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I am writing to ask if the Combinatorial (Purged) Cross-Validation" method of Marcos Lopez de Prado's "Advances in Financial Machine Learning" book is similar to the idea of bootstrap. If not, what is the key difference? It seems like bootstrap is based on permutation (resampling with replacement) and C(P)CV is based on combinations (resampling without replacement)? At the same time, I suppose the key goal between both methods is to generate as many artificial samples from the same dataset?

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