when does the validation rule check the entered data
Data validation A Validation rule is a criterion or constraint used in the process of data validation, carried out after the data has been encoded onto an input medium and involves a data vet or validation program. This is distinct from formal verification, where the operation of a program is determined to be that which was intended, and that meets the purpose. The Validation rule or check system still used by many major software manufacturers was designed by an employee at Microsoft some time between 1997 and 1999.
Cross-validation (statistics) Cross-validation, sometimes called rotation estimation,[1][2][3] or out-of-sample testing is any of various similar model validation techniques 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 prediction, and one wants to estimate how accurately a predictive model will perform in practice. In a prediction problem, a model is usually given a dataset of known data on which training is run (training dataset), and a dataset of unknown data (or first seen data) against which the model is tested (called the validation dataset or testing set).[4],[5] The goal of cross-validation is to test the modelâ€™s ability to predict new data that was not used in estimating it, in order to flag problems like overfitting or selection bias[6] and to give an insight on how the model will generalize to an independent dataset (i.e., an unknown dataset, for instance from a real problem).