutoML) systems propose an end-to-end solution to
a given machine learning problem, creating either fixed or flexible pipelines.
Fixed pipelines are task independent constructs: their general composition
remains the same, regardless of the data. In contrast, the structure of
flexible pipelines varies depending on the input, making them finely tailored
to individual tasks. However, flexible pipelines can be structurally
overcomplicated and have poor explainability. We propose the EVOSA approach
that compensates for the negative points of flexible pipelines by incorporating
a sensitivity analysis which increases the robustness and interpretability of
the flexible solutions. EVOSA quantitatively estimates positive and negative
impact of an edge or a node on a pipeline graph, and feeds this information to
the evolutionary AutoML optimizer. The correctness and efficiency of EVOSA was
validated in tabular, multimodal and computer vision tasks, suggesting
generalizability of the proposed approach across domains.

自动化机器学习系统提出了一种针对给定机器学习问题的端到端解决方案，创建了固定或灵活的流程。 EVOSA 方法通过引入敏感性分析来解决灵活流程的负面因素，并提高了灵活解决方案的鲁棒性和可解释性。