Machine learning is exceptionally good at finding structure in data. But a convincing pattern is not the same thing as a durable explanation.
Performance on a held-out dataset tells us something useful, but not everything. When conditions shift, relationships that appeared stable can weaken or reverse. In finance, where incentives and markets adapt, this is not an edge case.
Testing therefore has to include uncomfortable questions: which examples does the model get wrong, how sensitive is it to small changes, and which assumptions are hidden in the training data?
Knowing a model's limits is part of knowing the model.
Mathematical structure can give learning systems useful boundaries. It can encode known relationships, suggest meaningful diagnostics, and make failures easier to identify before they reach a user or a decision-maker.
The goal is not to choose between theory and data. It is to let each reveal the other's blind spots.
Mikael Helin / Journal · SAMPLE ARTICLE
