I am reading "Understanding Machine Learning: From Theory to Algorithms." In Chapter 5.2, it says that choosing the hypothesis class $\mathcal{H}$ to be a very rich class decreases the approximation error but at the same time
might increase the estimation error, as a rich $\mathcal{H}$ might lead to overfitting.
Based on this, it does not explain why overfitting is bad as we are talking about an upper bound. In this regard, I am even not sure in what sense Learning theory is necessary. As far as I know, the learning theory is concerned with the problem of generalization. To describe the problem in more detail, let me give some setup.
Let $\mathcal{D} \sim \mathcal{X}\times \mathcal{Y}$ be a data distribution and $\mathcal{T}_m = \{(x_i, y_i)\}_{i=1}^m$ be a set of $m$- iid data from $\mathcal{D}$. Let $\mathcal{H}$ be a hypothesis class. For each $h \in \mathcal{H}$, let us define $$L_{\mathcal{T}_m}(h) = \frac{1}{m}\sum_{i=1}^m (h(x_i) -y_i)^2, \qquad L_{\mathcal{D}}(h) = \mathbb{E}_{(x,y) \sim \mathcal{D}}[(h(x)-y)^2]. $$ By Glivenko–Cantelli theorem or Kolmogorov's theorem, we know that the empicical measure converges to the underlying distribution. Therefore, $$\lim_{m \to \infty} L_{\mathcal{T}_m}(h) = L_\mathcal{D}(h).$$ I couldn't find an analog of this result on a high-dimension. However, assuming $|L_{\mathcal{D}}(h) - L_{\mathcal{T}_m}(h)| \le \mathcal{O}(m^{-1/d})$ where $d$ is the input dimension, I believe that the generalization is now well explained. It shows that how the empirical error is close to the true error and the difference goes to 0 as the number of samples goes to $\infty$.
Then I think that the learnability is all about measuing difference between the empirical measure and the underlying measure. If then, why do we even care about statements like, with probability exceeding $1-\delta$ on the iid $m$-samples, $$ L_{\mathcal{D}}(h) \le L_{\mathcal{T}_m}(h) + \mathcal{O}\left(\frac{\log |\mathcal{H}|/\delta}{m}\right). $$ By the way, this is a PAC learnablitiy statement for a finite hypothesis class. Even this simple case, it does not explain that why overfitting is bad in generalization.
Any comments/suggestions/answers will be very appreciated.