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Cross-validation is a technique that is used for the assessment of how the results of statistical analysis generalize to an independent data set. Cross-validation is largely used in settings where the target is prediction and it is necessary to estimate the accuracy of the performance of a predictive model. The prime reason for the use of cross-validation rather than conventional validation is that there is not enough data available for partitioning them into separate training and test sets (as in conventional validation). This results in a loss of testing and modeling capability.
Cross-validation is also known as rotation estimation.
For a prediction problem, a model is generally provided with a data set of known data, called the training data set, and a set of unknown data against which the model is tested, known as the test data set. The target is to have a data set for testing the model in the training phase and then provide insight on how the specific model adapts to an independent data set. A round of cross-validation comprises the partitioning of data into complementary subsets, then performing analysis on one subset. After this, the analysis is validated on other subsets (testing sets). To reduce variability, many rounds of cross-validation are performed using many different partitions and then an average of the results are taken. Cross-validation is a powerful technique in the estimation of model performance technique.