Cross-validation
Estimating out-of-sample error by rotating parts of the data through the validation role: in k-fold cross-validation, each of k parts is held out once while the model trains on the rest.
Where you will meet it
Cross-validation appears in 2 learning outcomes on the CFA syllabus. A few of them:
- Explain overfitting and recognise ways to deal with it
“Remedies are reducing complexity, for example with penalised regression or pruning, and choosing how far to go by estimating out-of-sample error with k-fold cross-validation, which rotates each of k parts of the data through the validation role.”
- Set out the aims, stages and techniques of training a model
“Performance evaluation uses the right metrics on data held out from training, with cross-validation.”
See also
Practise it
Practise cross-validation until it is automatic: questions that explain every wrong option.
Take the free diagnostic →