用语法约束防止机器学习数据泄露，从代码层面杜绝关键错误。
A Grammar of Machine Learning Workflows: Rejecting Data Leakage at Call Time
- 设计八种类型构件与图结构，强制阻止数据泄露的代码构造。
- 首次在调用时强制执行评估边界，有效避免训练泄漏。
- 适合科研人员和开发者，提升实验可复现性与结果可信度。
数据泄露问题已在30个科学领域中发现648篇已发表论文存在。尽管防范方法已存在十余年，但工具未能落实教材所教规范。本文提出一种语法体系（由八种类型构件通过有向无环图连接，包含四项硬约束），使最严重的泄露类型在该语法范围内无法表示。核心机制为终端评估门：首次在同行评审文献中记录的、在调用时强制执行的评估/评估边界，其规格精确到可独立复现。配套的2,047个数据集横断面研究验证了约束的实证效果。提供Python和R两个参考实现。
原文摘要 · Abstract (English)
Data leakage has been identified in 648 published papers across 30 scientific fields. The knowledge to prevent it has existed for over a decade; the problem persists because the tools do not enforce what the textbooks teach. This paper presents a grammar (eight typed primitives connected by a directed acyclic graph with four hard constraints) that makes the most damaging leakage types structurally unrepresentable within the grammar's scope. The core mechanism is a terminal assessment gate: the first call-time-enforced evaluate/assess boundary documented in the peer-reviewed ML methodology literature (to my knowledge, as of May 2026), backed by a specification precise enough for independent reimplementation. A companion landscape study across 2,047 datasets grounds the constraints in measured effect sizes. Two reference implementations (Python, R) are available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。