Discussion questions#
Cross validation is seen as a gold standard for estimating the generalizability of an observed effect. What are ways that information can “leak” into the cross validation process and impact the estimate of a model’s risk?
While cross validation is largely used to evaluate test error, a measure of prediction accuracy, how can cross validation be used to ask an inferential question about your data?