It seems that without knowing the model complexity, it is difficult to state for certain what is the relationship between the number of training examples and over/underfitting.
As a concrete example, suppose that I have some unspecified class of model. I have 1000 data at my disposal. Suppose we partition the data into N training examples and train a classifier based on these N points.
Now supposed that N is small (e.g., 200) will I have overfitting or underfitting? Similarly, suppose N is large (e.g., 800), what is the answer to the above question?
It seems logically plausible that both might occur.
Can someone chime in and come up with some example where one or the other might occur (or both)?