VC dimension, fat-shattering dimension, and other complexity measures, of a class BV functions I wish to show that a function which is "essentially constant" (defined shortly) can't be a good classifier (machine learning). For this i need to estimate the "complexity" of such a class of functions.
So let $\mathcal X$ be an abstract set (we may assume has metric structure, e.g $\mathbb R^d$). Given $0 < \alpha \ll 1$, define
$$
\mathcal H_\alpha := \{h: \mathcal X \rightarrow [0, 1]\text{ s.t } | \exists \bar{h} \in \mathbb [0, 1] \text{ veryfing } |h(x) - \bar{h}| \le \alpha\;\forall x \in \mathcal X\}.
$$
Questions
(A) What is the VC dimension or fat-shattering dimension of $\mathcal H_\alpha$ ? Good lower and upper bounds thereof would be just as important.
(B) I'd also be interested in metric complexity measures for $\mathcal H_\alpha$ (covering number, metric entropy, etc.).
Good lower and upper bounds thereof would be just as important. 
Observations
Theorem 2 of this 1997 paper shows that there exists absolute constants $c_1,c_2$ such that for any $\mathcal H \subseteq [0,1]^{\mathcal X}$, the following bounds metric entropy and fat-shattering dimension holds
$$
\operatorname{fat}_{\mathcal {H}}(4\epsilon)/32 \le \max_{P}\log_2(\mathcal N(\epsilon,\mathcal H,\mathcal L_1(dP))) \le c_1 \operatorname{fat}_{\mathcal H}(c_2 \epsilon)(\log_2(1/\epsilon))^2.
$$
So if we can estimate $\operatorname{fat}_{\mathcal H_\alpha}(\gamma)$, then we're done!
Answer to one-dimensional case.
A user has given an explicit computation of $\operatorname{fat}_{\mathcal H_\alpha}(\gamma)$ in the simple one-dimensional case $\mathcal X = [0, 1]$.
 A: I think the family $\mathcal{H}_\alpha$ is ill-suited for your purposes, because it is too rich.
For example in the case $\mathcal{X} = [0,1]$ (or anything atomless probability measure space) one can find arbitrarily many $h_\tau\in\mathcal{H}_\alpha$ such that any two of them are at distance $\alpha/2$. This means that $\mathcal{N}(\varepsilon,\mathcal{H}_\alpha,L^1([0,1])) = \infty$ as soon as $\varepsilon\le \alpha/2$.
To prove this explicitely, one can use Hadamard matrices of size $N$ (this is certainly a conceptual overkill and could be replaced by a random choice, or a classical non-compactness argument in functional analysis, but it seems the simplest way to proceed). Divide $[0,1]$ into $N$ equal intervals, and for each sequence $\tau=(\tau_i)_{1\le i\le N} \in \{-1,1\}^N$  define $h_\tau$ to be $\frac{1+\tau_i\alpha}2$ on the $i$-th interval. If $\tau,\tau'$ are two lines of a Hadamard matrix, they differ on exactly half their entries, so that $\lVert h_\tau-h_{\tau'}\rVert_{L^1([0,1])}=\frac\alpha2$. There exist arbitrarily large Hadamard matrices and we are done.
Note that if you stay at the level of precision $\varepsilon\simeq \alpha$, then your family is essentially reduced to a point (any two $h\in\mathcal{H}_\alpha$ are indistinguishable at this scale).
In order not to stay on a negative claim, let me suggest that you use instead another definition of ``essentially constant'', through a more refined measures of variations of a function. The problem with your condition is that it does not see any of the geometry of $\mathcal{X}$ (the class $\mathcal{H}_\alpha$ essentially only depend on the cardinal of $\mathcal{X}$), and you cannot expect anything (unless possibly you capture some geometry with the chosen metric on the considered space of functions - $L^1$ would not do, as  the $[0,1]^{n}$ are all isomorphic when endowed with their Lebesgue measures).
There are many natural choices:

*

*H"older functions, when $\mathcal{X}$ is a metric space (the particular case of Lipschitz function is the most common),


*As mentioned by Aryeh Kontorovich, BV functions (this notion is quite simple in dimension $1$, but significantly more intricate in higher dimension), and for a rougher notion $p$-BV (in dimension $1$, one replaces the $\lvert f(x_{i+1})-f(x_i)\rvert$ by $\lvert f(x_{i+1})-f(x_i)\rvert^p$, where $1/p<1$ plays the same role as the H"older exponent,


*smooth ($\mathcal{C}^k$) functions when $\mathcal{X}$ is a domain in $\mathbb{R}^n$ or a manifold, and the many available variations: Sobolev, Besov, etc.
A: Since the OP mentions functions of bounded variation in the title, let's take that literal definition. A function $f:[0,1]\to\mathbb{R}$ is said to have variation $V(f)$ defined by
$$ V(f) = \sup_{0=x_0<x_1<\ldots<x_n=1}\sum_{i=1}^n|f(x_i)-f(x_{i-1})|.
$$
For functions with integrable derivative, $V(f)=\int_0^1|f'(x)|dx$.
Let $F_v$ be the collection of all $f:[0,1]\to\mathbb{R}$ with $V(f)\le v$. It is known (Anthony and Bartlett, Neural Network Learning (1999), Theorem 11.12) that the fat-shattering dimension of $F_v$ at scale $\gamma$ is
$$ 1+\left\lfloor \frac{v}{2\gamma} \right\rfloor.$$
(The notion of fat-shattering -- the one most appropriate for learnability of continuous function classes, is given ibid. in Definition 11.11.)
Of course, covering numbers and fat-shattering are intimately related. For example, Theorem 12.7 ibid. shows how to bound the $L_\infty$ covering numbers in terms of the fat-shattering dimension (read the whole chapter!).
Finally, I take issue with "a function whose output doesn't vary much cannot be a good classifier". Linear classifiers/regressors are as smooth as can be and yet have an excellent track record. Conversely, functions that vary too rapidly are prone to overfitting.
