just so i understand this. to detect overfitting we monitor accuracy or validation error?
in the response above, Jeremy states that we want to track accuracy, then in the lesson 8 video, Jeremy says it’s the validation error we want to track:
so overfit means what? it means that your training loss is lower than your validation loss. no. no it doesn’t mean that. remember it doesn’t mean that. a well fit model will almost always have training loss lower than the validation loss. remember that overfit means you have actually personally seen your validation error getting worse okay. until you see that happening you’re not overfitting.
or maybe this is just definition thing, we have:
validation loss
training loss
validation error
accuracy
error_rate (introduced in pets notebook from part 1)
so i understand that validation loss is not the same as validation error
and that validation error (or error rate) = 1 - accuracy
and that to detect overfitting we track accuracy or validation error (error rate).
correct me if i am wrong here. thanks 