What Is Overfitting?
Overfitting is a modelling failure in which a system learns the noise and idiosyncrasies of its training data rather than the underlying pattern, so it performs well in testing but poorly in the real world.
Overfitting — a modelling failure in which a system learns the noise and idiosyncrasies of its training data rather than the underlying pattern, so it performs well in testing but poorly in the real world.
An overfitted model has effectively memorised its training examples instead of generalising from them. It looks accurate on data it has already seen but degrades sharply on new inputs. Overfitting is a governance concern because a model that was validated on historical data can fail silently once deployed, producing confident but wrong outputs — which is why post-market monitoring and testing on representative, held-out data are core controls.
Source: NIST AI 100-1; ISO/IEC 22989:2022
Plain-language explanation
An overfitted model has effectively memorised its training examples instead of generalising from them. It looks accurate on data it has already seen but degrades sharply on new inputs. Overfitting is a governance concern because a model that was validated on historical data can fail silently once deployed, producing confident but wrong outputs — which is why post-market monitoring and testing on representative, held-out data are core controls.
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