关于自动化机器学习工具优势与局限性的多声部文献综述
A Multivocal Literature Review on the Benefits and Limitations of Automated Machine Learning Tools
AI总结：
本研究通过多声部文献综述分析 162 个学术与灰色文献来源，系统梳理 AutoML 工具的 18 项优势与 25 项局限，指出其可简化 ML 流程但仍依赖专业用户。
摘要：
背景。机器学习（ML）的进步正在彻底改变每一个应用领域，推动前所未有的转型并促进创新。然而，尽管取得了这些进展，一些组织在采用基于 ML 的技术时仍遇到阻力，这主要是由于 ML 专业人员短缺。在此背景下，自动化机器学习（AutoML）技术被提出作为实现 ML 应用普及化的一种有前景的解决方案。目标。我们旨在概述有关使用 AutoML 工具的优势与局限性的证据。方法。我们开展了一项多声部文献综述，由此识别出来自学术文献的 54 个来源和来自灰色文献的 108 个来源，这些来源报告了 AutoML 的优势与局限性。我们从论文中提取所报告的优势与局限性，并应用了主题分析。结果。我们识别出 18 项优势和 25 项局限性。关于优势，我们强调 AutoML 工具能够帮助简化 ML 工作流的核心步骤，即数据准备、特征工程、模型构建和超参数调优，并在模型性能、效率和可扩展性方面带来具体收益。此外，AutoML 为新手和经验丰富的数据科学家都赋予了能力，促进了 ML 的可及性。另一方面，我们强调了若干可能阻碍 AutoML 广泛采用的局限性。例如，AutoML 工具可能在透明度和互操作性方面带来障碍，在复杂场景中表现出有限的灵活性，并且对 ML 工作流的覆盖不一致。结论。AutoML 在促进用户采用机器学习方面的有效性可能因工具及其使用情境而异。截至目前，AutoML 工具被用于增强而非替代人类专业知识，因此它们需要熟练的用户。
英文摘要：
Context. Advancements in Machine Learning (ML) are revolutionizing every application domain, driving unprecedented transformations and fostering innovation. However, despite these advances, several organizations are experiencing friction in the adoption of ML-based technologies, mainly due to the shortage of ML professionals. In this context, Automated Machine Learning (AutoML) techniques have been presented as a promising solution to democratize ML adoption. Objective. We aim to provide an overview of the evidence on the benefits and limitations of using AutoML tools. Method. We conducted a multivocal literature review, which allowed us to identify 54 sources from the academic literature and 108 sources from the grey literature reporting on AutoML benefits and limitations. We extracted reported benefits and limitations from the papers and applied thematic analysis. Results. We identified 18 benefits and 25 limitations. Concerning the benefits, we highlight that AutoML tools can help streamline the core steps of ML workflows, namely data preparation, feature engineering, model construction, and hyperparameter tuning, with concrete benefits on model performance, efficiency, and scalability. In addition, AutoML empowers both novice and experienced data scientists, promoting ML accessibility. On the other hand, we highlight several limitations that may represent obstacles to the widespread adoption of AutoML. For instance, AutoML tools may introduce barriers to transparency and interoperability, exhibit limited flexibility for complex scenarios, and offer inconsistent coverage of the ML workflow. Conclusions. The effectiveness of AutoML in facilitating the adoption of machine learning by users may vary depending on the tool and the context in which it is used. As of today, AutoML tools are used to increase human expertise rather than replace it, and, as such, they require skilled users.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
