Cross validation maximizes the availability of training data by splitting data into various combinations and testing each specific combination. Cross validation can be performed through two primary methods. The first method is exhaustive cross validation, which involves finding and testing all possible combinations to divide the original sample into a training set and a test set. The alternative and more common method is non-exhaustive cross validation, known as k-fold validation. The k-fold validation technique involves splitting data into k assigned buckets and reserving one of those buckets
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