Overview#Underfitting or High Bias error occurs when a Machine Learning model does not adequately capture the underlying structure of 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