Validation Gap Diagnosis
A model review. You put up a model whose training error is low and whose validation error is not, and the reviewer wants to know what you make of it.
The questions in this round
Your training error is low, but validation error is much higher. Explain what is happening and what you would try next.
You add regularization and the gap closes. Validation accuracy does not move. What did you learn?
One more detail I left out: the validation set was made by randomly splitting rows, and each customer appears many times. Does your diagnosis survive?