I know that learning curves are a good tool to diagnose overfitting or underfitting for a model.
Their working principle is simple: The training/validation loss/accuracy is plotted depending on specific parameters, e.g., the learning rate, the model architecture complexity, the training set size, the number of training epochs, etc.
However, should I always plot many different learning curves, i.e., depending on these different parameters? Or is it sufficient, if I only plot one learning curve depending on just one parameter? Thus, would it be reliable enough, if I would just plot the learning curve based on the keras (tensorflow 2 as the backend) model.fit history, i.e., the training/validation loss depending on the training epochs.