Underfitting

Overfitting is when a model is too complex and memorizes training noise (performing well on training data but poorly on test data). Underfitting is when the model is too simple and fails to learn the basic patterns (performing poorly on both datasets).

By increasing model complexity (e.g., adding layers or parameters), training for more epochs, engineering more predictive features, or reducing regularization constraints.