What specific risk arises from poor data quality when using an overly complex model?
Answer
The overly complex model mistakes inherent measurement noise or errors for meaningful information and models them explicitly.
If data contains errors or noise, a model searching for perfect accuracy will incorporate these imperfections into its learned parameters, much like memorizing typos in a textbook, which hurts generalization.
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