In the above decomposition, part (b) does not depend on the data and is non-stochastic. It describes how far away our assumptions (\mathcal{F}) are from the truth (\mathcal{F}^*). (b) will be strictly greater than 0 if we make assumptions that are too strong (\mathcal{F} too small). On the other hand, failing to put enough restrictions on \mathcal{F} will cause it to be not learnable, and part (a) will not stochastically converge to 0. This is the well-known overfitting problem in statistics and machine learning literature.
