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1

The telltale signature of overfitting is when your validation loss starts increasing, while your training loss continues decreasing, i.e.: (Image adapted from Wikipedia entry on overfitting) It is clear that this does not happen in your diagram, hence your model does not overfit. A difference between a training and a validation score by itself does not ...

2

The validation loss settles exactly at an error of one. Probably means there's something off with either the kind of data validation set has or with something in the training. An exact validation loss of one almost definitely means there's something off. I'd recommend before doing anything thoroughly go through your data or see if there's anything to debug ...

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Depends on what does 1 represent in your task. If you are trying to predict household prices and 1 represents \$1, I think the average validation loss is good. If 1 represents \$10000 in this case, probably something is not right. But remember that there are 2 parts contributing to the overall loss. The mse loss and the l2 penalty loss. (Also remember that ...

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