Noise

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See: Noisy Data, Measurement Error, Classification Error, Missing Attibute Value.



References

2017

In addition to errors, training examples may have missing attribute values. That is, the values of some attribute values are not recorded.

Noisy data can cause learning algorithms to fail to converge to a concept description or to build a concept description that has poor classification accuracy on unseen examples. This is often due to overfitting.