Research in various fields is currently experiencing challenges regarding the reproducibility of results. This problem is also prevalent in machine learning (ML) research. The issue arises primarily due to unpublished data and/or source code and the sensitivity of ML training conditions. Although different solutions have been proposed to address this issue, such as using ML platforms, the level of reproducibility in ML-driven research remains unsatisfactory. Therefore, in this article, we discuss the reproducibility of ML-driven research with three main aims: (i) identify the barriers to reproducibility when applying ML in research as well as categorize the barriers to different types of reproducibility (description, code, data, and experiment reproducibility), (ii) identify potential drivers such as tools, practices, and interventions that support ML reproducibility as well as distinguish between technology-driven drivers, procedural drivers, and drivers related to awareness and education, and (iii) map the drivers to the barriers. With this work, we hope to provide insights and contribute to the decision-making process regarding the adoption of different solutions to support ML reproducibility.

翻译：当前，各领域研究正面临结果可复现性方面的挑战。这一问题在机器学习研究中也普遍存在，主要源于未公开的数据和/或源代码，以及ML训练条件的敏感性。尽管已有多种解决方案被提出以应对此问题（例如使用ML平台），但ML驱动研究的可复现性水平仍不尽如人意。因此，本文围绕ML驱动研究的可复现性展开讨论，主要目标包括：（i）识别在研究中使用ML时面临的可复现性障碍，并将这些障碍归类至不同类型的可复现性（描述可复现性、代码可复现性、数据可复现性和实验可复现性）；（ii）识别支持ML可复现性的潜在驱动因素（如工具、实践和干预措施），并区分技术驱动因素、流程驱动因素以及与认知教育相关的驱动因素；（iii）建立驱动因素与障碍之间的映射关系。通过本项工作，我们期望为决策过程提供见解，助力选择支持ML可复现性的多样化解决方案。