random splitting

Random splitting is a method of dividing a dataset into two or more subsets randomly. This is often used in machine learning, data analysis, and other data-related tasks.

For example, if you have a dataset of 1000 records, you might randomly split it into two subsets - a training set with 700 records and a testing set with 300 records. You can then use the training set to train a machine learning model, and the testing set to evaluate its performance.

Random splitting is preferred over other methods of splitting, such as systematic splitting or stratified splitting, when you want to ensure that your subsets have a representative sample of the population. This is because random splitting ensures that every record has an equal chance of being included in either subset.