"A model learns statistical regularities from training data, but its past performance provides weaker evidence when deployment conditions change."
Zhang and colleagues explain why predictive models have limited performance guarantees on inputs that differ from their training distribution. Their research grounds the manuscript's distinction between successful prediction within a familiar statistical map and uncertain performance in new conditions.
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