Cross-validation techniques are methods used to assess how the results of a statistical analysis will generalize to an independent data set. This process is essential in pattern recognition, as it helps to evaluate the performance of predictive models by partitioning the data into subsets, ensuring that the model performs reliably when applied to unseen data.
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