Best Practices

Question 1

cross validation

Answer 1

One round of cross-validation involves partitioning a sample of data into complementary subsets, performing the analysis on the training set, and validating the analysis on the validation set. To reduce variability, in most methods multiple rounds of cross-validation are performed using different partitions, and the validation results are combined (e.g. averaged) over the rounds to give an estimate of the model’s predictive performance.