What is cross-validation in Machine Learning?

The cross-validation method in Machine Learning allows a system to increase the performance of the given Machine Learning algorithm to which you feed multiple sample data from the dataset. This sampling process is done to break the dataset into smaller parts that have the same number of rows, out of which a random part is selected as a test set, and the rest of the parts are kept as train sets. It consists of the following techniques:

  • Holdout method
  • K-fold cross-validation
  • Stratified k-fold cross-validation
  • Leave p-out cross-validation