====== Model ======

A model is a representation or abstraction of a real-world system or concept.

===== Computational model =====
 
[[Computational model]].
 
===== Machine learning model =====

[[Machine learning model]].

===== Animal model =====

see [[Animal model]].

===== Canonical model =====

see [[Canonical model]].

===== Head model =====

[[Head model]].

[[Glioma model]]

===== Prediction model =====

[[Prediction model]].

===== Statistical model =====
[[Statistical model]]

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.)).