Hyperparameter optimization (HPO), as a central paradigm of AutoML, is crucial for leveraging the full potential of machine learning (ML) models; yet its complexity poses challenges in understanding and debugging the optimization process. We present DeepCAVE, a tool for interactive visualization and analysis, providing insights into HPO. Through an interactive dashboard, researchers, data scientists, and ML engineers can explore various aspects of the HPO process and identify issues, untouched potentials, and new insights about the ML model being tuned. By empowering users with actionable insights, DeepCAVE contributes to the interpretability of HPO and ML on a design level and aims to foster the development of more robust and efficient methodologies in the future.

翻译：超参数优化（HPO）作为自动化机器学习（AutoML）的核心范式，对于充分发挥机器学习（ML）模型的潜力至关重要；然而其复杂性给理解和调试优化过程带来了挑战。我们提出了DeepCAVE，一个用于交互式可视化与分析的工具，旨在提供对HPO的深入洞察。通过交互式仪表板，研究人员、数据科学家和机器学习工程师能够探索HPO过程的各个方面，并识别正在调优的机器学习模型存在的问题、未开发的潜力以及新的见解。通过为用户提供可操作的洞察，DeepCAVE在设计层面上促进了HPO和机器学习的可解释性，并旨在推动未来开发更稳健、更高效的方法论。