You train a model and track two numbers as training goes on:
Which explanation best fits what you're seeing?
Select all that apply.
The model is overfitting — it's memorising the training set instead of learning patterns that generalise.
The model is underfitting — it isn't powerful enough to capture the pattern.
The learning rate is too low, so training has stalled.
Nothing is wrong — validation accuracy always drops late in training.