Underfitting
When a model is too simple to capture the pattern, and is wrong on training and new data alike.
Underfitting is the opposite failure to overfitting: the model has not learned enough. Symptoms are poor scores everywhere, and the fixes are more capacity, better features, longer training, or a less aggressive regulariser.
In practice: Fitting a straight line to data that clearly curves.