A failure mode in which a machine-learning model learns the noise and specific idiosyncrasies of its training data rather than the underlying pattern, so it performs well on the data it was trained on but poorly on new, unseen data.
Facts
Core PrincipleProducing an analysis that corresponds too closely or exactly to a particular set of data and may therefore fail to fit additional data or predict future observations reliably. 1 Connections
Sources
1. Wikipedia: Overfitting
Lead, first sentenceQuote, Lead, first sentence
is the production of an analysis that corresponds too closely or exactly to a particular set of data and may therefore fail to fit to additional data or predict future observations reliably.
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