Underfitting
A modeling failure where an algorithm is far too simple to capture the underlying complex patterns in the data, resulting in poor accuracy everywhere.
Think of It Like This
Like trying to explain the entire plot of a massive fantasy book series using only three very generic bullet points.
Underfitting occurs when a linear model is applied to highly non-linear data, or when a neural network is trained for too few epochs or lacks sufficient parameters. It is characterized by high error rates on both the training dataset and the validation dataset. The solution is invariably to increase model capacity or feature complexity.