In first part we explored the statistical model underlying the machine learning problem, and used it to formalize the problem in terms of obtaining the minimum generalization error. By noting that we cannot directly evaluate the generalization error of an ML model, we continued in the second part by establishing a theory that relates this elusive generalization error to another error metric that we can actually evaluate, which is the empirical error. Our final result was that:

That is: the generalization error (or the risk) $R(h)$ is bounded by the empirical...