A Grammar of Machine Learning Workflows: Rejecting Data Leakage at Call Time
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 independen...
| Authors | Simon Roth |
|---|---|
| Primary category | cs.LG |
| Published | 11 March 2026 |
| Last updated at source | 29 May 2026 |
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