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Cross-validation can be used on two levels. When you want to evaluate an estimator h h by estimating its risk E[L(h(x), y)] E [L (h (x), y)] based on some loss function L L, you need independent samples to compute the estimator h h and to compute the expectation. Then you can use cross-validation on all the data to get a better estimate of the risk than just using a single data split ...
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