How to check learning feasibility on a binary classification problem with Hoeffding's inequality/VC dimension with Python?
Well here is how I handled it : I generate multiple train/test samples, run the algorithm on them, calculate Ein as the train set error, Eout estimated by the test set error, calculate how many times their differnces exceeds the value of epsilon (for a range of epsilons). And then I plot the curve of these rates of exceeding epsilon and the curve of the right side of the Hoeffding's /VC inequality so I see if the differences curve is always under the Hoeffding/VC's Bound curve, this informs me about the learning feasiblity.
I have a simple binary classification problem, and I want to assess the learning feasibility using Hoeffding's Inequality and also if possible VC dimension. 
I understand the theory but, I am still stuck on how to implement it in Python.
I understand that In-sample Error (Ein) is the training Error. Out of sample Error(Eout) is the error on the test subsample I guess.
But how do I plot the difference between these two errors with the Hoeffdings bound?