Take $ H $ to be a class of binary classifiers ever a domain $X$. $D$ is a unknown distrbution over $X$, and $f$ is the target hypothesis in $H$. If we fix some $h$ $\in H$ we have the expected value of the training error equal to generalization error, that is:$\mathbb{E}[L_S(h)]=L_{(D,f)}(h)$Could you please come up with the proof of this statement?Would you recommend another page inside stackexchange for these theoretical questions about machine learning?Thank you.
Prove that the expected value of the training error is equal to generalization error for a class of binary clasiffiers
machine learning