Scientific ML is a loop: evaluation is an experiment on your whole modeling hypothesis
bravo_abad · x · 2026-10-09
A science-ML scholar argues that scientific ML projects are not a straight line (data → model → prediction) but a loop that can reach far back.
- Performance collapse in a regime may mean the model is wrong — or a missing variable in the representation, noisy labels, uncovered data regimes, a wrong validation split, or a badly formulated scientific question. So you inspect data, redefine features, reconsider targets, collect measurements, change validation strategy.
- Evaluation isn't just the last step producing a number; it's an experiment on your entire modeling hypothesis, and failure tells you what to change. What's distinctive in science is how far back the loop reaches — into what you measure and how you pose the question.
- One asymmetry: you can iterate on training/validation data, though validation sets wear out from repeated consultation. The final test set must stay outside the loop — once it shapes the model, it's no longer independent, and you can't unsee it.
Key principle for AI for Science: iteration is not a failure of the workflow; iteration IS the workflow. Reliable ML depends on the discipline of the entire workflow, not just algorithm choice.
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