Overfitting
Version 1.0.0 · Updated 2026-07-30
CORE DEFINITION
The model fits the noise and details of the training data too closely, resulting in poor performance on new data.
SCAFFOLDING EFFECT
Reduce cognitive load
A warning about generalization ability, reminding that "performing well on known data" does not equal "performing well on unknown data."
Anchor fast decisions
The model captures noise in the training data as patterns, fitting too tightly, leading to poor generalization and failure on unseen data.
MINIMUM ACTION
In progress 0/4Practice this model in one real situation:
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Source support: Explicit
- en.wikipedia.orghttps://en.wikipedia.org/wiki/Overfittingverified
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