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I'm reading the proof of No-Free-Lunch Theorem (quoted at the end of this question) in Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, p.37, the author wrote:

Let $C$ be a subset of $\mathcal{X}$ of size $2m$. Note that there are $T = 2^{2m}$ possible functions from $C$ to $\{0,1\}$. Denote these functions by $f_{1},...,f_{T}$. For each such function, let $D_{i}$ be a distribution over $C×\{0,1\}$ defined by:

$$ D_{i}(\{(x,y)\}) = \begin{cases} 1/|C|\text{ if }y=f_{i}(x)\\ 0 \text{ otherwise} \end{cases} $$

I'm suspecting that $D_{i}$ defined in this way is a valid distribution because even though on the surface we have $|C|(1/|C|)=1$, there's no guarantee that $f_{i}$ will return correct labels for all examples in $C$ and so the cumulative probability may less than $|C|(1/|C|) = 1$?

The Theorem stated in the book as follows:

Let $A$ be any learning algorithm for the task of binary classification with respect to the 0−1 loss over a domain X . Let m be any number smaller than $|X|/2$, representing a training set size. Then, there exists a distribution D over X ×{0,1} such that:

  1. There exists a function $f :X \to\{0,1\}$ with $L_{\mathcal{D}}(f)=0$.
  2. With probability of at least 1/7 over the choice of $S \sim D^{m}$ we have that $L_{\mathcal{D}}(A(S)) ≥ 1/8$.
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