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Underfitting
Underfitting

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!!! Overview
[{$pagename}] or High [Bias error] occurs when a [Machine Learning model] does not adequately capture the underlying structure of the [Training dataset].
[{$pagename}] is when the [Machine Learning model] does not perform well on the [Training dataset].
An [{$pagename}] [Machine Learning model] is where some parameters or terms that would appear in a correctly specified model are missing.
[{$pagename}] would occur, for example, when fitting a linear model to non-linear [data]. Such a model will tend to have poor predictive performance.
!! Solving for [{$pagename}]
A bigger [Machine Learning model] (ie more [Hidden layers]) will almost always just reduces your [Bias error] without necessarily increasing [Overfitting] ([Variance error]), so long as you regularize appropriately.
Generally, using a larger [Training dataset] will __NOT__ solve [{$pagename}]
!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
This page (revision-1) was last changed on 29-Nov-2024 16:16 by UnknownAuthorTop