It is a technique for increasing the model performance by feeding multiple sample data from the dataset. The sampling process is done by breaking the data into smaller parts that have the same number of rows. Out of all the parts, one is randomly selected for the test and another one for train sets.
It consists of the following techniques :
* Holdout method
* k-fold cross-validation
* Stratified k-fold cross-validation
* Leave p-out cross-validation
