Cross-Validation is a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. It is mainly used in settings where the goal is a prediction, and one wants to estimate how accurately a predictive model will perform in practice. The idea is to define a dataset to “test” the model in the training phase (i.e., the validation dataset), in order to limit problems like overfitting, give an insight on how the model will generalize to an independent dataset. One round of cross-validation involves a sample of data into complementary subsets, performing the analysis on one subset (called the training set), and validating the analysis on the other subset (called the validation set or testing set). To reduce variability, multiple rounds of cross-validation are performed using different partitions, and the validation results are averaged over the rounds. One of the main reasons for using cross-validation instead of using the conventional is that there is not enough data available to partition it into separate training and test sets without losing significant modeling or testing capability.

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