机器学习研究中的陷阱：重新审视开发周期
Pitfalls in Machine Learning Research: Reexamining the Development Cycle
摘要：
机器学习有可能推动数据科学的进一步发展，但它受到特设设计过程，数据卫生状况差以及模型评估缺乏统计严谨性的极大阻碍。最近，这些问题已经开始引起更多的关注，因为它们在研究和开发中引起了公众和尴尬的问题。借鉴我们作为机器学习研究人员的经验，我们遵循从算法设计到数据汇集再到模型评估的机器学习过程，提请注意常见的陷阱并提供实用的改进建议。在每一步中，都会引入案例研究，以突出这些陷阱在实践中是如何发生的，以及可以改进的地方。
英文摘要：
Machine learning has the potential to fuel further advances in data science, but it is greatly hindered by an ad hoc design process, poor data hygiene, and a lack of statistical rigor in model evaluation. Recently, these issues have begun to attract more attention as they have caused public and embarrassing issues in research and development. Drawing from our experience as machine learning researchers, we follow the machine learning process from algorithm design to data collection to model evaluation, drawing attention to common pitfalls and providing practical recommendations for improvements. At each step, case studies are introduced to highlight how these pitfalls occur in practice, and where things could be improved.
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。
