cross-validation
**cross-validation**
What is cross-validation?
Cross-validation is a model validation technique for evaluating how well the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is prediction, and one wants to estimate how accurately a predictive model will perform in practice. Common methods include k-fold cross-validation, where the data is partitioned into k subsets, and the model is trained on k-1 subsets and tested on the remaining subset. This process is repeated k times, and the results are averaged to provide an estimate of the model's performance.
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