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For a given hypothesis space $H$, assuming $f\in H$ where $f$ is the true classifier, you can choose a group $S~D$ where $D$ is a distribution, with a large enough sample complexity, such that the true error over $D$ is in the margin of $\pm \epsilon$ for all hypotheses $h\in H$ with a given probability.

I need to decide if this statement is true or false. On the one hand, if I choose $VCdim(H)=\infty$, than the statement is false since there is no finite number of examples sufficient to satisfy the PAC bound. on the other hand, having $f\in H$ where $f$ is the true classifier means that $H$ is PAC learnable, thus satisfies the statement, means the statement is true.

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