DeepCAVE让超参优化过程可交互可视化，助力调试模型
DeepCAVE: A Visualization and Analysis Tool for Automated Machine Learning
- 通过交互式仪表板分析超参优化过程
- 可发现调优瓶颈与未利用潜力
- 适合研究者、数据科学家与工程师使用
超参数优化（HPO）是自动化机器学习（AutoML）的核心范式，对充分挖掘机器学习模型潜力至关重要；然而其复杂性给理解与调试带来挑战。本文提出DeepCAVE，一款用于交互式可视化与分析的工具，帮助研究人员、数据科学家和机器学习工程师探索HPO过程的多个方面，识别问题、未开发潜力及关于待调优模型的新见解。通过提供可操作的洞察，DeepCAVE提升了HPO与机器学习在设计层面的可解释性，旨在推动未来更稳健、高效的算法发展。
原文摘要 · Abstract (English)
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.
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