Cross-validation is training a model on a subset of a dataset and evaluating the model on a complementary subset of the dataset.

The three steps of cross-validation are:

  • Set aside some part of the sample data set.
  • Using the rest data set to instruct the model.
  • Examine the model using the saved portion of the data set.
BY Best Interview Question ON 30 Dec 2023