random_error [Neurosurgery Wiki]

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A model with high [[error]] due to [[bias]] can fail to capture the regularities in the [[data]], resulting in an inaccurate model underfitting the data. Increasing the complexity of the model, such as adding more [[parameter]]s in the model, can reduce this [[bias]]. However, an excessively complex model, such as having too many parameters compared to the number of [[patient]]s, can describe [[random error]] or noise instead of the meaningful relationships, referred to as overfitting of the data. This results in an increase in error due to variance and a reduced generalizability to previously unseen data. The complexity of a model should, therefore, be a tradeoff between bias and [[variance]]
((Jordan MI, Mitchell TM. Machine learning: trends, perspectives, and prospects. Science . 2015;349(6245):255-260.)).