In what stage should you plot validation and learning curve
I understand that with &quot;learning curve&quot; you are referring to the plot of the loss function over the training data (or subsets of it) when optimizing with iterative methods, like gradient descent. This is sometimes referred to as the training loss plot.
You use a training loss plot during training, to evaluate the convergence of your training algorithm and model. Usually, together with the training loss plot, you visualize the validation loss, that is, the loss function computed on a held-out dataset, to evaluate whether your model is overfitting your training data.
Normally, the training loss gets a new point after each optimization step. For instance, in stochastic gradient descent, the training loss points are the loss evaluated at each minibatch. On the other hand, the validation loss curve gets a new point after each epoch, or at least after a few optimization steps have been performed.
This is different from a ROC curve, which is evaluated once the model is fully trained, to understand the behavior of the trained model.