Underfitting

Underfitting is the phenomenon of a model not performing well, i.e., not making good predictions, because it wasn’t able to correctly or completely capture the signal in the training set. In other words, the model is generalizing too much, to the point that it’s actually missing the signal.

This means that the model doesn’t perform well on training examples (resulting in a high training loss), nor on examples it hasn’t seen before (resulting in a high validation loss).