What is cross validation?
Cross-validation is a technique used to assess how the results of statistical analysis are generalized to an independent data set. Cross-validation is primarily used in settings where the goal is prediction and the accuracy of the performance of a predictive model needs to be estimated. The main reason for using cross-validation instead of traditional validation is that there is not enough data available to divide it into separate training and test sets (as with traditional validation). This leads to a loss of testing and modeling skills.
Cross-validation is also known as rotation estimation.
For a prediction problem, a model is generally provided with a 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 goal is to have a data set to test the model in the training phase and then to provide insight into how the specific model adapts to an independent data set.
A round of cross-validation involves partitioning data into complementary subsets and then analyzing a subset. The analysis is then validated for other subsets (test kits). To reduce variability, many rounds of cross-validation are performed using many different partitions and the results are then averaged. Cross-validation is a powerful technique in estimating the model performance technique.