Underfitting or High Bias error occurs when a Machine Learning model does not adequately capture the underlying structure of the Training dataset.

Underfitting is when the Machine Learning model does not perform well on the Training dataset.

An Underfitting Machine Learning model is where some parameters or terms that would appear in a correctly specified model are missing.

Underfitting 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 Underfitting#

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 Underfitting

More Information#

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